01 Garde THESE - Institut des Géosciences de l ...©lien. Un remerciement particulier à Bruno Monteny pour ses nombreux conseils téléphoniques, ainsi que pour ses données sans

INSTITUT NATIONAL POLYTECHNIQUE DE GRENOBLE

N° attribué par la bibliothèque /_/_/_/_/_/_/_/_/_/_/

T H E S E

pour obtenir le grade de

DOCTEUR DE L'INPG

Spécialité : « Mécanique des Milieux Géophysiques et Environnement »

préparée au sein du Laboratoire d'étude des Transferts en Hydrologie et Environnement

UMR 5564 (CNRS, INPG, IRD, UJF)

dans le cadre de l'Ecole Doctorale « TERRE, UNIVERS, ENVIRONNEMENT »

présentée et soutenue publiquement par

Gaël DERIVE

le 11 Juillet 2003

ESTIMATION DE L'EVAPOTRANSPIRATION EN REGION SAHELIENNE SYNTHESE DES CONNAISSANCES

ET EVALUATION DE MODELISATIONS (SISVAT, RITCHIE) APPLICATION A LA ZONE D'HAPEX-SAHEL (NIGER)

JURY

Mr Philippe BOIS Professeur, INP Grenoble Président Mme Catherine OTTLE DR CNRS, CETP Velizy Rapporteur Mr Pierre RIBSTEIN DR IRD, MSEM Montpellier Rapporteur Mr Joost BROUWER Lecturer, Wageningen University (NL) Examinateur Mr André CHANZY DR INRA, Avignon Examinateur Mme Sylvie GALLE CR IRD, LTHE Grenoble Directeur de thèse

Ce n’est que lorsque le puit s’assèche

que l’on découvre la valeur de l’eau.

REMERCIEMENTS

La route est longue, raide et tortueuse. Et la ligne finale franchie, essoufflé mais heureux, on ne peut que regarder en arrière et remercier les personnes qui nous ont aidées à franchir cette étape. Vous m'avez soutenu, porté et supporté. Les remerciements qui suivent vous sont donc logiquement adressés.

En premier lieu, je tiens à adresser mes plus sincères remerciements à Sylvie Galle qui a dirigé ces travaux de thèse. Merci tout d’abord pour m'avoir accordé ta confiance sur ce sujet. La perpétuelle indépendance dont tu m'as fait bénéficier à permis de m’assumer pleinement et de m'ouvrir l’esprit. L’alternance de moment de remise en cause et de périodes d’encouragement m'a donné la possibilité de gravir l’échelle à mon allure. Merci également pour ton soutien quotidien qui s’est étendu bien au delà des heures réglementaires de bureau. Saches aussi que cette thématique sahélienne, basée sur l'expérience d'HAPEX-Sahel, m’a été extrêmement agréable et m'a énormément intéressé. Ta grande connaissance du milieu sahélien m'a été également d’une aide précieuse. J'espère réellement qu’on aura l'occasion prochaine de collaborer de nouveau ensemble.

J’exprime également de très sincères remerciements aux rapporteurs de cette thèse, Catherine Ottlé et Pierre Ribstein. Merci pour vos critiques avisées et vos perceptions éclairées du domaine qui m’ont permis de prendre du recul sur ces travaux. Merci à Philippe Bois d’avoir admirablement animé cette soutenance, ainsi qu’à André Chanzy et Joost Brouwer d’avoir accepté de participer au jury.

Une pensée particulière revient également aux SVATeurs et autres MARathoniens. Merci

tout d'abord à Isabelle Braud et Hubert Gallée pour vos conseils avisés sur la modélisation de type SVAT qui ont permis d'enrichir les travaux de recherche sur le modèle SISVAT. Merci aussi pour votre précieuse aide et grande expérience dans la rédaction des publications qui ont suivies. Merci également à Christophe, Romain et Wilfram qui ont permis et/ou permettront de développer et améliorer le modèle couplé SISVAT-MAR.

Je tiens également à remercier les personnes qui m’ont donné l'opportunité de sortir du

monde virtuel de la modélisation, et de côtoyer ainsi le monde réel à travers les manipulations de terrain en Afrique. Je ne suis pas prêt d’oublier les semaines passées au Bénin en compagnie de Jean-Michel le roi de l’organisation et fin cuisinier, Stéphane (dit « le Boub ») une des personnes les plus agréables et gentilles du labo, Deva et José compagnons de route, Paolo et Randel les deux enfants du groupe (aux intestins un peu fragiles !). Un bémol néanmoins : l’intimité parfois mise à défaut lors du séjour (sacré Randel, on en rigolera encore longtemps de cette histoire !). Merci aussi à Thierry Lebel pour la participation à la conférence AMMA de Niamey, ce qui m'a permis de découvrir en même temps le milieu

sahélien. Un remerciement particulier à Bruno Monteny pour ses nombreux conseils téléphoniques, ainsi que pour ses données sans lesquelles cette thèse n'aurait pu se réaliser.

Une thèse, c’est aussi trois ans de vie passées au sein du laboratoire. Je n’oublierai donc

pas les multiples rencontres qui ont fait la vie quotidienne durant les jours, mais aussi les nuits de travail. Sachez tous et toutes que ces années se sont toujours déroulées dans d’excellentes conditions et dans une perpétuelle et extraordinaire bonne humeur. Et cela, c’est évidemment grâce à vous tous. Merci donc à Alexis, partenaire de bureau et de galère, initiateur d’escalade et de rugby. Grâce à toi, p'tit scarabé, ces années se sont passées dans une extraordinaire ambiance. Merci à Latif (je tairai l’ensemble de ses surnoms) qui traversent les générations allégrement, avec son éternelle bonne humeur et gentillesse. Les années ne te changeront pas (enfin, si tu ne regardes pas la balance !). Merci à Alex du labo voisin (LEGI) avec qui les midis et les week-ends de labeur se déroulaient toujours dans la bonne humeur. Saches que tu mérites aussi un grand bravo pour ta thèse. Merci à Duc pour ta gentillesse et surtout pour ton calme. Tu es maintenant tranquille dans le bureau pour finir ta thèse. N’oublions surtout pas les anciens du labo (Céline, Helena, Isa, Manue, Stéphanie, Babacar, Christian, Fabien, et Thierry) et les tout juste docteurs (Anne-Julie, Catherine et Wilfram) avec qui j'ai passé de merveilleux moments. Et enfin la nouvelle génération avec Guillaume, Maud et Noémie, Abdou qui parle comme un dieu en anglais, le roi Hubert, et José (à prononcer comme le vino) le skieur fou. Voilà désormais les p'tits nouveaux, Eddy, Laetitia, Marine, Mathieu et Théo, dont quelques-uns ont été des compagnons d’escalade. Sans oublier les deux tenniswomen Christophe et Guillaume. Bonne continuation à tous et toutes, et que ceux que j’aurais oublié me le pardonne (vous verrez avec l’âge !).

Merci également à tout ceux qui ont fait vivre l'ambiance du labo en dehors des seules

heures de travail, ce qui est essentiel. A noter, entre autres, les après-midi chez Michel, les soirées chez Georges-Marie, et les soirées guitares chez Thierry ou Arona. Merci également à Bruno pour les cours de guitare qui finissaient toujours en une joyeuse récréation.

Merci enfin à Albert, Cédric, Jérôme, Sophie et Stéphanie de mon accueillante famille à

l’INRA d'Avignon, à Noémie du LTHE, ainsi qu’à la Porret’s team (Raphaël, Christiane et Jean-François) pour vos conseils avisés de néophyte ou d’expert qui ont contribué à améliorer la soutenance qui restera incontestablement un grand moment de cette aventure.

Je tiens à remercier immensément (et bien plus que ça encore) Laurence qui a suivie et

subie cette belle aventure. Mille mercis pour ton soutien quotidien et tes encouragements permanents. Et si mon niveau d’anglais a atteint ce qu’il est aujourd’hui, c’est évidemment à toi que je le dois. Je suis certain qu’on reparlera encore longtemps de ces « flouxizes ». Je partage donc avec grande joie ce succès avec toi, mais aussi avec mes Parents qui m'ont toujours soutenus et encouragés. Merci à tous et bonne lecture …

TABLE DES MATIERES

3

Table des Matières

INTRODUCTION GENERALE .................................................................................. 9

Contexte de l'Etude....................................................................................................... 11

Problématique ............................................................................................................... 13

Objectifs et Moyens mis en Oeuvre............................................................................. 15

Organisation de la Thèse.............................................................................................. 17

Sites Web ....................................................................................................................... 18

References...................................................................................................................... 19

CHAPITRE 1 : SYNTHESE DES CONNAISSANCES SUR L'ESTIMATION DE L'EVAPOTRANPIRATION EN REGION SAHELIENNE.............................. 21

Résumé du Chapitre ..................................................................................................... 23 Contexte de l'Etude ...................................................................................................... 23 Résultats et Discussions............................................................................................... 24 (a) les observations de terrain .................................................................................. 24 (b) les modélisations ................................................................................................ 25

Abstract.......................................................................................................................... 26

1. Introduction............................................................................................................... 26

2. Description of the HAPEX-Sahel study area ......................................................... 28 2.1 Environmental conditions ...................................................................................... 28 2.2 Small endoreic watersheds..................................................................................... 29 2.3 Three main vegetation types .................................................................................. 30

3. Principle of evaporative flux Measurements ......................................................... 31 3.1 Total evapotranspiration measurement.................................................................. 31 (a) Surface energy Budget ....................................................................................... 31 (b) Surface mass Budget .......................................................................................... 32 3.2 Transpiration measurement.................................................................................... 33

4. Measurement and Monitoring made during HAPEX-Sahel ................................ 33 4.1 Time and space Resolution .................................................................................... 33 4.2 Measurement based on the Energy budget ............................................................ 34

TABLE DES MATIERES

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4.3 Measurement based on Water budget.................................................................... 35 (a) Measurement at parcel scale (plot scale)............................................................ 35 (b) Measurements at watershed scale (some km² ) .................................................. 37 4.4 Transpiration measurements .................................................................................. 37

5. Main results from HAPEX-Sahel measurements .................................................. 38 5.1 Total evapotranspiration ........................................................................................ 38 5.2 Open water evaporation ......................................................................................... 42 5.3 Leaves water interception ..................................................................................... 42 5.4 Soil evaporation .................................................................................................... 42 5.5 Vegetation transpiration......................................................................................... 44

6. Evapotranspiration modeling within the HAPEX-Sahel area.............................. 45 6.1 Determinist models ................................................................................................ 45 6.2 Aerodynamic formulation...................................................................................... 48 6.3 Conceptual models................................................................................................. 49

7. Conclusion ................................................................................................................. 50

References...................................................................................................................... 52

CHAPITRE 2. EVALUATION DU SCHEMA DE SURFACE SISVAT :

REPRESENTATION DE SURFACE SUR UNE ZONE DE JACHERE .............. 61

Résumé du Chapitre ..................................................................................................... 63 Contexte de l'Etude ...................................................................................................... 63 Résultats et Discussions............................................................................................... 63

Abstract ......................................................................................................................... 66

1. Introduction............................................................................................................... 67

2. The Model.................................................................................................................. 68 2.1 General Description ............................................................................................... 68 2.2 Soil and Vegetation Modules................................................................................. 71 2.3 Latent Heat Flux Expression.................................................................................. 72 2.4 Model Set Up ......................................................................................................... 73

3. Model Implementation and Evaluation .................................................................. 74 3.1 The Study Area ...................................................................................................... 74 3.2 Atmospheric Forcing ............................................................................................. 75 3.3 Soil Properties........................................................................................................ 76 3.4 Vegetation Characteristics ..................................................................................... 76 3.5 Water and Energy Budget Measurements ............................................................. 78 3.6 Model's performance criteria ................................................................................. 79

TABLE DES MATIERES

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4. Validation Period's Results ...................................................................................... 79 4.1 Energy Budget Simulations ................................................................................... 79 4.2 Water Budget Simulations ..................................................................................... 85

5. Surface representation Scheme Discussion ............................................................ 87 5.1 SISVAT Simulations ............................................................................................. 87 5.2 Comparison with SiSPAT and ISBA..................................................................... 89 (a) Surface scheme Description ............................................................................... 89 (b) Simulations Comparison .................................................................................... 90

6. Conclusion ................................................................................................................. 91

Appendix: Description of the SISVAT model ........................................................... 93

References...................................................................................................................... 94

CHAPITRE 3. EVALUATION DU SCHEMA DE SURFACE SISVAT :

SENSIBILITE DU MODELE SUR UNE CULTURE DE MIL............................... 97

Résumé du Chapitre ..................................................................................................... 99 Contexte de l'Etude ...................................................................................................... 99 Résultats et Discussions............................................................................................. 100

Abstract........................................................................................................................ 101

1. Introduction............................................................................................................. 102

2. Materials and Methods........................................................................................... 103 2.1 Site Description.................................................................................................... 103 2.2 Experimental Data Description............................................................................ 104 2.3 Parameter set Identification ................................................................................. 105

3. Simulations Results and Discussions..................................................................... 106 3.1 Simulated Surface Water and Energy Balance .................................................... 106 (a) Energy Budget................................................................................................... 110 (b) Water Budget .................................................................................................... 111 3.2 Discussion............................................................................................................ 112

4. Sensitivity Analysis ................................................................................................. 114 4.1 Period n°1: Bare soil area .................................................................................... 115 4.2 Period n°2: Vegetation Growing Season ............................................................. 116 (a) Vegetation parameters Influence...................................................................... 116 (b) Influence of vegetation cover fraction ............................................................. 119 (c) Impact of Vegetation variables Uncertainty..................................................... 119

5. Conclusion ............................................................................................................... 119

TABLE DES MATIERES

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References ................................................................................................................... 121

INFLUENCE DU CHOIX DU JEU DE PARAMETRES SUR LA SIMULATION DES FLUX DE CHALEUR LATENTE AU SEIN DU SCHEMA DE SURFACE SISVAT........................................................................................................................ 125

De la validation au mode opérationnel ..................................................................... 125

L'impact de la paramétrisation au sein du modèle SISVAT .................................. 126 Paramétrisation choisie en mode opérationnel .......................................................... 126 Comparaison des résultats ........................................................................................ 127 De l'échelle micro-météorologique à la méso-échelle .............................................. 129

Conclusion ................................................................................................................... 129

CHAPITRE 4. EVALUATION DU MODELE CONCEPTUEL DE RITCHIE SUR LES TROIS PRINCIPAUX TYPES DE VEGETATION.............................. 131

Résumé du Chapitre ................................................................................................... 133 Contexte de l'Etude .................................................................................................... 133 Résultats et Discussions............................................................................................. 134

Abstract........................................................................................................................ 135

1. Introduction............................................................................................................. 136

2. Study Area and Applied Methodology.................................................................. 137 2.1 Study Area Description........................................................................................ 137 2.2 Model Description ............................................................................................... 138

3. Available Data ......................................................................................................... 140 3.1 Atmospheric Forcing ........................................................................................... 140 3.2 Soil Textural Characteristics................................................................................ 141 3.3 Vegetation Characteristics Monitoring................................................................ 142 3.4 Latent heat Flux Measurements........................................................................... 143

4. Model Sensitivity..................................................................................................... 144 4.1 Model Response to the Parameters...................................................................... 145 4.2 Model Response to Variable Uncertainty............................................................ 146

5. Model evaluation..................................................................................................... 147 5.1 Daily EvapoTranspiration.................................................................................... 147 5.2 Cumulated Evapotranspiration ............................................................................ 150 5.3 Results comparison and discussions .................................................................... 151

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6. Annual Hydrological Cycle Simulation ................................................................ 152 6.1 Annual Evapotranspiration rate ........................................................................... 153 6.2 Partitioning between Soil and Vegetation ........................................................... 155

7. Conclusion ............................................................................................................... 157

Références.................................................................................................................... 159

CONCLUSION GENERALE ET PERSPECTIVES .............................................. 163

Les Observations de Terrain ..................................................................................... 165 Le bilan des observations de Terrain d'HAPEX-Sahel.............................................. 165 Les prochaines expériences de Terrain : l'ORE AMMA-CATCH … ....................... 166

Le schéma de surface SISVAT .................................................................................. 166 D'excellente performances à l'échelle micro-météorologique ................................... 166 Les prochaines étapes pour SISVAT......................................................................... 167 SISVAT, le MAR et la Mousson ouest africaine ….................................................. 168

Le modèle conceptuel de Ritchie ............................................................................... 168 Un modèle simple, mais robuste................................................................................ 168 Les avantages de la simplicité … .............................................................................. 169

Vers une compréhension intégrée des processus ..................................................... 171

Références.................................................................................................................... 171

ANNEXE : A simple model to estimate the evapotranspiration rate using remote sensing. A case study in the Sahel (IAHS publication ).................... 173

TABLE DES MATIERES

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Introduction Générale

INTRODUCTION GENERALE

11

INTRODUCTION GENERALE Contexte de l'Etude

Les sciences de l'environnement terrestre sont confrontées depuis une dizaine d'années à la prise de conscience de l'impact important que pourraient avoir sur les ressources en eau les changements climatiques potentiels résultant des modifications imposées par l'homme à son milieu physique. Cette thématique apparaît de plus en plus comme une préoccupation socio-politico-économique majeure pour le siècle à venir. Les différents compartiments de ce milieu (atmosphère, hydrosphère et biosphère) se caractérisent cependant par de nombreuses interactions qui agissent sur une très large gamme d'échelles. Leur compréhension impose donc de développer une vision globale du complexe atmosphère-biosphère-hydrosphère.

Dans cette vision globale, une attention particulière doit être accordée aux régions tropicales et cela pour deux raisons essentielles. Tout d'abord, ces régions subissent de plein fouet les effets de la variabilité climatique, et ce même dans le contexte du climat réputé stable des cent dernières années. Les phénomènes hydrologiques extrêmes (sécheresses, inondations) ont souvent une ampleur bien supérieure à leur équivalent des zones tempérées. Par ailleurs, le bilan énergétique, largement excédentaire sous les tropiques, conditionne la circulation atmosphérique planétaire, et ses fluctuations ont un impact qui s'étend bien au-delà de la seule ceinture intertropicale. La compréhension du cycle hydrologique dans la ceinture intertropicale est donc un enjeu primordial, tant du point de vue scientifique que de celui du développement de ces régions.

Le contraste entre les ressources hydriques et les besoins humains est particulièrement marqué au sein de la bande sahélienne, en Afrique de l'Ouest. Le Sahel (le rivage en arabe) se définit comme la zone géographique séparant latitudinalement le désert saharien (au Nord) de la zone équatoriale soudanienne (au Sud), en s'étalant longitudinalement du Sénégal (à l'Ouest) à l'Ethiopie (à l'Est). Ce découpage géographique s'appuie avant tout sur des considérations pluviométriques, le cumul annuel des précipitations sahéliennes étant compris entre 200 et 700 mm (du Nord au Sud), et s'opérant sur une unique saison des pluies centrée sur l'été boréal. Ces précipitations sont caractérisées par une forte variabilité temporelle s'observant à l'échelle inter-annuelle, ce qui engendre des déséquilibres notables d'une année sur l'autre. Ceci rend ainsi encore plus incertain les faibles disponibilités en eau dans cette région. Au Niger, le coefficient de variation (écart type rapporté à la moyenne) de la pluviométrie annuelle varie de 0.22 à Niamey (564 mm par an) à 0.47 à Agadez (138 mm par an) sur la période 1950-89 (Le Barbé et Lebel, 1997). A cette variabilité interannuelle, s’ajoute une variabilité à plus long terme entre des décennies humides (1950-1970) puis sèches (1970-1990) (figure 1). Les sécheresses marquées (et successives) des années 70 (1972-74) puis 80 (1983-85) (Nicholson, 1981; Lamb, 1982; Folland et al., 1986) ont rappelé


12

de façon très concrète la forte dépendance des populations locales sahéliennes vis à vis de cette ressource en eau.

-3

-2

-1

0

1

2

3

1920 1930 1940 1950 1960 1970 1980 1990Année

Indi

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luvi

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Figure 1 Evolution de l'indice des pluies normalisé (écart à la moyenne normalisé par l'écart type) sur le Sahel (11-16° Nord, 10° Ouest - 10° Est) de 1921 à 1997 (d'après Le Barbé et al., 2002).

Suite au déficit pluviométrique continu depuis les années 1970, un programme international, nommé HAPEX-Sahel (Hydrological and Atmospheric Pilot EXperiment in the Sahel) (Goutorbe et al., 1997), s'est focalisé en 1992 sur une sous-zone de la bande sahélienne (au Niger) afin de suivre et comprendre le fonctionnement climatique de cette région. Cette expérience constitue la première phase de l’Observatoire de Recherche en Environnement (ORE) CATCH (Couplage de l'Atmosphère Tropicale et du Cycle Hydrologique) qui étudie l'impact des fluctuations pluviométriques sur les ressources en eau, sur une large fenêtre en Afrique de l'ouest (0-5° Est, 6-15° Nord) (figure 2). Cet ORE représente le site africain de l'expérience GEWEX (Global Energy and Water cycle EXperiment) du programme mondial WCRP (World Climate Research Program) de recherche sur le climat. L’ORE CATCH s'insère également dans le projet AMMA (Analyse Multidisciplinaire de la Mousson Africaine) visant à étudier le phénomène de mousson sur l'ensemble de l'Afrique de l'Ouest au cours de la période 2001-2010.

Le site d'expérimentation d'HAPEX-Sahel est localisé sur le degré carré de Niamey (2-3° Est, 13-14° Nord) en République du Niger. A cette latitude, le cumul annuel des précipitations (assimilable à celui de Niamey) est de 564 mm sur la période 1950-1989 (Le Barbe et Lebel, 1997), dont 95 % des précipitations tombent entre les mois de mai et septembre (Lebel et al., 1997). Une Période d'Observation Intensive s'est déroulée en 1992 sur une durée d'environ deux mois, du 15 août au cœur de la saison des pluies, au 9 octobre près d'un mois après le


13

dernier événement pluvieux. Lors de cette expérience, l'accent a particulièrement été mis sur les processus de surface qui semblent être un des facteurs majeurs influant sur le régime des pluies de cette région (mécanisme de rétroaction).

Figure 2 Localisation géographique de l’observatoire CATCH (0-5° Est, 6-15° Nord) et de ses deux sites de suivi intensif : HAPEX-Sahel (2-3° Est, 13-14° Nord) et CATCH-Bénin (1.5-2.5° Est, 9-10° Nord) en Afrique de l'Ouest. Problématique

Dans la zone sahélienne, une attention particulière doit être accordée aux flux évaporatifs au sein des processus de surface, et cela pour deux raisons majeures. Tout d'abord, ces flux interviennent majoritairement dans les bilans d'eau (évapotranspiration) et d'énergie (flux de chaleur latente) au niveau même de la surface. Les observations récentes ont par exemple mis en avant que sur cette zone, à l'échelle annuelle, plus de 75 % de l'eau précipitée s'évapore directement avant même de rejoindre les mares et les nappes profondes, cette limite reste valable à toutes les échelles spatiales (de la parcelle au bassin versant) et pour tous les types de couverture végétale (mil, jachère, brousse tigrée) (Peugeot, 1995; Gash et al., 1997). Par ailleurs, les flux de vapeur d'eau conditionnent directement le taux d'humidité dans les plus basses couches de l'atmosphère, modifiant ainsi le taux de saturation de l'air. Ceci permet alors aux évènements convectifs de se former et d'amener de l'eau précipitée jusqu'à la surface (phénomène de rétroaction) (Taylor et al., 1997ab, De Ridder, 1998). Par conséquent, l'estimation de ces flux demeure une priorité, que ce soit au niveau de la surface pour les besoins hydriques des cultures et les ressources locales en eau, ou au niveau des phénomènes atmosphériques à plus grande échelle.


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Deux principaux facteurs extérieurs peuvent modifier le taux des flux évaporatifs. Ce sont d’une part les modifications climatiques à travers le cumul de la pluviométrie et le changement du régime pluvieux. On comprend évidemment qu'une réduction des précipitations entraîne une diminution de l'évapotranspiration (en absolu), mais il advient nécessaire de quantifier le rapport entre ces deux termes en faisant également intervenir la répartition des pluies au cours de la saison. C'est également une forte pression démographique qui influe directement sur la modification de l'occupation des sols et du couvert végétal, et cela sur l'ensemble du bassin versant. Dans les plaines, les zones naturelles disparaissent et la période de rotation entre les champs de mil et les jachères diminue (Loireau, 1998), tandis que sur les plateaux les zones de forêt dite « brousse tigrée » tendent à disparaître (Peltier et al., 1995). Dans ce sens, la contribution relative de chaque élément de la toposéquence doit donc être quantifiée.

L'estimation des flux évaporatifs demeure cependant difficile à mettre en œuvre au Sahel, et cela compte tenu d'au moins deux fortes spécificités locales. Tout d'abord, des conditions atmosphériques extrêmes apparaissent tout au long de l'année, compte tenu du fort pouvoir évaporant de l'air. D'un côté, l'évapotranspiration potentielle excède la pluviométrie pendant une grande partie de l'année. Au pas de temps annuel, l'évapotranspiration potentielle moyenne (de l'ordre de 2000 mm) équivaut à environ quatre fois les précipitations. D'un autre côté, les évènements convectifs introduisent une forte variabilité dans le temps et l'espace des précipitations qu'ils génèrent. Il est vrai que la moitié des précipitations de l'année (en quantité) tombe en moins de 5 heures, avec des intensités supérieures à 35 mm.h-1 (Lebel et al., 1997). Ensuite, la description de la surface est relativement complexe. Les caractéristiques hydrodynamiques du sol comportent une forte hétérogénéité spatio-temporelle, souvent accentuée par une croûte superficielle naturelle et des facteurs extérieurs perturbateurs (les termitières, par exemple). De plus, la couverture végétale reste toujours très éparse, le sol nu jouant donc un rôle déterminant. Les principaux types de végétation rencontrés sont les cultures de mil et les zones de jachère dans les plaines sableuses, ainsi que la brousse tigrée sur les plateaux latéritiques. Plusieurs strates de plantes se retrouvent au sein d'un même type de végétation, comme c'est le cas pour la jachère composée d'une strate herbacée basse cohabitant avec des arbustes parsemés. La brousse tigrée est quant à elle composée d’une alternance ordonnée de fourrés arbustifs et de sol nu. Par conséquent, la représentation des couverts sahéliens doit prendre en compte explicitement la contribution des zones nues et végétalisées. En plus de la quantification globale de l'évapotranspiration, sa décomposition entre évaporation et transpiration doit être étudiée afin de connaître le rôle du sol et de la canopée dans cet ensemble fortement hétérogène. Cette nécessité est un point central, comme l'ont fortement suggérée Wallace et al. (1993).


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Objectifs et Moyens mis en Oeuvre

L’objectif de cette thèse est de proposer des outils de modélisation permettant de quantifier l’évapotranspiration totale, ainsi que sa décomposition entre l’évaporation et la transpiration, et cela sur les trois principaux types de couvert rencontrés au Sahel (mil, jachère, brousse tigrée) et sur l’ensemble du cycle hydrologique. Ceci permettra ultérieurement de simuler l’impact de la variabilité du régime des pluies ou de l’occupation des sols sur ces taux évaporatifs.

Compte tenu de cet objectif, un travail de synthèse des estimations de la composante évaporative au Sahel est premièrement nécessaire. En effet, aucune étude de ce genre n'a été menée au Sahel depuis les études pionnières effectuées par Monteith (1991) et surtout Wallace (1991). Cependant, d'énormes avancées ont été réalisées depuis, grâce notamment à l'expérience HAPEX-Sahel menée en 1992 et dont les travaux de recherches ont été rapportés dans un numéro spécial du Journal of Hydrology paru en 1997 (numéro 188-189). Les données recueillies par des expérimentations originales ont permis d'augmenter considérablement la compréhension (au sens large) de l'évapotranspiration. Il apparaît alors comme appréciable de faire un bilan des résultats obtenus cette dernière décennie à partir d'HAPEX-Sahel sur l'ensemble des couverts rencontrés, en effectuant une synthèse des études entreprises, des avancées mises en avant, des difficultés rencontrées, ainsi que des perspectives ouvertes. Ce travail apparaît d'autant plus crucial que des mesures de flux vont très prochainement (2004-05) être mises en œuvre sur l'ensemble de l'Afrique de l'Ouest dans le cadre du projet AMMA.

Cette étude bibliographique a montré l’intérêt et la complémentarité de deux types de modélisation au concept totalement différent. Les modèles de type SVAT (Soil-Vegetation-Atmosphere Transfer), à base physique, représentent explicitement les processus de surface. Parmi ces modèles, le schéma de surface SISVAT (Soil-Ice-Snow-Vegetation-Atmosphere Transfer) présente l’avantage d’être couplé avec un modèle atmosphérique de meso-échelle, ce qui est fondamental pour notre objectif final. Les modèles conceptuels ou « paramétrés » sont beaucoup plus simples à mettre en œuvre sur des périodes longues car ils requièrent peu de paramètres et de variables. L’apport respectif de ces deux types de modélisation est discuté en conclusion.

Le modèle atmosphérique de mésoéchelle MAR (Modèle Atmosphérique Régional) (Gallée et Schayes, 1994; Gallée, 1995) permet actuellement de suivre l'évolution spatio-temporelle de la mousson en Afrique de l'Ouest avec une résolution de 40 km. Un schéma de surface nommé SISVAT (De Ridder, 1997; Gallée et al., 2001) est couplé avec ce modèle atmosphérique afin de représenter les conditions de surface. Ce modèle unidimensionnel (vertical) permet en effet de reproduire les processus de surface par le couplage des bilans d'eau et d'énergie à travers le continuum sol-végétation-atmosphère. Même si ce système couplé SISVAT-MAR fonctionne actuellement sur l'ensemble de la région ouest africaine, les


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performances du modèle SISVAT n'ont toujours pas été évaluées spécifiquement sur cette zone d'étude. Par conséquent, il apparaît comme essentiel d'évaluer ce schéma de surface SISVAT sur les principaux types de végétation, en mettant exclusivement l'accent sur les flux de chaleur latente étant donné que (i) ces flux représentent la composante principale au sein des bilans hydrique et énergétique, et (ii) qu’ils alimentent en vapeur d'eau le modèle atmosphérique lors de la réalisation du couplage. Dans cet objectif et compte tenu des données disponibles, une évaluation de ce schéma de surface est entreprise sur deux types de végétation, à savoir :

• sur une zone de jachère, qui représente la végétation prépondérante sur la zone de

l'expérience HAPEX-Sahel (39 % de sa superficie totale). La mise en œuvre du modèle SISVAT sur ce type de végétation représente un test sévère pour évaluer son comportement compte tenu (i) de la complexité de ce type de végétation associant deux strates végétales au comportement hydrique contrasté, et (ii) de la période d’évaluation disponible (jour julien 239-292) comportant des conditions pluviométriques variées, mêlant la fin de la saison des pluies et le début de la saison sèche. De plus, deux autres modèles de type SVAT (ISBA, SiSPAT) ont été appliqués à cette même zone de jachère et sur la même période. Ceci permet une inter-comparaison des performances des modèles utilisés sur ce type de surface.

• sur une culture de mil, qui représente la quasi exclusive culture vivrière sur la bande

sahélienne, et qui couvre 22% de la superficie d’HAPEX-Sahel. Il est bon de signaler qu’aucun autre modèle couplant les bilans d'eau et d'énergie n'a été appliqué sur ce type de végétation mêlant une forte surface foliaire à un faible taux de couverture. La période d'observation disponible (jour julien 202-263) se divise en (i) une période de sol nu sur laquelle le module ‘sol’ du modèle est testé, et (ii) une période de culture durant laquelle le comportement du modèle est testé globalement. Cette dernière période permet également de comparer les résultats à ceux obtenus sur la jachère. C'est aussi l'occasion de faire une étude de la sensibilité du modèle aux variations des paramètres (sol et végétation) compte tenu de leur incertitude respective, ainsi qu’aux variables liées à l'évolution du couvert végétal.

Dans sa configuration opérationnelle, étant donné l’absence de mesures locales, le schéma de surface SISVAT utilise des valeurs tabulées pour ses paramètres de surface, définies en fonction des types de sol (classification USDA _United State Department of Agriculture_) et de végétation (classification IGBP _International Geosphere-Biosphere Program_). L’objectif final en ce qui concerne le schéma de surface SISVAT est donc d’évaluer l’impact de cette simplification sur les résultats évaporatifs. Pour cela les résultats du modèle obtenus en utilisant des paramètres mesurés in situ sur les deux types de végétation (mil, jachère), ont été comparés avec ceux obtenus à partir de typologies pré-établies.


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Compte tenu du manque de données disponibles, le modèle SISVAT n’a pas pu être mis en œuvre sur une année entière, ni sur une zone de brousse tigrée. Par conséquent, une modélisation plus simple semble nécessaire. Cette recherche de simplicité a d'ailleurs été recommandée par de nombreux auteurs, parmi lesquels Wallace et al. (1993). La deuxième approche de modélisation va dans ce sens, en s'appuyant sur le modèle conceptuel de Ritchie (1972). Ce modèle permet l'estimation des flux d'évapotranspiration au pas de temps journalier. En outre, les composantes du sol (évaporation) et de la végétation (transpiration) peuvent être dissociées. Malgré son pas de temps journalier, ce modèle peut être utilisé pour étudier l’impact d’une modification du régime des pluies sur l’évaporation et la transpiration au Sahel. En effet, de récentes études de Le Barbé et al. (2002) ont montré qu’au Sahel la différence entre les périodes sèches et humides se caractérise principalement par une diminution du nombre d’évènements pluvieux au cours de la saison, sans modification des caractéristiques des averses (intensité, cumul). La sécheresse se traduit concrètement par de plus longues durées inter-événement, typiquement de 2.8 jours en période sèche et de 1.8 jours en période humide. Ce modèle journalier peut donc paraître suffisant dans un premier temps pour tester l’impact des variations climatiques. Ce modèle, peu gourmand en paramètres (seulement 3) et en variables d'entrée (seulement 4), a pu être évalué sur les trois principaux types de couverts sahélien (culture de mil, jachère et brousse tigrée). Ses performances sont ensuite évaluées sur une période longue, typiquement une année hydrologique. Cette étude permet directement de tester la contribution de chacun des types de végétation au bilan hydrologique ainsi que leur réaction évaporative au changement climatique, et devrait permettre de simuler par la suite l’impact de la modification de l'occupation des sols. Organisation de la Thèse

Ce mémoire de doctorat se divise en quatre chapitres, reprenant point par point les objectifs précédemment cités. Chacun de ces chapitres correspond à un article scientifique (rédigé en anglais) introduit par une synthèse d'environ deux pages (rédigée en français). Les divers chapitres se répartissent tel que décrit ci-après :

• Le premier chapitre est consacré à un état de l'art relatif à l'estimation des flux évaporatifs au Sahel (chapitre 1). Après avoir brièvement effectué la description de l’expérience d’HAPEX-Sahel (sites d’études, types de couverts végétaux, et bassins versants) et des principales méthodes d'estimation des flux évaporatifs (mesures in situ, modélisations), les observations et modélisations menées lors de l'expérience HAPEX-Sahel sont exposées à divers pas de temps (minute à l'année) et d'espace (parcelle à bassin versant), et ceci pour les trois principaux types de végétation (mil, jachère, brousse tigrée). Les résultats associés, les difficultés et les limites rencontrées sont exposés et critiqués.


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• L'évaluation du schéma de surface SISVAT (chapitre 2) est réalisée sur la jachère qui représente la principale couverture végétale sur la zone HAPEX-Sahel. Les résultats de la simulation des flux de chaleur latente, sont analysés puis comparés avec les mesures in situ, ainsi qu'avec ceux obtenus par deux autres SVATs (ISBA et SiSPAT) sur la même zone de jachère durant la même période. Le rôle de la représentation de surface, qui joue un rôle majeur, est alors discuté.

• Le schéma de surface SISVAT est ensuite testé sur une culture de mil (chapitre 3).

Compte tenu de l'importance du sol nu sur ce type de couvert, le module ‘sol’ a pu être testé et évalué séparément. Le comportement du modèle est ensuite comparé à celui observé sur la jachère sur une période commune. C'est également l'occasion de tester la sensibilité du modèle aux variations des paramètres (sol et végétation) et à l'incertitude des variables liées à l'évolution du couvert végétal. C’est finalement la description et la quantification de l’impact du choix du jeu de paramètre, entre les paramètres in situ utilisé en mode de validation, et les paramètres tabulés automatiquement définis à partir de cartes des types de sols (USDA) et de végétation (IGBP) en mode opérationnel sur la zone africaine.

• Une méthodologie résolument simple est construite autour du modèle conceptuel de

Ricthie (chapitre 4). La simplicité de l'approche permet de tester ce modèle sur les trois principaux types de végétation, à savoir une culture de mil, une zone de jachère et un système de brousse tigrée. Les performances de ce modèle journalier sont discutées sur une courte période de validation, avant d'entreprendre des simulations sur une année complète (année hydrologique). Les résultats sont comparés aux données existantes (mesures de terrain, résultats provenant d’autres modèles).

• Pour finir, une conclusion générale et des perspectives clôturent ces travaux de

recherche. Sites Web

AMMA : http://medias.obs-mip.fr/amma CATCH : http://www.lthe.hmg.inpg.fr/catch GEWEX : http://www.gewex.org HAPEX : http://www.ird.fr/hapex WCRP : http://www.wmo.ch/web/wcrp


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Références De Ridder, K., 1997. Land surface processes and the potential for regional climate change in

semiarid regions. Thèse de doctorat, Université de Louvain (Belgique), 120 pp. De Ridder, K., 1998. The impact of vegetation cover on sahelian drought persistence.

Boundary-Layer Meteorology, 88, 307-321. Folland, C.K., T.N. Palmer, et D.E. Parker, 1986. Sahel rainfall and worldwide sea

temperature 1901-1985. Nature, 320, 602-607. Gallée, H., et G. Schayes, 1994. Development of a three-dimensional meso-gamma primitive

equations model, katabatic winds simulation in the area of Terra Nova Bay, Antarctica. Monthly Weather Revue, 122, 671-685.

Gallée, H., 1995. Simulation of the mesocyclonic activity in the Ross Sea, Antarctica Monthly Weather Revue, 123, 2051-2069.

Gallée, H., G. Guyomarc'h, et E. Brun, 2001. Impact of snow drift on the Antarctic ice sheet surface mass balance : possible sensitivity to snow-surface properties. Boundary-Layer Meteorology, 99, 1-19.

Gash, J.H.C., P. Kabat, B.A. Monteny, M. Amadou, P. Bessemoulin, H. Billing, E.M. Blyth, H.A.R. DeBruin, J.A. Elbers, T. Friborg, G. Harrison, C.J. Holwill, C.R. Lloyd, J.P Lhomme, J.B. Moncrieff, D. Puech, H. Soegaard, J.D. Taupin, A. Tuzet, et A. Verhoef, 1997. The variability of evaporation during the HAPEX-Sahel Intensive Observation Period. Journal of Hydrology, 188-189, 385-399.

Goutorbe, J.P., T. Lebel, A.J. Dolman, J.H.C. Gash, P. Kabat, Y.H. Kerr, B. Monteny, S.D. Prince, J.N.M. Stricker, A. Tinga, et J.S. Wallace, 1997. An overview of HAPEX-Sahel : a study in climate and desertification. Journal of Hydrology, 188-189, 4-17.

Lamb, P.J., 1982. Persistence of subsaharan drought. Nature, 299, 198-212. Le Barbe, L., et T. Lebel, 1997. Rainfall climatology of the HAPEX-Sahel region during the

years 1950-1990. Journal of Hydrology, 188-189, 43-73. Le Barbe L., T. Lebel, et D. Tapsoba, 2002. Rainfall variability in West Africa during the

years 1950-1990. Journal of Climate, 15 (2), 187-202. Lebel, T., J.D. Taupin, et N. Amato, 1997. Rainfall monitoring during HAPEX-Sahel. 1.

General rainfall conditions and climatology. Journal of Hydrology, 188-189, 74-96. Nicholson, S.E., 1981. Rainfall and atmospheric circulation during drought periods and wetter

years in West Africa. Monthly Weather Revue, 109, 2191-2208. Loireau, M., 1998. Espace, ressources, usages : Spatialisation des interactions dynamiques

entre les systèmes sociaux et les systèmes écologiques au Sahel nigériens. Thèse de doctorat, Université de Montpellier III (France).

Monteith, J.L, 1991. Weather and water in the Sudano-Sahelian zone. Dans : Soil Water balance in the Sudano-Sahelian zone (Sivakumar M.V.K., Wallace J.S., Renard C., Giroux C. Eds), Proceedings du workshop de Niamey (Février 1991), IAHS publication, 199, 11-29.

Peltier, R., A. Bertrand, E.M Lawali, G. Madon, et P. Montagne, 1995. Marchés ruraux de bois-énergie au Sahel. Bois et forêts des tropiques, 245, 75-89.


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Peugeot, C., 1995. Influence de l'encroûtement superficiel du sol sur le fonctionnement hydrologique d'un versant sahélien (Niger)-Expérimentations in-situ et modélisation. Thèse de doctorat, Université de Grenoble, LTHE, (France), 305 pp.

Ritchie, J.T., 1972. Model for Predicting Evaporation from a Row Crop with Incomplete Cover. Water Resources Research, 8 (5), 1204-1213.

Taylor, C.M., F. Said, et T. Lebel, 1997a. Interactions between the land surface and mesoscale rainfall variability during HAPEX-Sahel. Monthly Weather Revue, 125, 2211-2227.

Taylor, C.M., R.J. Harding, A.J. Thorpe, et P. Bessemoulin, 1997b. A mesoscale simulation of land surface heterogeneity from HAPEX-Sahel. Journal of Hydrology, 188-189, 1040-1066.

Wallace, J.S., 1991. The measurement and modeling of evaporation from semiarid land. Dans : Soil Water balance in the Sudano-Sahelian zone (Sivakumar M.V.K., Wallace J.S., Renard C., Giroux C. Eds), Proceedings du workshop de Niamey (Février 1991), publication IAHS, 199, 131-148.

Wallace, J.S., C.R. Lloyd, et M.V.K. Sivakumar, 1993. Measurements of soil, plant and total evaporation from millet in Niger. Agricultural and Forest Meteorology, 63, 149-169.

Premier Chapitre

Synthèse des Connaissances sur l'Estimation de l'EvapoTranspiration

en région Sahélienne

SYNTHESE DES CONNAISSANCES

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CHAPITRE 1 SYNTHESE DES CONNAISSANCES SUR L'ESTIMATION DE L'EVAPOTRANSPIRATION EN REGION SAHELIENNE Résumé du Chapitre

Contexte de l'Etude

En région sahélienne, l'estimation des composants évaporatifs demeure primordiale, que ce soit pour la connaissance du bilan hydrique au niveau de la surface, ou pour le phénomène de rétroaction que ces flux entraînent sur le régime des pluies, via l'atmosphère. Les derniers travaux majeurs, visant à regrouper les méthodes et résultats concernant l'estimation de l'évapotranspiration en milieu sahélien, ont été produits au début des années 90. Il s'agit des études de Monteith (1991) et Wallace (1991). Depuis la publication de ces deux études pionnières, l'expérience HAPEX-Sahel a permis de fortement enrichir les connaissances sur cette thématique, grâce aux nombreuses études menées sur le degré carré de Niamey (2-3° Est, 13-14° Nord) en 1992. Malgré les multiples avancées produites, aucune étude de synthèse n'a été centrée sur ces flux évaporatifs, hormis celle de Gash et al. (1997) qui a consisté à observer la variabilité des flux de chaleur latente mesurés à l'échelle micro-météorologique (quelques hectares).

Dans ce contexte général, il semble bon aujourd'hui, plus de dix ans après les observations de terrain et six ans après le bilan rendu de l'expérience HAPEX-Sahel, de produire un état de l'art sur l'estimation des flux évaporatifs sur la zone sahélienne, en s'appuyant sur cette expérience. L'objectif principal reste le suivi journalier de l'évapotranspiration, et cela à une échelle représentative et homogène pour la végétation (typiquement quelques hectares), comme à celle du bassin versant. Cette échelle spatio-temporelle est cruciale aussi bien pour la compréhension directe du phénomène évaporatif, que pour les modélisations associées qui cherchent à quantifier l'impact des potentiels changements climatiques et l'influence de la modification de la couverture végétale. Ce chapitre va dans ce sens, en répertoriant et comparant les principaux résultats produits durant l'expérience HAPEX-Sahel, afin de mettre en exergue les avancées produites, et de souligner les points sensibles. Ce travail apparaît aujourd'hui comme essentiel compte tenu du lancement du programme AMMA (Analyse Multidisciplinaire de la Mousson Africaine) en Afrique de l'Ouest dont la période d'observation à long terme a commencé en 2001 et se poursuivra jusqu'en 2010, avec un suivi plus intense comprenant des mesures continues de flux (flux de chaleur latente compris) au cours des années 2004-05.

CHAPITRE 1

24

La synthèse des connaissances sur l’evapotranspiration s’articule autour de trois points qui apparaissent comme primordiaux pour atteindre une telle estimation :

• la description des trois principaux types de végétation : les cultures de mil et les zones de jachères dans les plaines sableuses, ainsi que la brousse tigrée sur les plateaux latéritiques,

• le suivi de l'ensemble des flux évaporatifs : l'évapotranspiration (totale), mais aussi sa répartition interne entre l'évaporation du sol et la transpiration des plantes,

• les différents outils : les mesures de terrain basées sur les bilans de masse ou d’énergie, ainsi que les modélisations numériques.

Cet objectif est cependant contraint par les échelles spatio-temporelles des mesures in situ.

Dans ce chapitre, on rappelle tout d’abord les caractéristiques de la zone HAPEX-Sahel, ainsi que les diverses méthodes de mesure disponibles et leurs limites. Ensuite, les moyens d'observation mis en œuvre pour suivre les flux évaporatifs au cours de l'expérience HAPEX-Sahel sont décrits, juste avant d'effectuer la synthèse des résultats acquis durant cette expérience, et ceci pour l'évapotranspiration mais aussi pour chacune de ses composantes (évaporation, transpiration), de l'échelle de la parcelle à celle du bassin versant, d'un pas de temps journalier à l'année entière. Finalement, les divers types de modélisation sont recensés, décrits puis critiqués.

Résultats et Discussions

La synthèse produite se focalise sur les résultats obtenus à partir de l’expérience HAPEX-Sahel. Les résultats obtenus sont discutés pour chacun des deux types d'estimations disponibles, à savoir :

(a) les observations de terrain

Les mesures in situ ont largement contribué à la connaissance de l'évapotranspiration, et cela à diverses échelles représentatives. A l'échelle ponctuelle, les mesures souvent indirectes sont basées sur le suivi de l'humidité du sol, l'estimation du ruissellement ou l'emploi de lysimètres. A l'échelle micro-météorologique (typiquement quelques hectares), les mesures directes sont basées sur des approches énergétiques (rapport de Bowen, méthode des fluctuations d'Eddy corrélation). A l'échelle annuelle, plus de 75 % des précipitations est repris par évapotranspiration, et ceci quelle que soit la couverture végétale (mil, jachère, ou brousse tigrée). A l'échelle hydrologique du bassin versant, l'estimation reste basée sur des mesures indirectes de suivi de mares et de nappes. Ce taux est cependant plus élevé sur les bassins de plateaux que sur ceux des plaines compte tenu de leur faible superficie, de leur


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faible pente et du sol plus argileux qui les composent. Pour l'ensemble des échelles décrites, le rôle du sol nu est majeur à l'échelle annuelle et devra donc être soigneusement pris en compte dans les modélisations. La transpiration devient cependant majoritaire quelques jours après la pluie et également au cours de la saison sèche. Dans la perspective du projet AMMA qui prévoit la réalisation de mesures de flux sur un transect latitudinal (du Mali au Bénin) en 2004-05, il serait souhaitable de pouvoir quantifier le terme évaporatif de chaque type de végétation sur une année complète. La décomposition interne de l'évapotranspiration (évaporation du sol, transpiration des plantes) a été peu observée lors d'HAPEX-Sahel, ce qui limite les conditions expérimentales sur le rôle respectif des plantes et du sol en région sahélienne.

(b) les modélisations

Les simulations numériques ont principalement été appliquées sur les zones de jachère durant la période d'observation intense d'environ 2 mois située au cœur de la saison des pluies. Les divers modèles complexes tels que le modèle bi-couches de Shuttleworth et Wallace (1985) ou les modèles couplant les bilans d’eau et d’énergie (ISBA, SiSPAT) permettent de reproduire correctement les flux de chaleur latente (r2 > 0.80) que ce soit à des pas de temps très fins (20 minutes) ou à l'échelle de la journée. L'erreur relative sur le cumul durant la période de validation reste toujours inférieure à 12 %. Cependant, ces modèles requièrent un nombre important de paramètres (de l'ordre d'une dizaine) pour caractériser la surface (sol, plante), ainsi qu'un nombre conséquent de variables (de l'ordre d'une demi-douzaine) pour suivre l'évolution de la couverture végétale. Cela limite considérablement leur utilisation, que ce soit temporellement sur l'année ou sur les divers types de végétation disponibles.

Par la suite, le choix de modélisation s’est porté sur le schéma de surface SISVAT en

raison de son couplage avec le modèle atmosphérique de méso-échelle MAR. Ce SVAT doit être validé sur le Sahel avant de lancer les simulations MAR. Ce modèle couplé permettra ensuite de poursuivre les efforts sur la compréhension des fluctuations spatio-temporelles des flux de chaleur latente en région sahélienne. Des modélisations simplifiées, comme celle basée sur le modèle de Ritchie (1972), devraient également permettre de remédier à certains inconvénients majeurs, comme le manque flagrant de données qui entraîne une limitation des modélisations entreprises à la fois dans le temps et l'espace.

CHAPITRE 1

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REVIEW OF THE EVAPOTRANSPIRATION (MEASUREMENTS AND MODELING)

IN THE SAHEL

Authors: G. Derive*, S. Galle

Laboratoire d'Etude des Tranferts en Hydrologie et Environnement

LTHE (CNRS UMR 5564), BP 53, 38041 Grenoble Cedex 9, France

To be submitted to the Journal of Arid Environments

* Corresponding author. Fax: (+33)-4-76-82-52-86.

E-mail address: [email protected] (G. Derive)

Abstract

The objective of this paper is to review the current state of the evapotranspiration estimation in the Sahel (ground observations, models). This paper focuses on the results of the most documented studied area in the sahelian zone: HAPEX-Sahel (Niger). In this climatic zone, the annual evapotranspiration represents the main component of the surface water budget, being significantly lower on both the millet field and fallow savannah (75-89 % of rainfall) than on tiger bush (95-100 %). The most accurate estimations were made by using the SVAT models, but simple models are also studied to make long term simulations. These simulations are essential to assess the impact of both the possible climate and land cover changes. Keywords: EvapoTranspiration, Latent heat flux, Ground measurements, Modeling, HAPEX-Sahel, Niger. 1. Introduction

The surface water balance plays a major role in the semiarid regions, not only for the global hydrological cycle at the climatic scale, but also for the local water resources for human living needs such as the agriculture needs or the available water in groundwater. Such problem is drastic in the Sahel (between the arid Sahara desert in the North and the humid soudanian zone in the South) in West Africa. This latter geographic zone is characterized by a relatively low annual rainfall (200-700 mm), which takes place during one short rainy season. About 90 % of the annual rain falls during three months (July, August and September). The recent droughts, added to the strong famines of the 70's (1972-74) and the 80's (1983-85),


27

highlighted the strong water dependence of the local population (Le Barbé et al., 2002). Both the understanding of the spatio-temporal variations and the quantification of the surface water budget become therefore necessary to anticipate these dramatic situations.

In the sahelian zone, the atmospheric evaporative demand exceeds the water supply most of the year given the high air temperature (Sivakumar, 1987; Monteny, 1993). It is therefore not surprising to observe that the EvapoTranspiration (ET) represents the major component of the surface water budget at all space and time scales, for all the vegetation types (Peugeot, 1995; Desconnets et al., 1997; Gash et al., 1997; Rockstrom 1997; Ehrmann, 1999). The understanding of the ET (quantity, fluctuations) appears then as essential. Nevertheless, its estimation is generally more complex in these semiarid areas than in the temperate regions, as reported by Wallace (1991). In the sahelian region, strong rainy events take place because of convective storms, which leads to a significant spatial and temporal variability at event and seasonal scales (Lebel et al., 1997). The frequent appearance of crusts strongly modifies the soil runoff capacity (Casenave & Valentin, 1991). Moreover, most of the time, the vegetation types include a juxtaposition of bare soil areas and mixed vegetation strata (Monteny, 1993).

This review takes place after the studies of Monteith (1991) and Wallace (1991), who both summed up the methods and the results found under the soudano-sahelian conditions. Many studies were led during the last ten years. The most significant achievements were made thanks to the HAPEX-Sahel (Hydrological Atmospheric Pilot Experiment in the Sahel) (Goutorbe et al., 1997a). The experimental area is located in the square degree of Niamey (2-3° East, 13-14° North) in South Niger. An Intensive Observation Period (IOP) took place during a 2-months period in 1992 (from August 15th to October 9th), during the core of the rainy season. This experiment represents the most documented studied zone in the Sahel. We will therefore focus on this area.

The objective of this paper is to make a review of the various studies made to assess the ET variations in order to specify the role of the vegetation, the soil and the climate in this sahelian zone. These methods include (i) the in situ measurements and their associated instrumentations, and (ii) the running models and their limits. The discussions are made for the three main vegetation types: millet and fallow savannah in the plains, and the natural tiger bush in the plateaus. In this perspective, three focus points are taken into consideration: (i) the global amount and spatio-temporal variability of the total ET, (ii) the ET internal repartition between the soil evaporation and the plants transpiration to understand their respective roles, and (iii) the related spatial and temporal scales required for these estimations.

CHAPITRE 1

28

2. Description of the HAPEX-Sahel study area

2.1 Environmental Conditions

The HAPEX-Sahel area is the square degree located between 2° and 3° East and from 13° and 14° North, in the Republic of Niger (West Africa) (figure 1-a). Three studied sub-sites are respectively the South SuperSite (SSS), the Western (WCSS) and the Eastern (ECSS) Central SuperSite (figure 1-b).

Figure 1 (a) Geographical location of the HAPEX-Sahel area (2-3° East, 13-14° North) in West Africa. (b) The three studied sub-sites are the South SuperSite (SSS), the Western (WCSS) and the Eastern (ECSS) Central SuperSite.

In the sahelian region, the precipitation are irregular along the year. Two months of sparse rains are followed by three months of monsoon regime when 90% of the annual rain falls. On a 12-years period (1990-2001), Balme et al. (2003) found that the monsoon period is quite steady beginning June 21th (± 6 days) and ending October 4th (± 7 days). The seven other months are totally dry. Figure 2 shows the time repartition of the rainfall at the Banizoumbou station in the ECSS in 1992. In Niamey, the average rainfall is of 564 mm from 1950 to 1989 (Le Barbé & Lebel., 1997). The natural annual south-north rainfall gradient is about 1 mm.km-1. However, there can be a significant spatial rainfall heterogeneity at local scale, as rainfall is mainly due to convective storms. For instance, a rainfall gradient of 28 mm.km-1 was observed for the annual rain between two neighbor raingauges (10 km apart). The rainfall correlation length is 20-40 km calculated at event scale during three years (1989-91) in the HAPEX-Sahel area (Taupin et al., 1993). The median rain rate is high (35 mm.h-1) and 1/3 of the rain falls with an intensity greater than 50 mm.h-1. Mathon (2001) showed that 12 % of the organized convective systems provides 80 % of the ground rainfall in this climatic zone. Moreover, the inter-annual rainfall variability, which differentiates the wet and the dry periods, is explained by the number of rainy events while the cumulated quantity of each

2°E 3°E

ECSSWCSS

SSS

Niamey

Niger

14°N

13°N

10°N

0°N-20°W 10°E

HAPEXSahel

(a) (b)


29

rainy event does not change (Le Barbé & Lebel, 2002). As the duration of the effective rainy season is quite stable (105 ± 13 days) (Balme et al., 2003), the time period between two successive rainy events is much longer during a dry year.

In the HAPEX-Sahel area, the daily Potential EvapoTranspiration (ETP) ranges from 2 to 8 mm (figure 2). ETP is minimum during the rainy season, and lower on rainy days, but significantly higher the day just after the rainy days. The annual ETP reaches about 2000 mm, which corresponds to four times the annual rainfall (Monteny, 1993; Wallace et al., 1993). This high evaporative demand is typical of the sahelian areas.

Figure 2 Time evolution of the Potential ET and the rainfall at the Banizoumbou station, Eastern Central SuperSite, in 1992.

2.2 Small endoreic Watersheds

The HAPEX-Sahel area is characterized by a gentle relief where the elevation ranges from 200 to 260 m above the sea level. The landscape is split between plains (72 % of the total superficy) and plateaus (28 %) (D'Herbes & Valentin, 1997). The soil type is deep reddish brown sandy soil (88 % sand, 8 % silt, 4 % clay) in the plain area (2-8 % slope), and shallow sandy clay loam soil (41 % sand, 39 % clay, 20 % silt) in the flat plateaus (0-2 % slope) (Nagumo, 1993). The frequent appearance of crusts is observed at the soil surface, because of the great intensity of the storms. For instance, Casenave & Valentin (1992) demonstrated that the runoff coefficient might be better predicted using the soil surface conditions rather than the soil type description.

0

2

4

6

8

10

j f m a m j j a s o n d

Months of Year (1992)

ETP

(mm

/day

)

0

10

20

30

40

50

Rai

nfal

l (m

m/d

ay)

CHAPITRE 1

30

In this sahelian zone, the typical hydrological unit consists in a small endoreic hydrologic system (0-10 km²), where the water supplies a central pool. After the rainy event, the water redistribution takes place in two step. First, the pool is filled up thanks to the surface runoff generated on the hillslopes. Intermediate spreading areas may appear in the hillslope. Secondly, the central pool gets empty because of both the deep infiltration, and the evaporation. Note that the major part of the groundwater recharge is due to the deep drainage under such ponds in the sahelian area (Leduc et al., 2001). Three watershed types can be identified (Desconnets et al., 1997): (i) the small plateau watershed characterized by a low slope and a loamy soil type, (ii) the small valley bottom pool in sandy beds or old valleys, and (iii) the skin pool watershed characterized by a very rapid drying out.

2.3 Three main vegetation types

In the HAPEX-Sahel area, three main vegetation types were identified: the millet fields

(22 % of the landscape in 1992) and the fallow savannah areas (39 %) located in the sandy valleys and hillslope, while a natural forest called tiger bush (28 %) covers the lateritic plateaus (D'Herbes & Valentin, 1997). The remaining area consists in degraded hillslope (11 % of the landscape).

The millet crop is cultivated in pockets of 6-12 plants (2-3 meters high) spaced by a

distance of about 1 meter. The traditional local specie (Pennisetum glaucum) is sowed after the first strong rainy event (near 20 mm) and grows in 120 days (Sivakumar, 1989; Rockstrom 1997). Fallow savannah area is composed of two vegetation strata: a sparse annual grass layer (20 cm high) and scattered bushes (Guiera senegalensis) (2-4 m high). The shrubs density is nearly 1.2 trees per 10 m² (Seghieri & Simier, 2002). The bare soil area covers about 15-30 % of the total superficy (Monteny, 1993; Kabbat et al., 1997). Nude old termite mounds regularly appear at the soil surface. The tiger bush system is an alternance of crusted bare soil bands and natural forest bands (Ambouta, 1997). Tree species are mainly Combretum micranthum and Guiera senegalensis (73 % of the forested zone) (Ehrmann, 1999). The "forest band / bare soil band" ratio depends on the mean annual rainfall as observed by D'Herbes & Valentin (1999). For instance, this ratio ranges from about 1/2 (rainfall of 350 mm) to 2 (rainfall of 750 mm) in a representative sahelian zone. This global spatial organization enables the forest band to be over-supplied in water thanks to the runoff coming from the upper bare soil zone (Galle et al., 2001). The deep drainage under the forest zone enables a low groundwater recharge.


31

3. Principle of Evaporative flux measurements

3.1 Total Evapotranspiration measurement

EvapoTranspiration (ET) includes all the phase change processes from liquid to water vapor, from the surface to the atmosphere. Its rate depends on both the atmospheric demand (downward radiation, windspeed, air humidity) and the surface characteristics (soil and vegetation types, soil water content). The measurement of the ET is based either on the heat conservation law through the surface energy balance (eqn 1), or on the mass conservation law through the surface water balance (eqn 5). The principle is to determinate separately each term of the equation in order to infer the ET rate. It is then necessary to be aware of the cumulative errors due to the estimation of each measured variable. (a) Surface Energy Budget

The first relation is based on the surface energy budget where fluxes are expressed in Watt.m-2. The latent heat flux (LE) from the surface (soil, vegetation) to the atmosphere is deduced from the measurement of four vertical heat fluxes, as follows:

)sHG(RnLE ++−= (1) Where L is the latent heat of water vaporization (2.5 106 J.kg-1), E the evaporative water height (mm), Rn the incoming net radiation, G the ground heat flux, H the sensible heat flux, and s the photosynthesis flux. Most of the studies simply ignore photosynthesis (s) (less than 1 %).

Two techniques based on the direct measurement of the turbulent transfer fluxes are known as the Bowen ratio and Eddy correlation methods. These methods are mainly used to determinate the latent heat fluxes. The Bowen ratio method (Bowen, 1926) is based on the diffusion measurements. The water vapor, heat and momentum fluxes are proportional to their respective gradients of humidity, temperature and wind speed. The Bowen ratio (β) is derived from the measurements of the vertical gradient of the air temperature dT (°C) and the air water vapor pressure de (kPa) (eqn 2). Note that both the windspeed and the aerodynamic properties measurements are not necessary (Heilman et al., 1989). LE is then directly deduced from the available energy measurements (eqn 3).

dedT

LEH

×γ==β (2)

thus, ( )( )β+

−=

1GRnLE (3)

CHAPITRE 1

32

Where γ is the psychometric constant (0.66 mb.°C-1). In that case, the representative uncertainty equals nearly 10 %, as highlighted by Sinclair et al. (1975). The other micrometeorological method, called Eddy correlation, only uses the vertical fluctuations of the water vapor content and windspeed, as follows:

a

vLEρρ= (4)

Where v is the vertical component of the air speed (m.s-1), ρ the variation of the absolute humidity of the air (mm water mm-3 air volume) in comparison with the mean value ρa, the mass density of water in the air. The overbar represents the product average along the sampling period. (b) Surface Mass Budget

The surface water balance is expressed in liquid water quantity height (mm) by time unit (from hour to year) using the following expression:

)IRoff()RonP(ET +−+= (5) with DSI +∆= (6)

Where ET is EvapoTranspiration, P precipitation, Ron runon, Roff runoff, I infiltration, ∆S change in soil water storage in the root zone layer, and D deep drainage below this layer and resulting in the groundwater recharge. The underlying hypothesis is that no horizontal water movement below the soil surface is taken into account.

One of the approaches to estimate ET is to measure the variables of the surface water balance. The rainfall measurements are made using an automatic recording rain-gauge, while the drainage is followed using piezographs. A specific application consists in the lysimeter technique. Practically, a small isolated soil volume (some dm3) is monitored between two successive rainy events. The weight variation is explained by the loss of water due to both the drainage and the ET. The smallest lysimeter diameters lead to very low disturbances within the global spatial configuration and enable to gain time and great efforts, especially to get a deep drainage. Moreover, the mini-lysimeters or micro-lysimeters enable to reduce the evaporation variability. Another method is based on the soil water profile monitoring. In that case, no rainfall and runoff measurements are required. Indeed, the knowledge of soil moisture content change (∆S) enables to deduce the ET variation, if the deep drainage is taken into consideration. The soil moisture can be measured by non-destructive techniques within the soil profile (neutron probe, TDR).


33

The selection of the method to use depends on the required spatial scale. The soil moisture profile measurement or the lysimeter technique are efficient at punctual or parcel scale, while the global monitoring is required for watershed scale studies. In the later case, no complete in situ measurement is possible. Therefore, the annual ET is deduced from both the rainfall and the water table variation. The rainfall spatialisation is necessary as the rainfall is significantly variable, which is not the case of the sedimentary aquifer which has a low spatial variation.

3.2 Transpiration measurement

The methods based on the surface mass or energy budget estimate the total ET, but are not able to identify the respective soil and vegetation fractions. Nevertheless, the transpiration rate is required to determinate the vegetation role within the surface processes. The vegetation transpiration can obviously be deduced from the total ET if the soil evaporation is known, but direct measurements must be privileged as well. The most commonly used direct method is based on the sap flow measurement using the thermometric method. The main associated techniques are (i) the Heat-Pulse Velocities (HPV), which measures the temperature variation from an initial heat pulse within a trunk, and (ii) the Thermal Heat Balance (THB), which consists in monitoring the heat flux of a ruban warming the trunk (Valancogne & Granier, 1991; Swanson, 1994). The transpiration of each tree is then the product of the sap flow density by the efficient basal area. A high time step is accessible, from some minutes to half an hour. Another technique based on tracers concentration monitoring enables as well to estimate the plants transpiration rate. 4. Measurements and Monitoring made during HAPEX-Sahel

4.1 Time and Space resolution

The two approaches (water and energy budgets) were made on each vegetation type with various temporal and spatial resolutions during the HAPEX-Sahel experiment. The techniques based on the energy budget can provide a high time resolution (hour or less) thanks to data logger, which enables to precisely deduce the soil (conductivity, moisture) and the vegetation (stomatal reaction) state. In practice, the implementation of such methods lasted a limited time (2 months). The methods based on the water budget have a lower spatial extent (punctual to some m²) than those based on the energy balance (field of some hectares), but have a longer observation period including a complete hydrological year. No direct in situ measurements can estimate the ET at watershed scale. Nevertheless, indirect measurements can provide precious information (runoff measurements, pool survey).

CHAPITRE 1

34

4.2 Measurement based on the Energy budget ( some hectares )

Both the Bowen ratio and the Eddy correlation methods were used during the HAPEX-Sahel experiment (Gash et al., 1997). The latent heat flux measurements were made using automatic micrometeorological stations located at 10 m high at the maximum, depending on the vegetation height. Thus, they are representative of a limited area called Wind Aligned Blob (WAB) of some hectares, which mainly depends on both the wind characteristics (speed and direction) and the sensor height. During the HAPEX-Sahel, the Bowen ratio method was applied to millet crop (Monteny, 1993), to fallow savannah (height is 0.2 and 1.2 m above the grass layer, 4.5 and 9 m above the savannah area) (Monteny et al., 1997) and to the bare soil band of the tiger bush (between 5-20 cm high) (Wallace & Holwill, 1997). The net radiation is deduced from both the global and the reflected radiation (short and long) measurements. The ground heat flux measurement is required at the surface level. In practice, this flux is measured some centimeters beneath the surface because of the instrumentation requirements (surface radiation exposure, moisture movement disturbances very close to the surface). Passerat de Silans et al. (1997) measured the ground heat flux (G) at the surface (2 mm) and (25 mm) on a fallow savannah area several days after a rainy event (Day +2, +6, +11). At a 20-min time step, the maximum relative flux difference never exceeds 25 %. This relative difference however infers a small error of the LE, as much before midday as during the night. At daily scale, the measured LE is unaffected by the determination of the soil heat flux.

Massman (1992) considered that the soil Bowen ratio (βs) can be linked to a soil evaporative resistance depending on the soil moisture conditions (eqn 7). In that case, the soil evaporation is then directly linked to the soil moisture content, which is easily measurable.

)/(cws ∆γ×=β (7) Where γ is the psychometric constant, ∆ the slope of saturation vapor pressure curve at the soil temperature, and cw the soil Bowen ratio coefficient. During the HAPEX-Sahel, Jacobs et al. (1997) found a soil evaporation rate in accordance with the one locally obtained when making the lysimeter measurements on the fallow savannah area. In that study, the cw coefficient equaled one if the top soil layer (5 cm) remained relatively wet (superior to 0.1 m3.m-3), but exponentially increased as soil moisture decreased.

The Eddy correlation method follows the quick evolution of the fluctuations (Wallace, 1991). The Hydra system, that is used and that was developed by Shuttleworth et al. (1988) has an hourly relative error of less than 10 %, as estimated in Niger between two instruments at 4.5m high and 5m from one another. It was implemented on the millet field in Niger (Wallace et al., 1993) and on 9 other sites during the IOP period. Indeed, the Eddy correlation technique was the most commonly used method during the HAPEX-Sahel experiment (all


35

sites except three) (Gash et al., 1997). Lloyd et al. (1997) compared the hourly LE measured on fallow savannah on three local sub-areas (15 m of distance) within each SuperSite (East, West, South). In this study, the sensors are positioned at 5-9 m high. The single regression within a SuperSite highlights a strong correlation (r² > 0.88). The hourly latent heat fluxes have a 20 % variation range. The difference that is noticed is probably due to the local surface heterogeneity. They also suggested a multiple regression, which enables to eliminate some bias, and therefore to reduce the relative errors.

The Eddy correlation technique was also implemented using an aircraft (Saïd et al., 1997). The measurements altitude was around 50 meters above the mean relief, i.e., at the lowest part of the boundary layer ranging from 600 to 2300 m near midday throughout the studied period. Under these conditions, the measured turbulent fluxes are not significantly different from the surface fluxes. Six flights were made near midday (10-13h) during the rainy season (DOY 245, 248, 254, 256) and at the beginning of the dry season (DOY 269, 277). The airborne daily evaporative fractions are close to the ground micrometeorological measurements (some hectares) described by Gash et al. (1997). The final LE relative accuracy is about 11-13 % under wet conditions, but reaches 22-24 % under dry conditions. LE is highly heterogeneous at the beginning of the dry season. Therefore, the minimum length for a relative error of 20 % is 10 and 30 km, respectively under wet and dry conditions. These results are limited to few flights per day during few days.

4.3 Measurement based on Water Budget

(a) Measurements at Parcel scale ( plot scale ) The components of the water balance were monitored in the SuperSites of the HAPEX-

Sahel area for various applications. The measurements that were made on the Western Central Super Site (WCSS) are more precisely described hereunder.

The runoff is determinated by delimiting a spatial area where the downslope border is physically linked to a tank collector. Peugeot et al. (1995, 1997) monitored 3 runoff plots (20�5 m2) located on millet, fallow savannah and bare soil of the tiger bush during 2 years (1992, 1993). Ehrmann (1999) installed 3 additional plots on characteristic areas of the tiger bush in 1994. The runoff rate depends on the surface conditions (slope, soil crusting, vegetation) (Casenave et Valentin, 1992), the soil moisture state, and the rainfall characteristics (amount, intensity, duration) which appear as the most influent factor. Peugeot (1995) suggested a simple piecewise relation to explain the runoff event: no runoff occurs below a rain threshold, while a linear relation links the rainfall to the runoff over this latter rainy limit. Both the rainfall threshold and the relation slope depend on the vegetation type (table 1). These relations were obtained under various soil moisture conditions (from 6 to 14 %). The soil moisture has then a second role (only on very wet or very dry soils). The runoff relative error is of about 2 % at punctual scale (Peugeot, 1995).

CHAPITRE 1

36

Table 1 Parameters of the piecewise linear relation between rain and runoff: rainfall threshold (mm) under which no runoff appears and runoff efficiency over this threshold (slope). (after Peugeot, 1995)

Rainfall limit (mm)

Slope Number of sample

Coefficient r²

Millet 8 0.26 16 0.81 Fallow savannah 7 0.38 35 0.97

Bare soil of the tiger bush 5 0.77 25 0.92

One of the most commonly used methods to estimate the infiltration in that sahelian zone

is based on the soil water profile monitoring (Cuenca et al., 1997). The soil moisture profiles were measured using the neutron probe access tubes on millet and fallow savannah (down to 3.4 m) (Peugeot, 1995) and the tiger bush (down to 5.6 m) (Galle et al., 1999) in the HAPEX-Sahel area. The maximum absolute uncertainty is about 0.01 cm3.cm-3 for a punctual soil moisture value, and about 15 mm for the soil water storage (down to 5.6 m). The soil moisture was monitored during 4 years with a rain dependant time step. A first measurement is made as soon as possible after each rain (day D), while the following measurements are performed D+1, D+2, D+4, D+7, then once a week during the rainy season, and once a month during the dry season. This represents about 40 measurements per year. The daily water stock then needs to be extrapolated between the measured points. The soil water storage difference was used to deduce the annual ET (1992-93) on the three vegetation types by Peugeot (1995). These types of measurements provide punctual information, most of the time extrapolated to field scale thanks to profile repetitions.

A simple micro-lysimeter (1-3 kg) provides accurate evaporation measurements on sandy soil in Niger (Daamen et al., 1993). The absolute error is estimated to 0.1 mm of the mean daily evaporative rate. The main two error sources are the water root extraction from the lysimeter and the drainage at the base of the lysimeter. Nevertheless, no lysimeter technique can be performed on a high (bushes) or dense (tiger bush forest band) vegetation layer.

A few studies based on the tracers evolution were implemented in the sahelian zone. Taupin (1996) combined deuterium and 18O concentrations to directly separate the evaporation and the infiltration into two pools (Bazanga on plateau, Wankama in plain) during two years (1991-92). The evaporative fraction estimated using this methodology in both the Bazanga (75 % of the rainfall) and the Wankama (35 %) pool was slightly lower than the local ETP measurements (76-81 % and 42-44 % respectively).


37

(b) Measurements at Watershed scale ( some km² ) Two main difficulties appear at watershed scale, given the endoreic characteristic of the

sahelian watershed. Firstly, the mid slope spreading areas lead to deep infiltration, which is not quantified. Secondly, the time variations of the pool level enable to estimate the global watershed runoff, but the distinction between the evaporative term and the infiltration is then required. This can be performed using isotopic measurements as suggested by Taupin (1996) or Desconnets et al. (1997). An additional knowledge of the rainfall spatial variation is needed when estimating the ET at watershed scale. Indeed, a strong rainfall spatial variability characterizes the HAPEX-Sahel zone at seasonal and event time scale (Lebel & Le Barbé, 1997). Esteves & Lenoir (1996) measured the rainfall difference between five rain-gauges spaced out by less than 3 km at the Samadey watershed (6.1 km²). Although close, these rain-gauges recorded a high rainfall difference reaching 40 % during the strongest rainy events, and 9 % (38 mm) at seasonal scale for a mean annual rainfall of 405 mm. Higher differences were observed for the global network during the 12-years monitoring. For instance, a gradient of 28 mm.km-1 was observed between two rain-gauges spaced out by only about ten kilometers in 1992 (Le Barbe & Lebel, 1997).

4.4 Transpiration Measurements

An indirect method to estimate the transpiration was tested in the HAPEX-Sahel by Monteny et al. (1997). They inferred the bush transpiration from a latent heat flux difference by using two close instrumentations: the first one on a fallow savannah area, and the second one on a grass layer without bush. The direct measurements based on the sap flow survey were made as well. This technique is applied to only one trunk, and thus needs a representative sampling at both inter-plant and intra-plant scale. The latter condition is particularly important in case of the multi-trunk Guiera Senegalensis bush. The maximum final error for the total flux, with a correct sampling case at parcel scale, is 15 % (Valancogne & Granier, 1991). The implementation of the HPV for low stem diameters (except conifers) is difficult, while the THB technique can be applied to very small stems (as small as 30 mm), such as millet plants or bush trees (about 50 mm in diameter). Nevertheless, the THB sensor requires a more complex instrumentation and greater energy.

The transpiration rate, estimated on the basis of the HPV method, was compared in a satisfying way to the transpiration calculated by the vapor pressure deficit on an experimental neem windbreak (70-170 mm in diameter) in north Niger (Brenner et al., 1991). Tuzet et al. (1997) monitored the sap flow during few days using the THB on five representative bushes (in term of exposure and repartition), where the stem diameters ranged from 45 to 55 mm. The measured transpiration is in accordance with the one estimated by simultaneous measurements (Eddy correlation) made on fallow savannah and grassland. The THB method was also applied to the two main tree species in the tiger bush forest (Ehrmann, 1999), and on

CHAPITRE 1

38

the savannah shrub where a similar uncertainty, i.e. greater than 10 %, is observed by the daily sap flow measurements (Allen & Grime, 1995).

The monitoring of the tracers concentration enables to determinate the transpiration rate within the tree stem. The tracers are typically infrared gas analyzers, deuterium oxide or radioactive isotopic injections (Wallace, 1991). Brunel et al. (1997) used the signatures of stable isotopes (2H, 18O) on Guiera Senegalensis bush during a short period (18-30 July 1992). The measurement uncertainty leads to a 2 % error of the final evaporative ratio.

During the HAPEX-Sahel, the transpiration rate was only measured during both a limited time period (only few days) and spatial area (only few specific trunks), which appears as not sufficient (i) to follow and understand the vegetation role, and then (ii) to validate the transpiration model in a satisfying way. 5. Main results from HAPEX-Sahel Measurements

The total ET is composed of four water vapor fluxes: soil evaporation (Es), open water evaporation on soil (Esdir), vegetation transpiration (Ev), and water intercepted by plants and then freely evaporated (Evdir). The measured ET is the addition of these variables at punctual scale (eqn 8). The vegetation is always sparse and heterogeneous in the sahelian zone. The vegetation cover (fCover) never exceeds 56 % in the millet field and 72 % in the fallow savannah area (Monteny, 1993). Such cover fraction is much smaller in the case of a tiger bush organization, where only 25 % of the total superficy is covered by forests (Galle et al., 1997). The bare soil areas need therefore to be taken into account from field (some square meters) to watershed (some hectares) scale (eqn 9).

)EvdirEv()EsdirEs(ET +++= (8) ETs)fCover1(ETvfCoverET ×−+×= (9)

In the latter equation, ETs and ETv are the ET contribution respectively of the bare soil and the vegetated areas. The surface representation is then described using a mosaic composed of elementary vegetated and bare soil areas. Such mosaic can be more complex in case of fallow savannah where the vegetation is composed of mixed strata (bush and grass). The results obtained for the total ET and its internal components are successively described and compared on the three main vegetation types.

5.1 Total EvapoTranspiration

The evaporative fraction (LE / (Rn-G)) was measured on various sites on each of the three vegetation types, during the Intensive Observation Period in 1992. The LE measurements (using Bowen or Eddy correlation methods) were made at a high time step (20-


39

min, hour), and at some hectares spatial resolution, which is a representative scale to characterize the vegetation behaviour. The measurements took place during a two-month period including both the end of the rainy season (Days Of Year 230-260) and the beginning of the following dry season (Days of Year 260-280). The comparisons of these in situ measurements on each vegetation type between each SuperSite were made by Gash et al. (1997) (figure 3).

Figure 3 Time evolution of the evaporative fraction for (a) millet, (b) savannah, and (c) tiger bush sites. The measurements were made during the 2-months Intensive Observation Period (IOP) in 1992, at the Southern (SSS), West (WCS) and East (ECS) Central SuperSite, and Danguey Gourou (DG) within WCS. The techniques that were used are based on the Bowen or the Eddy correlation methods. (after Gash et al., 1997)

During the rainy season, the daily evaporative fraction ranges from 0.5 to 0.8 on millet and from 0.6 to 0.8 on the tiger bush, while it is never inferior to 0.7 on the fallow savannah area. A small evaporative difference appears between the various sub-sites for each vegetation type when the rainy season is in place. According to Gash et al. (1997), such small evaporative difference is essentially due to the rainfall spatial variability. This is particularly

CHAPITRE 1

40

true at the beginning of the rainy season when the rainy events are spaced and the soil is drier (Saïd et al., 1997).

After the last rainy event of the season, the daily fraction immediately decreases for millet

and tiger bush (DOY 260), but only around ten days later for fallow savannah (DOY 270). Both the tiger bush and the millet crop have a strong proportion of bare soil area where the evaporation rate rapidly decreases after the last rainy event. For the tiger bush, this rapid decrease is also due to a strong response of the woodland canopy conductance to the vapor pressure deficit. On the opposite, the shallow-rooted understorey plays the major role with regards to the evaporative diminution in fallow savannah. Indeed, its evaporative rate progressively decreases when the top soil layer becomes sufficiently dry (Gash et al., 1991; Kabat et al., 1997), while bushes continue to transpirate at a constant rate. Finally, the evaporative fraction reaches 0.4-0.5 for the three vegetation types at the end of the observed dry period (about one month after the last significant rainy event).

The ET was cumulated during the whole studied period using identical instrumentation on the three southern sub-sites. The cumulated ET found is similar on both fallow savannah and the tiger bush area, but millet evaporates nearly 22 % less (Blyth, 1997; Gash et al., 1997). This latter result demonstrates that the millet plant has a stronger surface resistance, which was locally observed. According to that study, fallow savannah has a significantly higher evaporative rate than the millet field during the core of the rainy season.

No direct measurement was made during a complete year. An extrapolation method is then

required. The annual ET was estimated by Peugeot (1995, 1997) during a two years period (1992-93) using an indirect method based on the runoff and the soil moisture monitoring at parcel scale. The deep drainage is assumed to be negligible on both millet and fallow savannah. Indeed, no infiltration was observed below 3.4 m using neutron probe monitoring (Peugeot, 1995, 1997). This latter point appears however as a controversial issue (Leduc et al., 2001; Bromley et al., 2002; Favreau et al., 2002). Only the forest band of the tiger bush has a deep drainage, which equals 0-5 % of the annual rainfall at the tiger bush scale (Culf et al., 1993; Bromley et al., 1997-b). If the deep drainage is known, the runoff measurement becomes sufficient to indirectly estimate the ET. The annual ET is schematically represented in figure 4 for the three vegetation types. The estimated annual ET equals 87-89 % of the rainfall on the millet field and 75-79 % on the fallow savannah area. The peasants weeding, which limits the soil crusting, may explain the higher infiltration rate of millet and then the higher exfiltration rate. However, this annual trend may be local, given the contradictory results of Gash et al. (1997) who observed a higher evaporative rate on fallow savannah than on the millet crop during the end of the rainy season. Indeed, during the rest of the year, fallow savannah may evaporate more than the millet field (i) at the beginning of the rainy season when fallow savannah activity begins while the millet fields are just sowed, and (ii) during the dry season when the transpiration, which represents the higher evapotranspiration part, is greater on fallow savannah than on the millet areas. Therefore,


41

fallow savannah area should evaporate more than the millet field, but local characteristics may invert this trend. Peugeot (1997) plots were located on upward sloping areas, which reinforces the runoff, and then limits the infiltration and the evapotranspiration. Therefore, the peasants weeding limits the influence of the slope in the millet field.

For the tiger bush, various methods converge to estimate that the annual ET almost

reaches the rainfall quantity (95-100 %) (Culf et al., 1993; Bromley et al., 1997-a; Ehrmann, 1999). This underlines the outstanding water harvesting efficiency of this particular vegetation pattern. As no runoff takes place at the whole tiger bush scale, the deep drainage under the forest band is the only output variable. The annual deep drainage rate is however very small. For instance, Bromley et al. (1997-b) estimated a mean annual value of 13 mm in a forest zone using the isotopic technique.

Figure 4 Schematic representation of the annual water budget of millet, fallow savannah and tiger bush, highlighted during the HAPEX-Sahel experiment at parcel scale. Note that percentages are order of magnitude depending on the annual rainfall. (after Peugeot, 1995; Bromley et al., 1997-b).

At watershed scale, the estimations are only made at annual scale. For instance, Esteves & Lenoir (1996) estimated the evapotranspiration of the whole Samadey watershed (6.1 km²) covered by millet (32 % of the watershed superficy), fallow savannah (32 %), and spreading areas (11%) in valley zones, while the tiger bush covers the plateau areas (25 %). In that case, the annual ET was deduced from the pool monitoring and ranges from 69 % (rainfall of 520 mm), 82 % (440 mm) to 90 % (570 mm), mainly depending on the rainfall event distribution along the three observed years (1992-94). Desconnets et al. (1997) stressed a higher annual ET in the plateau pool than in the valley pool. Indeed, they measured a lower mean annual deep infiltration in the plateau pools (10-20 mm) than in the valley or the skin pools (10-80 mm). This difference is mainly due to the watershed characteristics (low superficy, low slope, more loamy soil type).

Millet Runoff (11-13 %)

ET ( 87-89 %)

Fallow savannah Runoff

(21-25 %)

ET ( 75-79 %)

Tiger bush

Groundwater (0-5 %)

ET ( 95-100 %)

CHAPITRE 1

42

5.2 Open water Evaporation

The appearance of flooding at the soil surface depends on both the rainy event (intensity, duration) and the surface characteristics (soil type, roughness, crust appearance). The flooding occurs during a limited time (some hours after a rainy event). The potential evaporation just after the rainy events is limited by the cloud cover. Moreover, the evaporative difference between an open water situation and a wet soil situation remains very limited. Thus, the open water evaporation is negligible in most of the studies using daily time step.

The open water evaporation represents however a significant component in specific surfaces, such as pools. For instance, Desconnets et al. (1997) indirectly estimated the pool evaporation using limnimetric monitoring and isotopic measurements on various typical watersheds near Niamey. The open water evaporation at the Bazanga pool scale represented 20 % of its annual water budget in 1993. Such loss mainly occurs during the dry season, such as in 1992 (70 % of total evaporation). At watershed scale, the open evaporation of the pool represented up to 6 % of the annual rainfall on the plateau watershed (Bazanga, 1991), but was lower than 1 % on the two watersheds located in the plains (Wankama 1991-93, Samadey 1992-93).

5.3 Leaves water Interception

The quantity of water intercepted by the leaves of the plants, and directly evaporated, mainly depends on both the vegetation (plant height, leaves area and inclination) and the rainy event (duration, intensity) characteristics. No measurement was made in the sahelian zone. This evaporative fraction is only described in aerodynamic approaches through the wet canopy fraction. Most of the time, its part is however estimated as negligible given the limited vegetation cover and the small leaf area index.

5.4 Soil Evaporation

The soil evaporation is mainly governed by the soil hydraulic properties depending on both the textural and the structural characteristics (Haverkamp et al., 1999), and the atmospheric demand. The soil textural parameters vary in time very slowly, but the structure of the soil, i.e. the grain arrangement, strongly varies at short spatial and temporal scales. For instance, the peasants weeding in the millet fields or the termite holes in the fallow savannah areas have a strong impact on the soil hydrodynamic properties.

The soil evaporation can be conceptually divided into two distinct phases after a rainy event (Phillip, 1957; Ritchie, 1972):


43

• First, just after the rainy event, the soil evaporation is limited by the atmospheric demand. The high evaporation rate is due to the easily available near-surface soil moisture. This first phase lasts approximately one day for both the millet field (Wallace et al., 1993) and the bare soil of the tiger bush (Wallace & Holwill, 1997). The soil evaporation during the first phase, although that phase is very short, may represent 70-80 % of the annual soil evaporation of the bare soil area of the tiger bush organization (Wallace & Holwill, 1997).

• The second phase is mainly limited by both the soil moisture content and the soil

ability to retain the water. During that phase, the daily soil evaporation rate is very low. For instance, three days after the rainy event, the daily soil evaporation reaches a rate inferior to 0.5 mm in the millet field (Wallace et al., 1993). Despite the different soil types, Wallace & Holwill, (1997) observed that the soil evaporation is similar in the sandy valleys and the plateaus during the second phase.

When the vegetation cover grows, the evaporation of the soil under the vegetated layer is

determinated by estimating the radiative attenuation of the semi-transparent vegetation. The fraction of the available energy reaching the soil surface is expressed through an exponential Beer's law ( )LAIkexp( ×− ) depending on the vegetation characteristics through the Leaf Area Index (LAI) and an extinction parameter (k). A k-parameter of 0.4 is generally used for all the vegetation types as suggested by Ritchie (1972). Wallace et al. (1990) measured a 0.41 value by making simultaneous radiation measurements above and below a pearl millet crop in Niger. This latter is the only measured value in a sahelian environment.

In the vegetated areas, the soil component represents about 50 % of the total daily ET just

after the rainfall, but only 20 % two-days after, as measured on the tiger bush (Wallace & Holwill, 1997) or the millet field (Soegaard & Boegh, 1995) at the maximum growing season. In some cases, the soil evaporation drops to 5 % of the daily ET as measured on millet three days after a rainy event by Wallace et al. (1993). During the crop cycle, Amadou et al. (1996) calculated that 40 % of the total ET is due to the bare soil areas between the millet pockets.

The time evolution of the soil evaporation is however modulated by two main external factors. Firstly, the soil crust appearance reinforces the runoff effect. For instance, the hydraulic conductivity of the thin crust layer is 2-6 times lower than the underlying soil layer in the bare soil bands of the tiger bush (Vandervaere et al., 1996). Le Fèvre et al. (1996) quantified the crust effect on a sandy bare soil in Niger. The crusted soil evaporates significantly less than a weeded soil, but only the first day after the rainy event, i.e., during the first and most important phase (about 1 mm less that specific day). In the millet fields, the peasants weeding reduces the crust appearance, which increases the infiltration (Lamachere, 1991) and so forth the available water for exfiltration. The soil crust re-appears after a cumulated rainfall of about 80 mm (Peugeot, 1995). Soil crusting explains as well the low annual ET of the bare soil of the tiger bush (42-50 % of the rainfall) as measured (directly or

CHAPITRE 1

44

not) by various authors (Peugeot, 1995; Wallace & Holwill, 1997; Ehrmann, 1999). Secondly, the soil fauna activity (essentially termites) strengthens the soil macro-porosity, which significantly increases the infiltration capacity (Leonard & Rajot, 2001). This activity takes place on all the types of vegetation, even if fallow savannah is particularly affected.

5.5 Vegetation Transpiration

The plants transpiration takes place through the vascular system and the stomatal leaf surface. The transpiration mainly depends on the types of plants and their intrinsic characteristics (root system, leaves type, plant resistances) but also on the available water for the plants, according to the focusing period.

In the millet crop, the transpiration can be linearly linked to the ETP value, as observed in the in situ measurements of Wallace et al. (1993). In that study, the daily transpiration reached the ETP value when the LAI equaled approximately two. A few days after a rainy event, the transpiration rate is superior to the soil evaporation, as the soil surface becomes drier. For instance, Soegaard & Boegh (1995) measured a millet transpiration fraction always greater than 50 % of the ET the days following the rainy event. Such fraction reaches 80 % of the ET a few days after the rainy event. At the maximum growing period, the transpiration rate can represent up to 95 % of the total daily ET a few days after the rainy event.

In fallow savannah, the bush transpiration equals about 15-35 % of the total ET. This proportion was observed by many authors. For instance, in the core of the rainy season, Tuzet et al. (1997) found a constant and low shrub contribution (about 50 W.m-2 at midday) by making sap flow measurements, in comparison with the grassland contribution (150-350 W.m-2 at midday) by making micro-meteorological measurements. Monteny et al. (1997) found that the daily evaporative fraction varies around 0.8 for fallow savannah, with a low bushes contribution (0.15). They measured these ratios in the core of the rainy season using two Bowen ratio instrumentations. The shrub transpiration depends on both the climatic demand and the soil water availability in its root layer, which is deeper than the grass layer root system located in the upper 60 cm of the soil. The bush transpiration was also determinated using the isotopic technique (21 % of ET) during a short period (18-30 July) (Brunel et al., 1997). Allen & Grime (1995) measured a higher transpiration (35 % of the ET) by making sap flow measurements in 1990 (31 May - 4 October). The relative contribution of the bushes within the global transpiration varies along the year, as the annual herbaceous layer activity begins later.

The forest band of the tiger bush is water supplied by both the rainfall and the runoff which is produced on the upward bare soil area. For instance, Galle et al. (1999) locally measured in the core of the forest band a maximum infiltration amount of eight times the rainfall at event scale. Moreover, a strong advection occurs from bare soil zone to the adjacent


45

forest, which increases the atmospheric evaporative demand (ETP). For instance, Ehrmann (1999) demonstrated that the daily ET can reach 140 % of the daily ETP in the forest band. Therefore, a strong transpiration rate characterizes such forest zone. The annual transpiration of the forest band equals about 2 times the rainfall (Ehrmann, 1999). At plateau scale, the transpiration represents about 50-70 % of the ET (Culf et al., 1993; Ehrmann, 1999).

The plants transpiration also depends on the water available within the root layer. A water

stress can appear within the plant system because of the water shortage. The available soil moisture within the root zone limits the sahelian vegetation growth. Seghieri & Galle (1999) measured the leaf water potential down to -4 MPa on a green tiger bush tree. This shows that the shrubs have regularly to face severe droughts.

All the types of measurements converge towards the same mean values, but local

differences may appear. For instance, the slope may increase the runoff and therefore decrease the annual evapotranspiration. The environmental conditions (slope, fertilization, crusts) must be taken into account to accurately describe the evaporation processes and to compare the vegetation behaviour. No measurements were however really concomitant, which is why the models are needed to go further in the understanding of the plants behaviour. 6. Evapotranspiration modeling within the HAPEX-Sahel area

In the latter paragraph, we showed that the ground measurements were limited both spatially (some specific vegetated zones, areas of some hectares) and temporally (two months) during the HAPEX-Sahel experiment. Moreover, some vegetation types (as fallow savannah) were largely studied compared to other vegetation covers (millet, tiger bush). In this context, the modeling is needed (i) to extrapolate the vegetation behaviour during the whole hydrological cycle, and (ii) to make a comparison between the contributions of the three main vegetation types during the seasonal cycle.

The models (determinist, aerodynamic formulation, conceptual) were mainly applied to the fallow savannah areas of the HAPEX-Sahel experiment. The estimations were made from 20-min to 1-day time step, during short validation periods. One of the main difficulties is linked to the surface heterogeneity, which requires a specific and rigorous surface representation. Few models were extrapolated to the whole watershed scale, using landscape description.

6.1 Determinist models

Two classical models are used in most of the studies: (i) the Penman-Monteith equation (PM) (Monteith, 1965) and its combination suggested by Shuttleworth & Wallace (1985), and (ii) the Priestley & Taylor (1972) formulation. In all cases, the real ET values are inferior to

CHAPITRE 1

46

the Potential ET value (ETP) defined by Penman (1948), which is equal to the atmospheric evaporative demand. The ETP value is divided into both the equilibrium ET0 representing the radiation power and the aerodynamic ETa representing the aerodynamic power, respectively 80 % and 20 % of the ETP in this sahelian zone.

The Penman-Monteith (PM) (1965) equation enables to estimate the real ET by limiting the Penman's ETP using both the canopy surface (rs) and the aerodynamic (ra) resistances (s.m-1) (eqn 10). This two-layer model enables to determinate the daily latent heat flux either on the bare soil or on the homogeneous vegetation cover (single big leaf approach).

( )( )as

ap

rr1r/DcGRnLE

+×γ+∆ρ+−×∆

= (10)

With ∆ the slope of the saturated vapor pressure versus the temperature curve (mb.°C-1), γ the psychometric constant (mb.°C-1), Rn and G the evaporative equivalent water respectively for the net radiation and the ground heat flux (mm.day-1), ρ the air density, cp the specific heat of the air at a constant pressure, D the pressure deficit between the saturated (qsat) and the standard (q) air pressure (kPa). The resistances estimation represents the main difficulty, especially under the sahelian dry conditions. Shuttleworth & Wallace (SW) (1985) developed a linear combination of the PM equation in order to adapt that model on the sparse canopies, and separated both the soil and the vegetation term, as follows:

PMvCvPMsCsLE ×+×= (11) Where PMs and PMv are the Penman-Monteith equations (eqn 10) respectively for the bare soil and the closed canopy, Cs and Cv are the coefficients depending on the aerodynamic and the bulk stomatal resistances respectively for the bare soil and the crop. The resistances formulation is described in the study made by Shuttleworth & Gurney (1990). Amadou et al. (1996) applied the SW model to a millet field during a 46-days period during the HAPEX-Sahel. The hourly ET is correctly simulated at the various vegetation stages. The determination coefficient (r²) varies from 0.65 for the low LAI (inferior to 1.5 m2.m-2) to 0.88 for the higher values (superior to 1.5). The average measured daily value (2.4 mm) is accurately estimated (r² = 0.60), with a mean overestimation of 0.3 mm for a RMSE of 0.8 mm.

Wallace et al. (1990) estimated the plants transpiration using both the PM and the SW equations in a millet field near Niamey. In that case, the stomatal conductance and the LAI data are required. The PM model tends to underestimate the transpiration under dry conditions, and to overestimate it under wet conditions, in comparison to the SW. Moreover, the SW provides better estimation than the PM for low LAI values (inferior to 1.2). The SW


47

approach was used to calculate the transpiration under various millet crop conditions for a few days in 1985-87 (Wallace et al., 1993). The Total ET was the sum of both the simulated transpiration and the soil evaporation, that were measured using the lysimeter technique. This final ET was compared to the one obtained using the hydra instrumentation (Eddy correlation method). The relative difference was inferior to 15 % most of the days, but could reach 60 % given the transpiration overestimation when the LAI was relatively high. This type of model is difficult to implement as it requires the knowledge of the surfaces resistances, which are difficult to estimate.

The Priestley & Taylor (1972) model estimates the current LE from a fraction of the

equilibrium LE0 (which corresponds to the potential LE if only the radiative conditions are taken into account) through a non-dimensional coefficient α (eqn 12). This method, based on the radiation approach, was theoretically validated by Mc Naughton (1976). The Evaporative Fraction (EF) is then exclusively linked to this α-parameter (eqn 13).

0LELE ×α= (12)

thus, ( ) ( ) α×γ+∆

∆=

−=

GRnLEEF (13)

The α parameter depends on the soil moisture content near the soil surface, ranging from less than 1.0 for a dry surface to 1.25 for an extremely wet surface (Priestley & Taylor, 1972; Monteny et al., 1997). Given this coefficient, the aerodynamic part is confirmed to be of about 20 % of the total ET. During the HAPEX-Sahel experiment, Monteny et al. (1997) related empirically the daily α-parameter to the soil water content moisture of a fallow savannah site (eqn 14). The evaporative fraction decreases when the soil water stock (0-0.6 m layer) is inferior to 70 % of its maximum value. This latter point stresses the limit between both the atmosphere demand and the soil control position.

−×−

−×=αmaxSS1

maxSS7.1exp11.1 (14)

With S/Smax the relative soil water content. The daily ET is correctly reproduced when comparing with the results of the Bowen ratio measurements (r² = 0.77) during a one-month period during the IOP. The scatter-plot is however relatively unsatisfying, with a slope of 0.75 and an intercept value of 1 mm, showing a systematic overestimation specially for low values.

A comparison of the three models (PM, PT, SW) sums up the behaviour of each model under semiarid conditions (Stannard, 1993). In this study, the estimated LE is compared to the Eddy correlation measurement, in a very sparse canopy with an extremely low LAI (inferior to 0.2). Under these conditions, both the SW and the PT provide better daily LE (r² = 0.85) than those obtained by the PM (r² = 0.6). The PM is less accurate because of the big leaf

CHAPITRE 1

48

assumption. The SW requires additional knowledge of the surface resistance. The SW tends to underestimate the small daily LE (as observed before), whereas the PT tends to underestimate the large daily values. This kind of comparison should be made in the sahelian area.

6.2 Aerodynamic Formulation

This formulation is taken from the fundamental equation which includes the aerodynamic resistance. The principle is based on the water vapor pressure deficit between the surface and the atmosphere level. This latter definition is generally used in the SVAT (Soil-Vegetation-Atmosphere Transfer) models. In this section, the equations of the SISVAT soil-vegetation module (Soil-Ice-Snow-Vegetation-Atmosphere Transfer) (De Ridder, 1997) are considered. The total LE is calculated as the sum of two main terms, the soil evaporation (LEs) depending on the relative moisture at the soil surface (eqn 15) and the plants transpiration (LEv) (eqn 16).

( )as

avsatga r

qTsq.hLEs −×ρ= (15)

( ) ( )cav

avsata rr

qTvq1LEv+

−×δ−×ρ= (16)

Where δ is the plant wet fraction, hg the ground surface relative humidity, ρa the air density, qsat-qav the specific humidity deficit depending on respectively the soil (Ts) and the vegetation (Tv) temperature. The flux is limited by the aerodynamic resistance of the soil (ras), the vegetation (rav), and the stomata (rc). This formulation requires a significant number of data (parameters, variables).

In the Sahel, two SVATs were applied to fallow savannah during 2-months, corresponding to the Intensive Observation Period. The first one, the SiSPAT (Simple-Soil-Plant-Atmosphere Transfer model) (Braud, 1996), represents a reference model given its great number of successful evaluations under various surface and climatic conditions. The second one, ISBA (Interactions Soil-Biosphere-Atmosphere) (Noilhan & Planton, 1989), was applied in its operational version, using a tabulated dataset. In the Sahel, the latent heat fluxes were correctly estimated at both 20-min (0.83 < r² < 0.90 for ISBA and SiSPAT) and 1-day time steps (r² = 0.8 for SiSPAT) (Braud et al., 1997; Goutorbe et al., 1997-b). The cumulated ET was estimated with a relative error inferior to 12 % for the two models. The main difference lies in the internal ET repartition, as the two models have a similar cumulated soil evaporation, but SiSPAT simulates a higher plant transpiration than ISBA (+ 35 %). Such gap may be due to the surface representation difference or to the parameters values as ISBA model used tabulated parameters. These two models were evaluated on a single vegetation type. The best results obtained using SVATS are comparable to those obtained by the best determinists models at daily time step.


49

In West Africa, the SISVAT surface scheme is coupled with the mesoscale atmospheric

model MAR (Gallée & Schayes, 1994). This coupled system aims at simulating both the monsoon phenomenon and the surface retroaction in this semi-arid area. Thus, SISVAT must be evaluated in this particular area. It will be tested with the HAPEX-Sahel dataset, (i) on fallow savannah to evaluate the surface representation and to make a comparison with ISBA and SiSPAT (Derive et al., subm.-a), and (ii) on the millet crop to study the model sensitivity under various surface conditions (bare soil, growing period) (Derive et al., subm.-b). For the two types of vegetation (millet, fallow savannah), the time evolution is correctly reproduced at various time steps, from 20-min (r² > 0.80) to daily scale (r² > 0.57). The maximal relative error equals 9 %, which is less than the one found by ISBA and SiSPAT. Two problems still remain: (i) the current parameters of all the types of vegetation are unknown, and (ii) an annual simulation is impossible as the energetic variables are not measured all year long. This model is dedicated to be coupled to the MAR atmospheric model. In that case, the atmospheric variables are simulated.

SVAT model can be spatially extended at watershed scale. For instance, Passerat de Silans et al. (1996) started to adapt an ISBA version on the Banizoumbou watershed, but the results were not as satisfying as expected. Indeed, the ET estimation is problematic at this hydrologic scale, mainly because of the heterogeneous repartition of the soil moisture, given the lateral redistribution of the water. Moreover, the parameters (resistances, saturated hydraulic conductivity) are only locally measured eventhough they are used at basin scale.

6.3 Conceptual Models

The implementation of a simple model in the sahelian area is of great interest given the very few data set available in such regions (Wallace, 1991; Taylor et al., 1997). The conceptual Ritchie's model (1972) enables to estimate the daily ET and to separate both the soil and the plants contributions. The soil evaporation is divided into two phases after a rainfall event, which have theoretically been explained by Phillip (1957). During the first phase, the soil evaporation (Es1) is equal to the potential soil evaporation (Es0), exclusively depending on the atmospheric demand (eqn 17). During the second phase, the soil evaporation (Es2) is limited by the soil moisture. The soil evaporation decreases as the square root of time proportionally to an hydrodynamic soil parameter called αs (mm.day-1/2) (eqn 18). The duration of the first phase (t1) depends on a parameter which characterizes the soil capacity (U).

t ≤ t1 UEs Es1tt

0t01 =∑ ∑=

=

= (17)

t > t1 ∑ −×α= )tt(Es1

s2 (18)

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50

The potential soil evaporation (Es0) under the vegetation layer is expressed through a Beer's law. The canopy transpiration is governed by the Leaf Area Index and is linearly related to the ETP value, as follows:

−

×= EsETP,

3LAIETPminEv (19)

This formulation was initially developed for temperate climates for well supplied crops with no hydric stress, which is not the case under the sahelian conditions. The soil module of this model was applied to the bare soil of the tiger bush (Wallace & Holwill, 1997) and to fallow savannah (Kabbat et al., 1997), but was only evaluated at a very limited time step. Given these encouraging although partial results, this simple model was recently applied to the three main types of vegetation at various time scales (from day to hydrological year) (Derive et al., subm.-c). The 2 months evaluation period results are quite satisfying, with a correlation coefficient ranging from 0.48 to 0.68 for the three types of vegetation. The relative error of the cumulated ET is close to 15 % during the whole period. Moreover, the simulations made at annual scale are coherent with the indirect ground measurements. 7. Conclusion

In the sahelian region, the evaporative flux represents the major component within both the surface water budget and the turbulent heat fluxes. During the HAPEX-Sahel experiment, direct and indirect measurements were made from punctual to micrometeorological scales. The total ET was then monitored using various techniques on the three main vegetation types (millet, fallow savannah, tiger bush) in various sites, during a short period (2 months), representing many environmental conditions. Few studies provided however the ET information at watershed scale. The main results that were obtained are summarized hereunder:

(i) More than 75 % of the rainfall is evapotranspirated at annual scale for all the types of vegetation. Both millet crop and fallow savannah have a similar annual rate (75-90 %), which appears as slightly lower on fallow savannah than on millet. This is mainly due to the peasants weeding which leads to a higher infiltration rate in the millet field. However, the role of the fertilization or of the slope has a great importance and may lead to a local inversion of this general trend. The choice of the sampled field has a significant impact on the results and must be carefully examined before any generalization. The annual ET nearly reaches the rainfall quantity (100 %) on the plateau covered by the tiger bush system, given its efficient organization. During the rainy season (August-September), fallow savannah and tiger bush evaporate more than the millet crop (about 23 % more).


51

(ii) The spatial variability of the total ET is of about 20 % between three neighbour plots covered by the same type of vegetation. Moreover, it strongly varies from one site to another, which is mainly due to the rainfall variability. (iii) The annual soil evaporation fraction is a significant part of the ET for both millet and the fallow savannah areas. The first day after the rainfall, the evaporation represents the major part of the total evapotranspiration of millet and fallow savannah. The transpiration term and the soil evaporation have a similar rate on the tiger bush given its specific spatial organization. In all cases, the soil module requires the most accurate estimation. (iv) The daily plant transpiration becomes the major term of all types of vegetation when the soil becomes drier, i.e., about two days after the rainfall or during the dry season. The transpiration is the dominant component of the annual water balance at the whole tiger bush scale, with 50-70 % of the total ET.

(v) At the watershed scale, the pool survey measurements indicate that the annual ET represents 80-90 % of the annual rainfall. This value complies with the mean annual aquifer recharge ranging from 10 mm to 47 mm per year (from 2 to 8 % of the annual rainfall) during the last decade (1990-99) (Leduc et al., 2001). These results, although found by independent measurements, converge to describe the

mean annual water balance. However we must keep in mind that their associated errors and their weak repetitivity do not enable to make further analysis or comparisons of the vegetation’s behavior. For instance, none of these in situ measurements were made during more than a 2-months period (August-September), which represents only 40 % of a complete rainy season (May-September). No measurement took place at the beginning of the rainy season, even if (i) that period could enable to monitor the bare soil behavior of various soil types under crusted conditions, and (ii) the rainy events are not as frequent, which leads to a small and limited water stock in the soil. Moreover, only a few direct measurements of soil evaporation and plant transpiration are available, even if such measurements appear as essential to understand and quantify their respective roles. The common measurement techniques are rather delicate to implement. Therefore, the plants transpiration is only determinated during a few days and only on few selected trunks. The lack of in situ measurements could be partially filled thanks to the future AMMA project (African Monsoon Multidisciplinary Analyses) (AMMA, 2002). This experiment will implement a meridian transect of the turbulent heat flux measurements in the soudano-sahelian transition in West Africa (from Mali to Benin). The evaporative monitoring should be performed on various types of vegetation during two complete rainy seasons 2004-05.

Until now, only the models enable to compare the evaporative term of each type of vegetation. The best modelisation of the daily ET was obtained by SVATs simulations with a significant performance (r² > 0.80) with a relative error of less than 12 % during the

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evaluation period. The surface representation emphasizes the need to explicitly take into account the surface heterogeneity. The applications of this kind of model are however limited by the number of data required given the lack of complete dataset in the sahelian zone. For instance, they were only applied to one type of vegetation (fallow savannah). Moreover, all the models were validated during the IOP, at the end of the rainy season, when the soil water stock is not a limiting factor. Therefore, the model evaluation was neither made on all the surfaces nor under dry meteorological conditions. Simple approaches enable to make longer simulations on various covers and could be interesting tools. The Ritchie model has shown a great ability to reproduce the sahelian evapotranspiration on specific surfaces. Its simulation on the three vegetation covers during a complete hydrological cycle shows that the relative contribution of each type of vegetation depends on the period considered. The limited observation period may lead to wrong conclusions if the results are extrapolated to a longer period. The need to make these estimations during a complete annual cycle, even more during a contrasted monitoring period, is of great importance.

A further step will consist in simulating the evaporative rate in case of various potential climatic changes. It is thus necessary to describe how the evaporative fraction is influenced by (i) the land cover modification due to the anthropic pressure, which tends to reduce the rotation between millet crop and fallow savannah (Loireau, 1998), while the tiger bush forests in the plateaus are cut to provide fuelwood, and by (ii) the rainfall frequency and the increasing dry spells which are the main factors to distinguish the dry from the wet years (Le Barbé & Lebel, 1997).

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Deuxième Chapitre

Evaluation du schéma de surface SISVAT en zone sahélienne : Représentation de surface

sur une zone de jachère

EVALUATION DE SISVAT SUR LA JACHERE

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CHAPITRE 2 EVALUATION DU SCHEMA DE SURFACE SISVAT : REPRESENTATION DE SURFACE SUR UNE ZONE DE JACHERE Résumé du Chapitre

Contexte de l'Etude

Le schéma de surface SISVAT (Soil-Ice-Snow-Vegetation-Atmosphere Transfer) (De Ridder, 1997; Gallée et al., 2001; Lefèbre et al., 2002) permet de simuler les échanges verticaux d'eau et d'énergie entre la surface et l'atmosphère. Son couplage actuel avec le modèle atmosphérique de méso-échelle MAR (Modèle Atmosphérique Régional) (Gallée et Schayes, 1994; Gallée, 1995) permet de reproduire le phénomène de mousson sur l'ensemble de l'Afrique de l'Ouest (objet de la thèse de W. Moufouma au LTHE). Les performances du schéma de surface SISVAT n'ont cependant jamais été évaluées sur cette zone d'étude sur laquelle il est cependant mis en œuvre. Par conséquent, il devient nécessaire d'évaluer SISVAT dans sa configuration actuelle, afin de quantifier ses performances et comprendre son comportement dans les conditions sahéliennes.

Pour ce faire, une simulation de SISVAT a été produite sur un site de jachère sur la région sahélienne d'HAPEX-Sahel. La jachère représente la principale couverture végétale sur la zone d'HAPEX-Sahel en 1992 (39 % de la superficie totale). La simulation est effectuée sur une période de 54 jours consécutifs (jours 239-292), débutant au cours de la saison des pluies et se terminant durant la saison sèche (dernier événement pluvieux le jour 264). Les flux évaporatifs sont au centre des discussions, et cela pour deux raisons majeures. Tout d'abord, l'évapotranspiration constitue la composante principale du bilan hydrique au sein de la zone de jachère, et les flux de chaleur latente sont prépondérants au sein des flux turbulents. Par ailleurs, les flux évaporatifs jouent un rôle majeur sur le régime des pluies simulées. En effet, ils forcent l'atmosphère en vapeur d'eau, gouvernant les taux d'humidité dans ses basses couches, provoquant ainsi un phénomène de rétroaction sur les précipitations au sol. Par conséquent, il est important de quantifier correctement ces flux de surface si on veut reproduire les caractéristiques de la mousson africaine.


La simulation numérique permet d'estimer les bilans d'eau et d'énergie à un pas de temps de 20 minutes. Les résultats associés obtenus montrent un très bon comportement général du modèle. En effet, les flux énergétiques moyens simulés sont du même ordre de

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64

grandeur que ceux mesurés. Leur cycle diurne est très bien reproduit (r² > 0.77; rmse < 50 W.m-2), notamment le rayonnement net (r² = 0.97, rmse = 33 W.m-2) et les flux de chaleur latente (r² = 0.92, rmse = 36 W.m-2). Pour sa part, le bilan hydrique est particulièrement bien simulé. L'évapotranspiration, le terme majoritaire, est correctement simulé, aussi bien au pas de temps de 20 minutes (r² = 0.92) qu'agrégé à l'échelle de la journée (r² = 0.65). Sur la totalité de la période de validation, l'erreur relative sur son cumul s'est avérée inférieure à 9 %. Cette dernière valeur peut être comparée à l'incertitude due à l'instrumentation des mesures (de l'ordre de 10 %). La répartition entre l'évaporation du sol et la transpiration a également été simulée, même si aucune mesure in situ n'est disponible pour valider cette distribution. Sur l'ensemble de la période d’évaluation, la fraction de transpiration est largement supérieure (environ 2/3 de l'évapotranspiration) à celle de l'évaporation du sol (1/3). Durant la saison des pluies, l'évaporation du sol est prépondérante (57 % de l'évapotranspiration), tandis qu'au cours de la saison sèche, la transpiration devient largement majoritaire (83 %). Le drainage profond et la variation d'eau dans le sol ont été relativement bien estimés, puisque les valeurs estimées sont du même ordre de grandeur que les valeurs mesurées.

Une surestimation générale d'évapotranspiration a cependant été mise en avant (+ 9 %). Diverses interprétations sont proposées au sujet de cet excès évaporatif. Tout d'abord, ce modèle (unidirectionnel) ne représente que les transferts verticaux d'eau. Les transferts latéraux ne sont cependant pas négligeables sur ce type de couverture végétale. Leur non prise en compte contribue à un surplus d'eau dans le sol, eau qui est alors disponible pour une exfiltration directe au niveau du sol ou pour le besoin hydrique des plantes. Cette tendance a été accentuée par la non prise en compte des croûtes de surface qui sont pourtant largement présentes dans ce type de végétation. Cependant, compte tenu de l'homogénéité des caractéristiques hydrodynamiques du sol le long de son profil, les croûtes ne peuvent être explicitement représentées. La représentation de la fraction de couverture végétale est également mise en défaut. Cette dernière reste constante toute l'année, engendrant une surestimation en début de saison des pluies. Finalement, la surestimation de l'évapotranspiration a été principalement mise sur le compte d'un surplus de transpiration. Ceci a notamment été montré au cours de la période sèche (lorsque l'influence du sol est quasiment nulle). La fonction de stress hydrique employée par le modèle ne permet pas de reproduire correctement la situation réelle, ce qui devra être analysé par la suite.

Deux autres schémas de surface avaient été testés sur le même site de jachère au cours de la même période de validation. Il s'agit des modèles ISBA (Interactions Surface Biosphère Atmosphère) (Noilhan et al., 1986) et SiSPAT (Simple Soil-Plant-Atmosphere Transfer) (Braud, 2000). Ces deux modèles ont déjà fait leurs preuves dans des conditions environnementales contrastées. SiSPAT est un modèle de référence dans la communauté scientifique compte tenu de son degré de représentation des processus. Les deux modèles ont bien simulé les bilans hydrique et énergétique, en employant des représentations de surface sensiblement différentes (homogénéité du sol, étendue de la couverture végétale, températures calculées). Une des préoccupations du projet HAPEX-Sahel a justement été d'améliorer le


65

réalisme et la justesse de cette représentation de surface des modèles. L'erreur sur le cumul des flux évaporatifs est de 8 % pour ISBA et + 12 % pour SiSPAT. Les résultats de SISVAT (+ 9 %) sont donc cohérents avec les résultats précédents. Les différences mises en avant sont dues principalement à la représentation de la strate végétative (couverture horizontale et atténuation radiative verticale).

Le schéma de surface SISVAT a correctement simulé les bilans d'eau et d'énergie, et a très bien reproduit les flux de chaleur latente. Mais, compte tenu du nombre important des paramètres (11 au total) et des variables d'entrées (6 au total), il reste important de tester la sensibilité du modèle à la paramétrisation choisie. Par conséquent, une étude de sensibilité est menée sur une culture de mil, où un jeu complet de mesures est disponible. Cela fait l'objet du chapitre suivant (chapitre 4). On a également vu que certains processus pouvaient être introduits pour tenter d'améliorer l'estimation de la reprise évaporative. Cette amélioration passe par les deux points suivants : la prise en compte du ruissellement de surface grâce à un couplage avec un modèle hydrologique (thèse de C. Messager en cours au LTHE), ainsi que l'évolution spatio-temporelle de la fraction de couverture végétale et la prise en compte de la croûte de surface. La fonction de stress hydrique devra également être évaluée séparément puisque son comportement peut potentiellement expliquer l'excès de transpiration.

CHAPITRE 2

66

EVALUATION OF THE SISVAT LAND SURFACE MODEL IN THE SAHEL

Authors: G. Derive(a)*, I. Braud(b), H. Gallee(c), and S. Galle(a)

(a) Laboratoire d'Etude des Tranferts en Hydrologie et Environnement

LTHE (CNRS UMR 5564), BP 53, 38041 Grenoble Cedex 9, France (b) CEntre national du Machinisme Agricole, du Génie Rural, des Eaux et des Forêts,

CEMAGREF, Lyon, France (c) Laboratoire de Glaciologie et Géophysique de l'Environnement,

LGGE, Grenoble, France

To be submitted to the Agricultural and Forest Meteorology journal

* Corresponding author. Fax: (+33)-4-76-82-52-86. E-mail address: [email protected] (G. Derive)

Abstract

The monsoon phenomenon determinates the rainfall regime in Western Africa. A RCM (Regional Climate Model) called MAR (Modèle Atmosphérique Régional) is used to predict its spatio-temporal evolution in the whole region. This mesoscale atmospheric model is coupled to a land surface scheme named SISVAT (Soil-Ice-Snow-Vegetation-Atmosphere Transfer). This vertical 1-D model couples the heat and the mass transfers within the Soil-Plant-Atmosphere continuum. The surface processes must be accurately simulated in this region, given the major feedback of such surface conditions to the rainfall regime. The objective of this study is to assess the performance of the model in order to predict both the energy and the water budgets at various time steps. The stress is made on the evaporative fluxes, keeping in mind that the upward surface fluxes lead to a feedback to the atmosphere.

The HAPEX-Sahel experiment (Hydrology Atmospheric Pilot EXperiment in the Sahel) provided one of the best documented dataset with regards to the soil-vegetation-atmosphere interactions in Africa. SISVAT runs on fallow savannah, which represents the dominant vegetation type in this sahelian zone. The validation period took place during a nearly 2-months period in 1992. Both the energy and the water budgets are accurately simulated during that period, although no calibration is made. The latent heat flux is correctly reproduced at 20-min (r² = 0.92) and 1-day (r² = 0.65) time steps. Its overestimation is inferior to 9 % during the whole period. Results comply with those obtained using other surface schemes (ISBA, SiSPAT) in the same location. The impact of the surface representation (cover, vegetation association, crust appearance) is discussed. Keywords: Latent heat flux, Fallow savannah, SISVAT model, MAR, HAPEX-Sahel, Niger.


67

1. Introduction

The West African climate is characterized by the strong spatial and temporal variability of its rainfall regime. Two main factors are considered as influencing such variability at many temporal and spatial scales. The first one is the Sea Surface Temperature (SST) which influences the rainy regime through the monsoon (Vizy and Cook, 2002). The second one is related to the coupling between the continental and the atmospheric components of the hydrological cycle. The response of the land surface to the atmosphere enhances the monsoon phenomenon (Wang and Eltahir, 2000). For instance, Charney (1975) first demonstrated that a vegetation reduction could cause a feedback mechanism decreasing the rainfall. This feedback was highlighted as well for West Africa by using models (Taylor et al., 1997a, 1997b; De Ridder, 1998) or making measurements (Taylor and Lebel, 1998). The current land cover changes due to the anthropic pressure can also lead to a regional change of the rainfall regime. This is why our knowledge of the land-atmosphere coupling mechanisms still requires some improvements, as their interactions influence the West African subcontinent. For this reason, the climate studies which use the Regional Climate Model (RCM) are essential. Such model must indeed include a representation of the surfaces, which is adapted to the West African context (CLIVAR, 1999). Specific validation studies of the Soil-Vegetation-Atmosphere Transfer (SVAT) schemes are therefore required in the semi-arid regions (Taylor and Clark, 2001).

The HAPEX-Sahel (Hydrology Atmospheric Pilot EXperiment in the Sahel) (Goutorbe et al., 1997a) experiment was made in the square degree of Niamey (2-3° East, 13-14° North) in Niger. It represents one of the most documented experiments with regards to the water and energy budgets in Africa. It is thus a useful semiarid site for a validation study of the land surface schemes. The soil-vegetation part of the SISVAT scheme (Soil-Ice-Snow-Vegetation-Atmosphere Transfer) (De Ridder, 1997; Gallée et al., 2001; Lefebre et al., 2002) is considered here. This validation is an important step towards the assessment of the RCM MAR (Modèle Atmosphérique Régional) (Gallée and Schayes, 1994; Gallée, 1995) ability to reproduce the West African climate. SISVAT was previously accurately tested under different climates and surface conditions (Belgium, Brazil, France, Israel) (De Ridder, 1997; De Ridder and Schayes, 1997; De Ridder and Gallée, 1998). In the present study, SISVAT evaluation is made on fallow savannah. This typical sahelian cover is an association of sparse grass and bushes. The relative contribution of each component enables to have a representation of the natural sahelian cover. Fallow savannah represents the dominant vegetation type in the sahelian HAPEX-Sahel area (39 % of the total area) (D'Herbes and Valentin, 1997). The used data were collected during the IOP (Intensive Observation Period) in 1992. The validation period lasts 54 consecutive days (DOY, Days Of Year 239-292).

This paper aims at analyzing the energy and the water budgets in a sahelian environment at

various time steps (20-min, 1-day, 2-months) at micrometeorological scale (some hectares). Both surface characteristics (sparse land cover, mixed plant species, crusted soil situation) and

CHAPITRE 2

68

atmospheric conditions (strong rainfall, high potential evapotranspiration) represent a severe test for the validation of the model. One of the objectives of the HAPEX-Sahel experiment was to improve both the realism and the accuracy of the surface representation on the sparse canopy in this sahelian zone. In this perspective, two surface schemes were previously applied to the same fallow savannah site during the same period, i.e., ISBA (Interface Soil-Biosphere-Atmosphere) (Goutorbe et al., 1997b) and SiSPAT (Simple Soil-Plant-Atmosphere Transfer) (Braud et al., 1997). The comparison of their results and the influence of their respective surface representation are discussed here. Keeping in mind that the upward surface evaporative flux makes feedback to the rainfall, the focus is made on the surface evaporative fluxes. Moreover, these evaporative fluxes represent the major component within both the water and the energy balances on the fallow savannah site. 2. The Model

2.1 General Description

The SISVAT (Soil-Ice-Snow-Vegetation-Atmosphere Transfer) scheme is a vertical 1-D model. The surface scheme includes a soil-vegetation (De Ridder, 1997), a snow (Gallée et al., 2001) and an ice module (Lefebre et al., 2002). Only the Soil-Vegetation-Atmosphere Transfer submodel is taken into account here (figure 1). The list of the symbols used is given in table 1. In this study, the model is forced by the measured meteorological data, i.e., the air characteristics (temperature and humidity), the wind speed, the incoming radiation (solar, long-wave) and rainfall. In an operational mode it would be supplied by the MAR outputs.


69

Figure 1 Schematic representation of the SISVAT soil-vegetation module. It includes both a soil (seven layers) and a vegetation (one layer) part forced by the atmospheric conditions (adapted from De Ridder, 1997).

SOILPROFILE

VEGETATION

ATMOSPHERE

qa, Ta

Layer 2

Layer 1

Layer 3

Layer 7

rag

rc

rav

Tv

rs7

rs3

rs2

rs1

qsat (Tv)

hg.qsat (Tg) Tg

BARE SOIL

ψ 1

ψ 3

ψ 7

ψ 2

CHAPITRE 2

70

TABLE 1 List of the symbols.

Latin symbols b Water retention curve exponent ( - ) cp Specific heat of air at constant pressure ( J.kg-1.K-1 ) cw Specific heat of liquid water ( J.kg-1.K-1 ) d Displacement height ( m ) di Thickness of the soil layer n° i ( m ) DL, D0 Downward radiative flux at the canopy base and top (W.m-2 ) Edir, Etr Direct and transpiration canopy water flux ( kg.m-2.s-1 ) Eg, Ev Ground and vegetation vapor flux ( kg.m-2.s-1 ) Gg Ground surface heat flux ( W.m-2 ) Hg, Hv Ground and canopy sensible heat flux ( W.m-2 ) hg Ground surface relative humidity ( % ) K Soil hydraulic conductivity ( m.s-1 ) Ksat Soil hydraulic conductivity at saturation ( m.s-1 ) Lv Latent heat of vaporization ( J.kg-1) LAI, LAIg Total and green Leaf Area Index ( m2.m-2 ) Le Effective leaf area index ( m2.m-2 ) rain rainfall intensity ( mm.s-1 ) qa Specific humidity of air at reference height ( kg.kg-1 ) qsat Saturation specific humidity of air at reference height ( kg.kg-1 ) Rlg, Rlv Net long-wave energy flux balance of soil and vegetation ( W.m-2 ) Rsg, Rsv Net short-wave energy flux balance of soil and vegetation ( W.m-2 ) rag, rav Ground and canopy aerodynamic resistance for heat ( s.m-1 ) rah Aerodynamic resistance for heat ( s.m-1 ) rc Canopy stomatal resistance ( s.m-1 ) rp Total plant resistance ( s ) rsi Soil resistance at layer n° i ( s ) r0 Minimal (unstressed) leaf stomatal resistance ( s.m-1 ) Sv Canopy heat storage flux ( W.m-2 ) t Time Ta Air temperature at reference level ( °K ) Ti Soil temperature of the layer n° i ( °K ) Tg, Tv Ground surface and vegetation temperature ( °K ) U0 Upward radiant flux at the canopy top ( W.m-2 ) v Wind speed ( m.s-1 ) wl Water equivalent of leaf biomass ( kg.m-2 ) wv Canopy intercepted water content ( kg.m-2 ) Wg, Wv Ground and vegetation water flux ( kg.m-2.s-1 ) zom Roughness length for momentum ( m )


71

Greek symbols αg Bare soil albedo ( - ) αgd Dry bare soil albedo ( - ) δ Wet leaf fraction on canopy surface ( % ) εg Soil emissivity ( no unit ) µ Soil thermal conductivity ( W.m-1.K-1 ) ϕ0.1m Root fraction upper 0.1 m ( % ) ϕi Root fraction per layer n° i ( % ) ψ Soil water potential ( m ) ψc Critical leaf water potential ( m ) ψg, ψv Ground and canopy water potential ( m ) ψi Soil water potential of the layer n° i ( m ) ψsat Soil water potential at saturation ( m ) ρa Air density ( kg.m-3 ) ρw Liquid water density ( kg.m-3 ) σ Stefan-Boltzman constant ( W.m-2.K-4 ) σv Fractional vegetation cover ( - ) θi Soil water content of the layer n° i ( m3.m-3 ) θsat Soil water content at saturation ( m3.m-3 )

2.2 Soil and Vegetation Modules

The soil hydraulic properties are assumed as being homogeneous along the whole soil profile. The soil water potential (ψ) (eqn 1) and the hydraulic conductivity (K) (eqn 2) are expressed as suggested by Brooks and Corey (1964) and adapted by Clapp and Hornberger (1978). The ground surface albedo (αg) is parameterized (McCumber and Pielke, 1981) as a function of the dry value (αgd) (eqn 3). Note that in this expression, the albedo of a wet soil surface is twice lower than the one of a dry soil surface. Time evolution of these soil hydraulic properties is linked to the variation of the volumetric soil moisture content (θ).

bsat

sat)(

θθ

×ψ=θψ (1)

3b2

satsatK)(K

+×

θθ

×=θ (2)

α×

θθ

−×α=θα gdsat

gdg21),1(max)( (3)

CHAPITRE 2

72

Five soil parameters are required : the shape parameter of the water retention curve (b), and four scale parameters which are the saturated water content (θsat), the water potential at saturation (ψsat), the hydraulic conductivity at saturation (Ksat), and the dry soil albedo (αgd). In this study, the measured values are available and preferred to default values used in the operational mode. In case of a lack of measurement, the parameters are derived from pedo-transfer functions for each type of soil defined in the USDA (United State Department of Agriculture) classification. The soil temperature and the moisture are calculated at the central node of each soil layer.

The vegetation is described using five parameters: two heights which are the displacement height (d) and the roughness length for momentum (zom), and three internal plants characteristics, the root fraction (ϕ0.1m), the minimum stomatal resistance (r0) and the global plant resistance (rp). These characteristics can be either selected in the IGBP (International Geophere-Biosphere Program) classification (seventeen possible types of vegetation) or specified by the user. The latter option is retained in this study. The vegetation module is represented through one vertical layer. Time evolution of the canopy is described using both the total Leaf Area Index (LAI) which reduces the downward solar radiation, and the green fraction which controls the plants transpiration. The vegetation heterogeneity is taken into account using a patch approach. Each patch (bare soil, homogeneous vegetation) is weighted by its cover ratio. The vegetation cover remains constant throughout the simulation, and equals to its maximum annual value.

Four non-linear coupled equations are solved in order to represent the energy and the water

conservation. The energy and the water budgets are calculated separately for both the soil and the vegetation, within each patch. The expression of each component and the formulation of the surface resistances are given in the appendix and described in De Ridder (1997). The energy equation is numerically solved using the Crank-Nicholson iteration technique for the soil temperature. The water transfers are numerically modeled using the semi-implicit Dublin's scheme for the soil (Dooge et al., 1993) and the Newton-Raphson technique for the vegetation system. The radiation attenuation by the vegetation layer is expressed using an exponential relation (Beer's law) by taking into account LAI.

2.3 Latent Heat Flux Expression

In the SISVAT model, the latent heat flux is split into soil and vegetation contributions. Indeed, the total latent heat flux is calculated as the sum of three components (eqn 4) including the soil evaporation Eg (eqn 5), the evaporation of the rainfall intercepted by the vegetation Edir (eqn 6), and the plant transpiration for the non-wet part of the canopy Etr (eqn 7). These fluxes are expressed using an electrical analogy, depending on the water vapor pressure deficit limited by an aerodynamic resistance.


73

( )trdirgvv EEELEL ++×= (4)

with ( )as

agsatgag

rqTq.hE −

×ρ= (5)

( )av

avsatadir

rqTqE −

×δ×ρ= (6)

( ) ( )cav

avsatatr

rrqTq1E

+−

×δ−×ρ= (7)

The latent heat of the water vaporization (Lv) only depends on the air temperature. The air

density (ρa) depends on the general air conditions (temperature, pressure, humidity). The soil specific humidity deficit (qsat-qa) depends on the soil surface temperature (Tg) (eqn 5), while the vegetation specific humidity deficit depends on the leaf temperature (Tv) (eqn 6). Heat transfer from the surface to the atmosphere is limited by both the aerodynamic soil (ras) and vegetation (rav) resistances. The ground surface relative humidity (hg) within the soil evaporation quantifies the available water at the soil surface. It depends on both the soil water potential at the ground surface (ψg) and the ground temperature (Tg). In the plant transpiration expression, the canopy stomatal resistance (rc) is directly linked to the unstressed stomatal resistance, to the effective LAI (i.e. the leaves that receive enough radiation to maintain their transpiration) and to a stress function characterizing the leaf water potential dependence. The wet fraction (δ) represents the normalized amount of intercepted water (wv/wvmax). In the present study, the evaporation from the intercepted water is negligible, given the very low LAI values and the large bare soil areas.

2.4 Model Set Up

The model makes a mosaic approach to take into account the surface heterogeneity (Koster and Suarez, 1992). Such mosaic is separated into subgrid patches including bare soil and homogeneous vegetation types. The fluxes from each patch are weighted according to their cover fraction. In this study, two patches are used to describe fallow savannah (bare soil area, vegetated zone). In this scheme, the respective aerodynamic resistance and the temperature are expressed in parallel for the soil (ras, Tg) and the vegetation (rav, Tv). The soil profile is discretized using seven sub-surface layers of respectively 0.5, 1.5, 5, 15, 50, 150 and 500 cm thick. The skin layer depth (0.5 cm) is equal to the one taken into consideration in the two previous studies running on this fallow savannah area using SiSPAT and ISBA. The six underlying layers are automatically calculated relation from the first layer thickness, by using an exponential.

The turbulent (sensible and latent heat fluxes) and radiative fluxes (reflected solar, emitted thermal long-wave) are calculated at a 5-minutes time step. These data are then averaged to 20-minutes periods in order to be compared to the measured values. A 3-months spin up period is needed.

CHAPITRE 2

74

3. Model Implementation and Evaluation

3.1 The Study Area

The HAPEX-Sahel experiment was implemented in the square degree of Niamey (2-3° East, 13-14° North) in southern Niger (figure 2) (Goutorbe et al, 1997a).

Figure 2 Geographical location of the HAPEX-Sahel experiment in Niger. The square degree of Niamey (2-3° East, 13-14° North) includes the experimental Southern (SSS), West Central (WCSS) and East Central (ECSS) Super-Sites.

The fluxes data were collected during the IOP (Intensive Observation Period) between August 15th (DOY 222) and October 9th (DOY 283). This period covers the last month of the rainy season and the first month of the dry season. In the HAPEX-Sahel area, the plains represent the major part of the landscape (72 % of the total HAPEX-Sahel area). They are covered by loamy sand (88 % sand, 4 % clay, 8 % silt) (Nagumo, 1993). Both the millet and the fallow savannah areas are located in these plains (2-4 % slope). The millet field and fallow savannah zones represent respectively 22 % and 39 % of the HAPEX-Sahel area. The "tiger bush" forest covers the flat lateritic plateau area (28 % of the total HAPEX area). This natural vegetation is composed of an alternance of parallel bare soil and natural tree bands (Galle et al., 1999). The soil type is sandy clay loam (41 % sand, 39 % clay, 20 silt) (Nagumo, 1993). The plateaus represent the highest points of the landscape (about 240 m high above the mean sea level). Rain falls during one short rainy season (beginning in June, end in October), but 90 % of the annual rain falls during only three months (July to September). The climatology rainfall North-South gradient is close to 1 mm per kilometer in the Sahel. The average annual rainfall was of 564 mm in Niamey from 1950 to 1989 (Le Barbe an Lebel, 1997). The convective nature of the precipitation is responsible for both the strong intensity of the rainy events (50 % of the annual rain falls in five hours) and the significant rainfall spatial heterogeneity. The strong annual potential evaporative rate (about 2000 mm) represents about four times the annual rainfall (Sivakumar, 1987).

2°E 3°E

ECSSWCSS

SSS

Niamey

Niger

14°N

13°N

10°N

0°N-20°W 10°E

HAPEXSahel

Banizoumbou


75

The fallow savannah studied site (2°40.93' East, 13°33.49' North) is located in the ECSS (Eastern Central SuperSite) near the Banizoumbou village.

3.2 Atmospheric Forcing

The micrometeorological data were collected by the CNRM team using a standard meteorological station. The air temperature (°K) and windspeed (m.s-1) were measured respectively at 2 m and 10 m above the surface level. They were averaged and recorded at a 20-minute time step. The sensors were respectively a Vaisala HMP35AC and a vector anemometer. The air humidity (kg.m-3) was deduced from both the water vapor pressure (Pa) and the atmosphere pressure (hPa) measurements. The incoming solar radiation (W.m-2) was measured at the climatic station of Banizoumbou (2°39.64' East, 13°31.93' North) using a Kipp and Zonnen Pyranometer. It was measured 2 meters above the surface level with an uncertainty of about 7 W.m-2. The distance between the study sites is of nearly 4 km. The available incoming long-wave radiation (W.m-2) is measured at the Southern SuperSite, located about 60 km from the fallow site. Rainfall was locally measured using an automatic rain-gauge. Rainfall data are available at a 5-min time step. Time evolution of the daily rainfall is illustrated in figure 3.

As the model requires a 5-minutes time step for the input data, a linear interpolation is made for the 20-minutes available data (except for rainfall).

Figure 3 Time evolution of the daily local rainfall (in mm) including the fluxes observation period (DOY 239-292).

0

10

20

30

40

50

60

200 210 220 230 240 250 260 270 280 290

DAYS OF YEAR ( 1992 )

RA

INFA

LL

( m

m ) Validation period

CHAPITRE 2

76

3.3 Soil Properties

The type of soil in the plains is deep reddish brown sand (Nagumo, 1993). The five hydraulic parameters (αgd, θsat, ψsat, Ksat, b) characterizing the valley sandy soil and the related references are given in table 2. Table 2 The soil parameters characterizing the deep reddish sandy soil of the valley zones, where the fallow savannah grows.

Soil Parameters Values Sources

Dry soil albedo (αgd, -) 0.27 Goutorbe et al. (1997b)

Saturated water content (θsat, m3.m-3) 0.30 Braud et al. (1997)

Water potential at saturation (ψsat, m) - 0.1 Gaze et al. (1997)

Hydraulic conductivity at saturation (Ksat, m.s-1) 5.4 10-5 Vandervaere et al. (1997)

Water retention curve exponent (b, -) 1.5 Braud et al. (1997)

Goutorbe et al. (1997b) measured the bare soil albedo (0.27) under dry conditions on the fallow savannah site. In the same site, Braud et al. (1997) reported a saturated water content ranging from 0.28 to 0.33 m3.m-3 along the soil profile (down to 4 m). A geometric mean of 0.30 is retained here, which corresponds to the measurement of Daamen (1993) on similar sandy soils valley. The value of the water potential at saturation (-0.1 m) is the value suggested by Gaze et al. (1997) in a similar type of soil. This latter value represents as well the value calibrated by Braud et al. (1997) in the second soil horizon (0.20-2.50 m). The hydraulic conductivity at saturation (5.4 10-5 m.s-1) was measured on a nearby fallow savannah site by Vandervaere et al. (1997) on uncrusted soil. The water retention curve exponent (1.5) is the one reported by Braud et al. (1997), which is close to the value (1.4) used by Gaze et al. (1997) in a very similar type of soil located in the southern site.

3.4 Vegetation Characteristics

Fallow savannah is composed of two vegetation layers : an annual bush (Guiera senegalensis) layer (2-4 m high), and an annual low grass layer (20 cm) which grows during the rainy season. In this study, the surface representation of fallow savannah is divided into two patches: a bare soil area and an homogeneous vegetation layer which combines the association of two vegetation strata (grass and bush). The five plants parameters applied to fallow savannah and the related references are given in table 3. The vegetation parameters selected (d = 0.38 m, zom = 0.07 m, r0 = 80 s.m-1) are the same than those suggested by other


77

authors (Braud et al., 1997; Goutorbe et al, 1997b, Braud, 1998). The associated ground measurements were made by Tuzet et al. (1994) and Hanan and Prince (1997). The root fraction retained in this study (0.25) is deduced from the root profile measured by Hanan and Prince (1997). The total plants resistance (9.4.108 s) is calculated by taking into account both the plants resistance in the root zone and the mean root density described by Braud et al. (1997). Table 3 The vegetation parameters retained for the model validation on the fallow savannah site.

Vegetation Parameters Values Sources Displacement height (d, m) 0.38 Tuzet et al. (1994)

Roughness length for momentum (zom, m) 0.07 Tuzet et al. (1994)

Root fraction upper 0.1 m (ϕ0.1m, -) 0.25 Hanan and Prince (1997)

Minimum stomatal resistance (r0, s.m-1) 80 Hanan and Prince (1997)

Internal plant resistance (rp, s) 9.4.108 Braud et al. (1997)

The plants characteristics were locally measured by B. Monteny (1993). They were deduced from occasional biomass measurements by making a statistical linear interpolation. LAI is assumed to be the sum of the bush (up to 0.25 m2.m-2) and the grass (up to 0.95 m2.m-

2) contributions. Both the total LAI and the green leaf fractions are independently inserted in the model. Time evolution of both the total (LAI) and the green (LAIg) leaf area index are described in figure 4. The plant cover fraction (vegetation horizontal spatial extension) is a constant in the model. It equals 72 %, which is the maximum recorded ratio during the monitored period, when the grass growth is over.

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78

Figure 4 Time evolution of the total (g) and the green (n) Leaf Area Index (LAI) of fallow savannah in 1992, including the validation period (DOY 239-292). (after Monteny, 1993)

3.5 Water and Energy Budget Measurements

The surface energy fluxes were recorded every 20-min using a micro-meteorological station. The heat fluxes data are available during 54 consecutive days (Days Of Year 239-292), which corresponds to the evaluation period of the SISVAT model. The last significant rainy event of the season (6.5 mm) took place DOY 259 in 1992. The following months (DOY 264-292) will be called the dry season. Several turbulent fluxes data are missing. They correspond to about 8 % of the whole period (302 out of 3888). The measured area depends on the wind speed and direction. It covers some hectares (typically 100×300 square meters). The net radiation was observed 8 m above the surface. The turbulent heat fluxes were measured using the Eddy correlation method. The ground heat flux was not measured at this local station. It is estimated as the only remaining term of the energy balance equation, and therefore it creates a high error. The repartition between the soil and the vegetation part was not measured either.

The total evapotranspiration within the water budget is deduced from micro-meteorological

measurements of the latent heat fluxes. Soil moisture profile was measured using the neutron probe technique (down to 2.6 meters soil depth). This type of measurement was made on eleven points in the fallow savannah area, and only the spatial average is considered here. Note that this soil water stock is only available during three specific days (DOY 239, 288, 302). Therefore, the soil water stock variation is calculated directly between DOY 239 and

0

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288, whereas a linear interpolation is required between DOY 288 and 302 when no rain falls. The deep drainage is finally deduced from water balance considerations.

3.6 Model's performance Criteria

The performance of the model is assessed using statistical criteria characterizing the scattering (r², rmse) and the bias (slope, intercept) of the linear relation between the simulated (Vsim) and the measured (Vobs) variables (eqn 8). The root mean square error (rmse) is calculated as hereunder (eqn 9), where N is the number of available pairs.

InterceptVSlopeV obssim +×= (8)

( )∑ −×−

= 2obssim VV

1N1rmse (9)

4. Validation Period's Results

4.1 Energy Budget Simulations

The observed and modeled mean values of the energy budget components (net radiation, latent, sensible and ground heat flux) are provided in table 4. The mean energy values are correctly predicted by the model. The maximum relative error is of about 9 %. Both the net radiation and the sensible heat flux are underestimated of respectively -5 % and -6 %. The latent heat flux is overpredicted with a relative error of +9 %. This value is inferior to the experimental spatial variability estimated to be of about 20 %. This latter value was reported by Lloyd et al. (1997) by comparing the fluxes using three measurement stations within only one fallow savannah site during the HAPEX-Sahel experiment. The relative error found by SISVAT is also inferior to the ground measurements uncertainty, usually near 10 %. This overestimation of the latent heat fluxes will be discussed hereafter. The modeled ground heat flux is negative whereas the only remaining term of the measured energy balance indicates a ground heat flux storage. Although this term is low, it represents the highest absolute error (-11 W.m-2).

The scatterograms of the modeled versus the observed fluxes are made at 20-min time step

in figure 5. The associated regression coefficients are shown in table 5. The model provides an accurate and unbiased estimation of each flux at this high time resolution, except of the high ground heat flux values (above 120 W.m-2) which are systematically underestimated. The dispersion of each scatter diagram is relatively low, especially for the net radiation. The associated determination coefficients (r²) are equal or superior to 0.77. The best results are observed for both the net radiation (r² = 0.97) and the latent heat flux (r² = 0.92). Note that the net radiation is correctly estimated, eventhough the input downward radiation is not measured

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nearby. The regression coefficient at a daily time step (r² = 0.65) is lower than the one at 20-min time step (r² = 0.92) given the cyclic time evolution of the latent heat flux variable. Table 4 The In situ observations and SISVAT simulations of both the energy (W.m-2) and the water (mm) budgets during the validation period (DOY 239-292). The results found by SiSPAT and ISBA are also mentioned. Note that the cumulated ET is calculated during the whole period using a linear interpolation when data are missing. (* estimated as the only remaining term of the energy balance equation).

Obs. SISVAT SiSPAT ISBA Energy Balance

Rn 126 120 (-5 %) 122 -

LE 81 88 (+9 %) 89 74

H 36 34 (-6 %) 36 -

G* 9 -2 - 3 -

Water Balance

Rainfall 144.4

ET 150.7 164.1 (+ 9 %) 168.8 138.0

Soil evaporation - 60.3 - 40.8 40.2

Vegetation transpiration - 103.8 - 128.0 95.0

Runoff - 0 - 0 0

Soil moisture variation -15.0 -24.1 (-61 %) -42.4 7.2

Drainage (< 2.6 m) * 8.7 4.4 (-49 %) 18.0 0

Table 5 The coefficients of determination (r²), the root mean square error (rmse), the slope (a) and the intercept (b) of the regression line. Except for the daily ET, the regressions are made at a 20-min time step. Both rmse and b are expressed in W.m-2 for the energy terms, and in mm for the daily ET.

r² rmse a b

Rn 0.97 32.7 0.89 8.8

LE 0.92 35.7 1.06 5.5

H 0.84 27.5 0.81 5.4

G 0.77 48.8 0.60 -9.8

Daily ET 0.65 0.57 0.88 0.65


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Figure 5 The scatterograms of the simulated versus the observed 20-min values for (a) the net radiation, (b) the latent heat flux, (c) the sensible heat flux and (d) the ground heat flux of the fallow savannah site. The expressions of the regression curves are given in table 5.

The daily evolution of the energy budget components, during three specific day, is illustrated in figure 6: DOY 256 (the day just after a 28.7 mm rainfall), DOY 254 (3 days after a 27.6 mm rainfall) and), and DOY 280 (21 days after the last rainy event). The daily variations are correctly reconstituted for the wet soils (DOY 256). Three days after a rain event, the net radiation and the sensible heat fluxes are correctly modeled but an overestimation of the latent heat flux can be noticed. Consequently, the ground heat flux naturally reacts on the opposite way. During the last observation day (DOY 280), the maximum measured net radiation reaches 600 W.m-2 and is composed of three similar components. The maximum net radiation (500 W.m-2) of the simulation is lower than the

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measured value, and the estimated latent heat fluxes are higher than the measured ones and than the two other components (sensible and ground heat flux). The quality of the simulation decreases as the soil becomes drier. The model may overestimate either the soil capacity to send the water back to the atmosphere or the plants transpiration. The discussions are made later below.

DOY 254Observation

-100

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DOY 256Model

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Figure 6 The observed and the modeled diurnal cycle of the energy budget components during three specific days : DOY 256 (the day just after a 28.7 mm rainfall), DOY 254 (3 days after a 27.6 mm rainfall), and DOY 280 (21 days after the last rainy event).

DOY 280Observation

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4.2 Water Budget Simulation

The observed and modeled fallow savannah water balance during the reference period are given in table 4. The water budget amounts are correctly simulated by the model. They are very similar to the observed ones. The cumulated ET represents the main component of the water budget (95 %). ET is well predicted with a relative error of + 9 % during the whole period. Note that the error occurs during the dry season period. The ET overestimation represents a major issue which is worth discussing. In this surface scheme, runoff (horizontal water flow) is supposed to be void, which therefore tends to increase the amount of the other components. The modeled soil water storage variations in the 0-2.6 m layer during the whole period (- 24 mm) are close to the measured ones (-15 mm). The resulting deep drainage (below 2.6 meters), estimated by SISVAT (4.4 mm) is very close to the measured one (8.6 mm).

The partition between the soil and the vegetation component is simulated even if no in situ

measurements are available to validate it. During the whole validation period, the average simulated plant transpiration (63 % of the total ET) is about two times higher than the soil evaporation (37 % of the total ET), but this proportion varies with time. In order to observe this repartition long term behaviour, the evaporative fluxes are averaged at a daily time step. Time evolution of the daily ET and its internal components are shown in figure 7.

Figure 7 Daily time evolution of the simulated (____) and the observed (♦) ET. The modeled ET is split into the soil evaporation (n) and the vegetation transpiration ( ). The vertical dotted line indicates the last significant rainy event of the year.

0

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DAYS OF YEAR (1992)

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m)

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During the core of the rainy season, when rain falls regularly every week (before DOY 259), the soil evaporation (57 % of ET) is superior to the vegetation transpiration (43 % of ET). On the opposite, during the dry season the transpiration process becomes preponderant, whereas the soil evaporation remains at a very low rate given the soil moisture shortage. During that period, transpiration represents 83 % of the total ET. The opposite trend begins about one week (DOY 265) after the last significant rainy event (DOY 259). During that period, the daily plant transpiration represents up to 10-20 times the soil evaporation. The daily soil evaporation finally reaches 0.1 mm (DOY 292), one month after the last rainy event. During the last 20 days when the soil contribution becomes negligible, the simulated transpiration rate remains 0.3 mm above the measured ET. The cumulated plant transpiration is systematically overestimated and consequently the total ET is overestimated as well, as observed during the total period. This overestimation trend is also illustrated in the scatter diagram between the modeled and the measured daily ET (figure 8). Indeed, a low systematic excess of ET is highlighted during the dry season. The determination coefficient of the daily ET remains however relatively high during both the dry season (r² = 0.84) and during the core of the rainy season (r² = 0.63). The final evolution is well reproduced (r² = 0.65) with a low bias.

Figure 8 Scatterograms of the simulated versus the observed daily ET value, respectively during the rainy season (until DOY 264) ( ), and during the dry season (n)

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)


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5. Surface representation Scheme Discussion

5.1 SISVAT Simulations

The ability of the model to reproduce both the water cycle and the energy diurnal cycle was demonstrated from 20-min to 2-months time steps. These results are satisfying as no calibration was ever made before the simulation. During the rainy season, the model has no bias. However, an evaporative overestimation is noticed when soils become dry. The simplicity of the surface representation and the difficulty to get a reliable and repetitive parameter set may explain this high evaporative rate. Four sensitive points are emphasized with regards to the model implementation and to the specific sahelian environment.

Firstly, the vegetation cover fraction is a constant of the model even if the effective cover increases during the rainy season from 36 % (DOY 239) to 72 % (DOY 292). Such simplification has a strong impact on the transpiration rate which is directly proportional to the selected plant cover fraction. For instance, this assumption directly doubles the estimated transpiration rate at the beginning of the validation period when the current plant cover is low. The global evapotranspiration is logically overestimated during that period, as the soil evaporative difference (between the soil under vegetation and the bare soil area) is not enough to compensate the transpiration excess. However, LAI indirectly expresses the soil cover change within the SISVAT scheme. Since the LAI is low at the beginning of the validation period, the evapotranspiration absolute error remains low. However, given the lack of transpiration measurements, the current transpiration rate cannot be validated. On the opposite, at the end of the validation period, the model vegetation cover is nearly equal to the measured one during the dry season. This parameter has then no impact on the transpiration rate during the dry season, and cannot explain the evapotranspiration overestimation. Time evolution of the cover fraction may however be a future step for the SISVAT implementation, as this value may induce significant errors during the dry spells of the rainy season. This is all the more true for the high LAI type of vegetation, such as millet crop. Such new version of the model will be however rather long to implement given the current computed program organization. We must also keep in mind that fallow savannah is described here as a homogeneous vegetation layer, equal to the sum of both the grass and the bushes contributions. The individual LAI are then directly added. In reality the bushes are not contiguous, and the grass layer grows under and between bush cover. This assumption tends therefore to increase the green fraction, which directly governs the transpiration process. As the LAI estimation is not precise enough on the wide heterogeneous areas, a test of the model sensitivity on the plants characteristics appears as a future essential step to understand the model behaviour.

Secondly, both runoff and evapotranspiration processes are strongly affected by the crust appearances in the sahelian zone, as reported by Casenave and Valentin (1992). This phenomenon tends to increase the runoff process and then to limit the evaporative potential all

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over the year. Note that the crust appearance can be a starting point enabling the growth of a specific spatial vegetation organization. This is the case of the tiger bush system where the crusted bare soil over-supplies of water the downslope forest bands, which can then survive (Galle et al., 1999). In the fallow savannah, the soil crusting was modeled by adding a very thin surface layer (1-10 mm), to a contrasted hydraulic conductivity. On a nearby site, Vandervaere et al., (1997) measured a crust saturated hydraulic conductivity 3-5 times lower than the underlying soil. This crust appearance does not only reduce the soil infiltration capacity during a rainy event, but also prevents the soil evaporation when the soil becomes dry. For instance, the crusts simulations (with a saturated hydraulic conductivity 100 times lower than the underlying soil) provided a cumulated ET about 24 % lower than the non-crusted soils simulations (Braud et al., 1997). Such variation is mainly due to a soil evaporation change. In fact, a split between crusted and non-crusted zones appears at studied scale, which makes the problem rather complex. In the SISVAT model, the crust is not taken into account. Indeed, it is not possible to represent it easily given the homogeneous parameterization of the soil profile. An intermediate saturated hydraulic conductivity was tested (1.8 10-6 m.s-1) between the underlying layer and a crust skin (Braud, 1998). This simulation that was made using this latter option in SISVAT tends to increase the global ET of about 15 % compared to the initial non-crusted soil, but gives a similar evapotranspiration value if ψsat is jointly modified. The soil hydraulic conductivity has a strong impact on evaporation and must be considered carefully. A future step in the SISVAT implementation will be to explicitly take into account the soil crust.

Thirdly, we must keep in mind that this land surface model is a one-dimensional approach, which does not directly take into account the horizontal process (runoff), which redistributes the incident rainfall on the watershed. A non-negligible runoff appears at parcel scale. For instance, Peugeot (1995) measured a mean annual runoff of 23 % of the total rainfall during two years (1992-93) in a nearby fallow savannah site. Runoff reduces the soil water storage, and then the exfiltration process. Moreover, runoff can reach up to 68 % of the rainfall at local scale during substantial rainy events. Esteves et al. (2000) show how the roughness fixes the local infiltration and the associated spatial soil moisture heterogeneity. At field scale, main part of the runoff that is generated infiltrates the downslope within the same field and never reaches an organized stream. This may explain why the relative evapotranspiration error, which is estimated at field scale the day following the rainy event, is low although this phenomenon is not taken into account in the SISVAT model. The current model coupled to the ABC hydrological model (Seguis et al., 2002) will soon enable to both observe and understand the runoff effect within the SISVAT surface scheme.

Fourthly, another issue which is worth discussing is the plants hydric stress. Indeed, this phenomenon takes place during the dry season, when the transpiration is largely overestimated. The semi-arid sahelian plants often meet hydric stress, even if they are selected to face extreme conditions. In this fallow savannah site, the hydric stress was mainly observed on the grass layer, whereas the bushes are more resistant, as they have a deeper root system.


89

In this surface scheme, the hydric stress function (Fst) (eqn 11) increases the canopy stomatal resistance (eqn 10) through a leaf water potential dependence, as follows:

st0

c FLerr ×= (10)

with ( ) 1cvst /1F −ψψ−= (11)

Where ψv is the canopy water potential and ψc the critical leaf water potential (m). In the initial version of De Ridder (1997), the critical value (ψc) equals -250 m all over the world, which appears relatively high in the arid areas where a value of -140 m is generally used (Gaze et al., 1997; Braud et al., 1997). This latter value was retained in this study, which slightly reduces the transpiration term (-14 %), mainly during the dry season. The ψv term implicitly takes into account the stress related to both the soil moisture, the atmospheric saturation deficit and the canopy temperature, which explains the efficiency of this stress function. It would be however interesting to compare this simple function to the complex one (five parameters) suggested by Hanan and Prince (1997) in order to follow the time evolution of the stomatal resistance.

5.2 Comparison with SiSPAT and ISBA

(a) Surface scheme Description

In the literature, SVAT (Soil-Vegetation-Atmosphere Transfer) models use different surface representations for both the soil and the vegetation modules. Two surface schemes were previously applied to this sahelian zone. The first one, SiSPAT (Simple Soil Plant Atmosphere Transfer model) (Braud et al., 1995; Braud, 2000), is a reference surface scheme, which was positively tested on various climatic zones. For this reason, SiSPAT is a reference model. The second one, ISBA (Interface Soil-Biosphere-Atmosphere) (Noilhan and Planton, 1989), is a simpler surface scheme. Indeed, it reduced as much as possible the number of parameters to be able to run in all parts of the world. The comparison between SISVAT and these two models is therefore an important issue.

In the ISBA scheme, the bare soil evaporation and the plants transpiration are calculated separately, as in SiSPAT. However, only one surface temperature is evaluated on an equivalent homogeneous surface, whereas different temperatures are used in SISVAT and SiSPAT. In a first study (Braud et al., 1997), SiSPAT was used in its standard version by taking into account a homogeneous vegetation cover. In a second study in a nearby site with a more degraded fallow savannah Braud (1998) considers two patches of respectively bare soil and vegetation. It was demonstrated that in this case, a more reliable simulation of both the net radiation, the surface and the soil temperatures was made. In SiSPAT, the relevance of

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using two patches on the sparse vegetation was highlighted by Boulet et al. (1999). They demonstrated that the model simulation is only improved if the patches are of sufficient extent. This patch size condition is fulfilled in the Sahel. In the SiSPAT model, the soil profile (4 m deep) is divided into three horizons (0.2 m, 2.3 m, 1.5 m), where the soil parameters are selected for each of these horizons. ISBA uniformly parameterizes the whole soil profile characteristics, as SISVAT does. The uniform parameterization within the soil profile is privileged here given the difficulties to both measure and calibrate the deep soil parameters. The various surface representations raise the question of finding a balance between the realism and the accuracy of the models representation in the sahelian land surface.

Few parameters are required for both the soil and the vegetation description within the standard version of ISBA and SISVAT (11 parameters). On the opposite, the SiSPAT model version requires about 45 parameters (measured or calibrated), mainly for the three soil horizons.

(b) Simulations Comparison

Globally, ISBA and SiSPAT provided satisfying results at all time steps in the same fallow savannah site during the same period (DOY 239-292) (Goutorbe et al., 1997b; Braud et al., 1997). The comparisons between the observed and the simulated values found using ISBA, SiSPAT and SISVAT are shown in table 4. Note that in practice in such sahelian area, both SiSPAT and SISVAT used quasi similar in-situ parameter dataset, while ISBA used tabulated parameters adapted from the USDA classification. Cumulated evapotranspiration is respectively underestimated by ISBA (- 8 %) and overestimated by SiSPAT (+ 12 %). The overestimation in SiSPAT may be due to an excess of transpiration as proposed by Goutorbe et al. (1997b). A similar comment can be made for the SISVAT model, even if the overestimation is lower (+ 9 %). The daily ET was simulated with the highest accuracy by SiSPAT (r² = 0.80, rmse = 0.43 mm). The SiSPAT scheme enabled to correctly reproduce the deep drainage (18.0 mm) when comparing it to the one observed (8.7 mm). All the schemes assume that the runoff is negligible. No gravitational drainage was estimated by ISBA in the version used, as the drainage module did not exist in 1995. The measured soil moisture changes (-15.0 mm) are between those simulated by SiSPAT (- 42.4 mm) and ISBA (7.2 mm). The SISVAT model is the most accurate with a better soil moisture change (-24.1 mm) and an accurate deep drainage (4.4 mm).

One of the main differences between the results found by the three SVATs lies in the internal repartition of the global ET between the soil evaporation and the vegetation transpiration. Indeed, both ISBA and SiSPAT simulated a significantly lower soil evaporation (40 mm, respectively 29 % and 24 % of the total ET) compared to the SISVAT model (60 mm, 37 % of the total ET). This may be due to the shielding factor in the SiSPAT study (close to 0.5) which severely reduces the incoming radiation in comparison with the SISVAT one


91

(equal to 0.4). In the ISBA study, the cover fraction is never superior to 48 %, as compared to 72 % retained in SISVAT. It may automatically reduce the transpiration rate. The impact of the vertical (attenuation factor) and horizontal (fraction cover) vegetation representation appears as rather significant in such sparse canopy, but this point cannot be tested given the lack of in situ measurements. In all the surface schemes, the plants transpiration represents however the major fraction of the total ET (63-76 %).

Globally, the SISVAT model performances are comparable to those of the reference SiSPAT model, except for the daily ET time evolution which is better estimated in SiSPAT (r² = 0.80) than in SISVAT (r² = 0.65). However, the total amount of evapotranspiration is similar in both two models. The ISBA scheme is tested in operational mode using tabulated parameters from the USDA classification, which can also explain its slightly lower performances as compared with SISVAT. 6. Conclusion

The performance of the SISVAT model is assessed in the sahelian zone of the HAPEX-Sahel. The validation of this surface scheme is an essential step for the future use of the MAR RCM in the sahelian zone. The various surface conditions (surface heterogeneity, high atmospheric demand, strong rainy events) met in this sahelian zone represent a serious and rigorous test for the model. Fallow savannah, composed of bare soil and mixed grass and bush strata, accurately represents such complexity. The SISVAT model correctly simulates the water budget on a fallow savannah area without any initial calibration. The diurnal cycle of each surface flux was also successfully simulated at 20-min temporal scale.

The focus made on the evapotranspiration is essential for two main reasons, as (i) these upward heat fluxes give feedback to the rainy regime, and (ii) they represent the major component of both the energy and the water budgets in such fallow savannah site. Time evolution is well reproduced at various time steps, from 20-min (r² = 0.92) to day (r² = 0.65). The total highlighted overflow (+ 9 %) is attributed firstly to the vegetation transpiration, secondly to the LAI overestimation and finally to the stress function within the transpiration module. The other surface schemes (ISBA, SiSPAT) found similar performances in the same fallow savannah site during the same validation period. The cover fraction used in ISBA model is relatively low, which reduces the transpiration rate and leads to a low total evapotranspiration estimation. In all cases, the plants transpiration represents the main component of the total ET (about 70 % of the total ET).

The results of the model can be improved on four main points in order to have a better surface representation. The first one would be to take into consideration the surface water redistribution (rainfall minus runoff). In our study, the horizontal water movement was not taken into account, even if the runoff phenomenon was not negligible in this climatic region

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where intense rains generate soil crusting. Coupling the SISVAT model to a hydrological model could improve the representation of the runoff processes. This will be a future step in the model's knowledge in order to improve its performances. This task is made in the SISVAT-MAR coupled system. The second weakness of the study is that the crust formation was not explicitly taken into account, all the more that it strongly controls both the runoff coefficient (Casenave and Valentin, 1992) and the exfiltration (Braud et al., 1997; Braud, 1998). The third issue which was not properly addressed in this study lies in the plants stress, which appears as mainly responsible for the transpiration overflow. In that case, improvement of the stress function representation appears as necessary. Finally, the fourth point lies in the cover fraction which remains constant, and tends to induce an overestimation of the total evapotranspiration during the wet season. This error can be put aside by inserting a time evolution of such value, which is however difficult to do given the existing program structure. Nevertheless, such an improvement may not be successful to obtain a better surface representation.

Finally, two further steps remain necessary in order to evaluate this land surface model. The first one is the model sensitivity to the parameter dataset. For instance, the model appears as very sensitive to specific parameters. Furthermore, the uncertainty of the input variables, such as the time evolution of the vegetation (Monteny, 1993), strongly influences the model. This step is necessary to both understand and improve the model behaviour under sahelian conditions. For instance, Braud (1998) reported a large influence of the soil parameters on the evapotranspiration process. This will be particularly true in our case by using the same soil properties in the whole soil profile. The second step is the validation of the model on the two other main types of vegetation in this sahelian area, i.e., the millet crop (22 % of the HAPEX-Sahel area) which represents the dominant cultivated crop, and tiger bush (28 %) which is the only vegetation in the plateaus. The evaluation on millet field is particularly crucial in order to quantify the impact of the anthropic pressure in the plains, which tends to reduce the crop rotation. Acknowledgements. O. Brasseur, C. Messager and W. Moufouma are particularly thanked for their discussion about the coupled SISVAT-MAR model. The authors would like to thank J.V. Demarais for the help given with regards to the numerical implementation of the SISVAT model.


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APPENDIX Description of the SISVAT model

The following paragraphs describe the equations solved, the internal variables and the symbols used in the SISVAT model. The symbols are also described in table 1. They are described in details by De Ridder (1997) or De Ridder and Schayes (1997). a. Description of the four-equations system based on the energy and the water conservation.

( ) ( ) ( ) ( )gggvggvvggvglgsg TG,T,TELT,THT,TRR +ψ+=+ (A1) ( ) ( )gggvgg W,T,TE0 ψ+ψ= (A2)

( ) ( ) ( ) ( )vvgvvvvgvvgvlvsv TST,,TELT,THT,TRR +ψ+=+ (A3) ( ) ( )vvsvvtr WT,,TE0 ψ+ψ= (A4)

b. Expression of the components included in the energy and water transfer equations.

Energy transfer 4

ggLglg TDR ×σ×ε−×ε= (A5) 00lglv UDRR −+= (A6)

( ) agagpag r/TTcH −××ρ= (A7) ( ) avavpav r/TTcH −××ρ= (A8)

( ) ( )11gg d2/1/TTG ×−×µ= (A9)

with ( ) 4343.0m1/81.3 −ψ−×=µ (A10)

( )t

TwwcS vlvwv

∂∂

×+×= (A11)

with dirvv Erain

dtdw

−×σ= and 1wl = kg.m-2 (A12)

Water transfer

( ) Kd2/1

KW w1

1sat1wg ×ρ+

×θ−θ

×θ∂ψ∂

×θ×ρ= (A13)

( )∑

+ψ−+ψ

×ϕ×ρ∑ ====

7

1i

7

1i sip

iviwviv

rrdWW (A14)

with ( )ip K/m0001.0r θ= (A15) c. Expression of the surface resistances

vahag /rr σ= (A16) ( )vahav 1/rr σ−= (A17)

( )vst0

c FLerr ψ×= (A18)

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Braud, I, A.C. Dantas-Antonino, M. Vauclin, J.L. Thony, and P. Ruelle, 1995: A Simple Soil Plant Atmosphere Transfer model (SiSPAT), Development and field verification. J. Hydrol., 166, 213-250.

Braud, I, P. Bessemoulin, B. Monteny, M. Sicot, J.-P. Vandervaere, and M. Vauclin, 1997: Unidimensional modeling of a fallow savannah during the HAPEX-Sahel experiment using the SiSPAT model. J. Hydrol., 188-189, 912-945.

Braud, I, 1998: Spatial variability of surface properties and estimation of surface fluxes of a savannah. Agr. For. Meteo., 89, 15-44.

Braud, I., 2000: SiSPAT users’s manual, version 3.0., available from LTHE (BP53, 38041 Grenoble cédex 9, France or at http://www.lthe.hmg.inpg.fr), 106 pp.

Brooks, R.H., and C.T. Corey, 1964. Hydraulic properties of porous media. Hydrology paper 3. Colorado State University, Fort Collins.

Casenave, A., and C. Valentin, 1992. A runoff capability classification system based on surface feastures criteria in semi-arid areas of West Africa. Journal of Hydrology, 130, 231-249.

Charney, J.G., 1975. The dynamics of deserts and droughts. Quart. J. Roy. Meteor. Soc., 101, 193-202.

Clapp, R.B., and G.M. Hornberger, 1978: Empirical equations for some soil hydraulic properties. Water Resour. Res., 14, 601-604.

CLIVAR, 1999: Climate Research for Africa. WRCP Informal Report No. 16/1999. ICPO Publication Series No.29.

Daamen, C.C., 1993: Evaporation from sandy soil beneath crops in the semi-arid zone : a study of the use of microlysimeters and numerical simulation. PhD Thesis, Department of Soil Science, University of Reading.

De Ridder, K., 1997: Land surface processes and the potential for regional climate change in semiarid regions. PhD Thesis, University of Louvain (Belgique), 120 pp.

De Ridder, K., 1998: The impact of vegetation cover on sahelian drought persistence. Bound-Layer Meteor., 88, 307-321.

De Ridder, K., and G. Schayes, 1997: The IAGL land surface model. J. Appl. Meteor., 36, 167-182.

De Ridder, K., and H.Gallée, 1998: Land surface-induced climate change in southern Israel. J. Appl. Meteor., 37, 1470-1485.

D'Herbès, J.M., and C. Valentin, 1997: Land surface of the Niamey region : ecological and hydrological implications. J. Hydrol., 188-189, 18-42.


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Dooge, J., M. Bruen, and A. Dowley, 1993: Final report on EU funded (PL890016 EPOCH) project on spatial variability of land surface processes : SLAP. Center for Water Resources Research, University College, Dublin.

Estèves, M., X., Faucher, S., Galle, and M. Vauclin, 2000. Overland flow and infiltration modelling for small plots during unsteady rain : numerical results versus observed values. J. Hydrol., 228, 265-282.

Gallée, H., and G. Schayes, 1994: Development of a three-dimensional meso-gamma primitive equations model, katabatic winds simulation in the area of Terra Nova Bay, Antarctica. Mon. Wea. Rev., 122, 671-685.

Gallée, H., 1995: Simulation of the mesocyclonic activity in the Ross Sea, Antarctica. Mon. Wea. Rev., 123, 2051-2069.

Gallée, H., G. Guyomarc'h, and E. Brun, 2001: Impact of snow drift on the Antarctic ice sheet surface mass balance : possible sensitivity to snow-surface properties. Bound-Layer Meteor., 99, 1-19.

Gaze, S.R., L.P. Simmonds, J. Brouwer, and J. Bouma, 1997: Measurement of surface redistribution of rainfall and modeling its effect on water balance calculations for a millet field on sandy soil in Niger. J. Hydrol., 188-189, 267-284.

Goutorbe, J.P., T. Lebel, A.J. Dolman, J.H.C. Gash, P. Kabat, Y.H. Kerr, B. Monteny, S.D. Prince, J.N.M. Stricker, A. Tinga, and J.S. Wallace, 1997a: An overview of HAPEX-Sahel : a study in climate and desertification. J. Hydrol., 188-189, 4-17.

Goutorbe, J.P., J. Noilhan, P. Lacarrere, and I. Braud, 1997b: Modeling of the atmospheric column over the Central sites during HAPEX-Sahel. J. Hydrol., 188-189, 1017-1039.

Hanan, N.P., and S.D. Prince, 1997: Stomatal conductance of West-Central Supersite vegetation in HAPEX-Sahel: measurements and empirical models. J. Hydrol., 188-189, 536-562.

Koster, R.D., and M.J. Suarez, 1992: A comparative analysis of two land-surface heterogeneity representations, J. Climate, 5, 1379-1390.

Le Barbe, L., and T. Lebel, 1997: Rainfall climatology of the HAPEX-Sahel region during the years 1950-1990. J. Hydrol., 188-189, 43-73.

Lefebre, F., H. Gallée, J.P. Van Ypersele, and W. Grevell, 2002: Modeling of snow and ice melt at ETH_Camp (West Greenland) : a study of surface albedo. J. Geophys. Res., accepted.

Lloyd, C.R., P. Bessemoulin, F.D. Cropley, A.D. Culf, A.J. Dolman, J. Elbers, B. Heusinkveld, J.B. Moncrieff, B. Monteny, and A. Verhoef, 1997: A comparison of surface fluxes at the HAPEX-Sahel fallow bush sites. J. Hydrol., 188-189, 400-425.

McCumber, M.C., and R.A. Pielke, 1981: Simulation of the effects of surface fluxes of heat and moisture in a mesoscale numerical model. Part I : soil layer. J. Geophys. Res., 86, 9929-9983.

Monteny, B.A., 1993: HAPEX-Sahel 1992, SuperSite Central Est, campagne de mesure. Report available on request at IRD, Montpellier, 230 pp.

Nagumo, F., 1993: Pedological Environment and Agro-ecological system of the Sudano-Sahelien zone, in Niger, West Africa. PhD Thesis, Hokkaido University / Orstom /

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Jica, Department of Environmental Structure Laboratory of Fundamental Research, 101 pp.

Noilhan, J., and S. Planton, 1989: A simple parameterization of land-surface processes for meteorological models. Mon. Wea. Rev., 117, 536-549.

Passerat de Silans, A., B. Monteny, and J.P. Lhomme, 1996: Tentative de spatialisation des paramètres d'un modèle SVAT. Application au bassin de Banizoumbou - Niger. Interactions surface continentale / atmosphere : l'experience HAPEX-Sahel, Proceedings of the Montpellier workshop (September 1994), 319-333.

Peugeot, C., 1995: Influence de l'encroûtement superficiel du sol sur le fonctionnement hydrologique d'un versant sahélien (Niger) - Expérimentations in situ et modélisation. PhD Thesis, LTHE Grenoble (France), 305 pp.

Séguis, L., B., Cappelaere, C., Peugeot, and B. Vieux, 2002. Impact on Sahelian runoff of stochastic and elevation–induced spatial distributions of soil parameters. Hydrological processes, 16, 313-332.

Sivakumar, M.V.K., 1987. Climate of Niamey. Progress Report-1, ICRISAT Sahelian Center, Niamey, Niger. International Crops Research Institute for the Semi-arid Tropics, 36 pp.

Taylor, C.M., F. Said, and T. Lebel, 1997a: Interactions between the land surface and mesoscale rainfall variability during HAPEX-Sahel. Mon. Wea. Rev., 125, 2211-2227.

Taylor, C.M., R.J. Harding, A.J. Thorpe, and P. Bessemoulin, 1997b: A mesoscale simulation of land surface heterogeneity from HAPEX-Sahel. J. Hydrol., 188-189, 1040-1066.

Taylor, C.M., and T. Lebel, 1998: Observational evidence of persistent convective-scale rainfall patterns. Mon. Wea. Rev., 126, 1597-1607.

Taylor, C.M., and D.B. Clark, 2001: The diurnal cycle and African easterly waves: a land surface perspective. Quart. J. Roy. Meteor. Soc., 127, 845-867.

Tuzet, A., J.F. Castell, A. Perrier A., and O. Zurfluh O., 1994: Caractérisation et modélisation des échanges de masse et d'énergie au niveau des couverts épars. Comptes-rendus des Xème journées hydrologiques de l'Orstom, Montpellier, 13-14 Septembre 1994, 12 pp.

Vandervaere, J.P., C. Peugeot, M. Vauclin, R.A. Angulo Jaramillo, and T. Lebel, 1997: Estimating hydraulic conductivity of crusted soils using disc infiltrometers and minitensiometers. J. Hydrol., 188-189, 203-223.

Vizy, E.K., and K.H. Cook, 2002: Development and application of a mesoscale climate model for the tropics: influence of sea surface temperature anomalies on the West African Monsoon. J. Geophys. Res., 107, (D3), ACL 2.

Wang, G., and E.A.B Eltahir, 2000: Modeling the biosphere-atmosphere system: the impact of the subgrid variability in rainfall interception. J. Climate, 13, 2887-2899.

Troisième Chapitre

Evaluation du schéma de surface SISVAT en zone sahélienne :

Sensibilité du modèle sur une culture de mil

SENSIBILITE DE SISVAT SUR LE MIL

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CHAPITRE 3 EVALUATION DU SCHEMA DE SURFACE SISVAT : SENSIBILITE DU MODELE SUR UNE CULTURE DE MIL Résumé du Chapitre

Contexte de l'Etude

Le chapitre précédent a permis d'évaluer les performances du schéma de surface SISVAT sur une parcelle de jachère qui représente la végétation majoritaire (39 %) au sein de la zone d'étude d'HAPEX-Sahel en 1992. Les bilans hydrique et énergétique sont globalement bien estimés. Les flux de chaleur latente, qui nous intéressent en premier lieu, sont bien reproduits (r² = 0.92), même si la simulation a révélé une surestimation de l'ordre de 9 % sur la quantité d'évapotranspiration cumulée. Cela est en partie dû à la représentation de surface mise en place, qui a contribué à cet excès d'évapotranspiration à travers un surplus de transpiration. Cette première étape, bien que nécessaire, n'est cependant pas suffisante pour évaluer ce modèle. Il a par exemple été impossible de tester individuellement et séparément les modules d'évaporation du sol et de transpiration des plantes au sein de l'évapotranspiration globale. Ceci est pourtant crucial pour comprendre l'impact d'une évolution du milieu. De plus, il est nécessaire de quantifier l'importance relative des paramètres du modèle. Dans le présent chapitre, une étude de sensibilité de SISVAT a été produite sur une culture de mil bien documentée.

Cette étude a été produite sur une zone de mil de quelques hectares. La période de validation (jours 202-263) regroupe des conditions de surface contrastées sur près de deux mois en saison des pluies. Durant la première sous-période (période n°1, jours 202-215), la surface est entièrement constituée de sol nu, ce qui permet d'évaluer isolement le module sol de notre modèle. Durant la seconde période (période n°2, jours 223-263), une saison culturale quasi complète peut être suivie. Dans un premier temps, les performances du modèle sont quantifiées sur les deux périodes. Sa représentation de surface est différente de celle de la jachère : zone de sol nu plus importante, LAI plus élevé, limitation des croûtes par des sarclages. Dans un second temps, la sensibilité du modèle aux 11 paramètres est analysée sur la période de sol nu (5 paramètres du sol) et la période de végétation (6 paramètres de végétation).

Cette nouvelle étape d'évaluation du modèle sur le mil est également intéressante compte tenu de trois autres points particuliers. Tout d'abord, peu de modélisations de type SVAT ont été menées sur une zone culturale de mil, tandis que de nombreuses études ont été conduites sur la jachère. Ensuite, les cultures de mil représentent la seule ressource céréalière

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pour les habitants de cette région sahélienne. Par exemple en 1992, les champs de mil ont représenté 22 % de la superficie totale d'HAPEX-Sahel. Les cultures de mil sont associées à de la jachère à l'échelle inter-annuelle, ce qui permet au sol de se reposer. Le complexe cultural couvre 61 % de la surface totale. Progressivement du fait de la forte pression anthropique, la période de rotation est réduite et la surface totale du complexe cultural augmente au dépend des surfaces naturelles de versant. L'impact de cette modification de couvert végétal doit être quantifié. Après la jachère, l'évaluation du modèle sur une culture de mil est donc indispensable.


L'évaluation du schéma de surface SISVAT sur une culture de mil permet d'observer le comportement du modèle sur les deux périodes de validation. L'évapotranspiration a été bien simulée sur les deux périodes. Sur la période de sol nu, le modèle réagit très correctement avec une évaporation du sol nu très bien simulée (+3 %). Sur la période de végétation, la surestimation s'est avérée plus importante (+6 %). Sur l'ensemble de la période d'évaluation, les flux de chaleur latente ont été relativement bien reproduits au pas de temps de 20 minutes de la simulation (r² = 0.80). Au pas de temps journalier, le comportement du modèle est particulièrement bon sur le sol nu (r² = 0.75), alors que les résultats sont beaucoup moins bons sur le cycle de culture (r² = 0.41).

L'étude de sensibilité a été réalisée sur les deux sous-périodes. Pour la période n°1 où le sol est complètement nu, le test s'est focalisé sur les paramètres du sol. L'évaporation du sol est particulièrement sensible à la conductivité à saturation (Ksat), ce qui a un impact important puisque ce paramètre varie facilement d'un ordre de grandeur. L'albédo (α) ainsi que le paramètre de forme (b) sont les deux autres principaux paramètres influents.

Pour la période n°2, la résistance stomatique minimale (r0) est le paramètre le plus

sensible. Le LAI est une variable qui joue beaucoup, ce qui est important puisqu’on peut observer des variations du LAI allant de 1 à 4 entre deux champs suivant les pratiques culturales (engrais, travail du sol). De plus au niveau même de la mesure il existe une incertitude importante (15 à 40%). La fraction de couverture végétale joue directement sur la répartition entre l'évaporation du sol et la transpiration des plantes. Les variations des deux termes se compensent presque et l'évapotranspiration varie donc faiblement avec la couverture végétale : une augmentation de la couverture végétale entraîne une augmentation de la transpiration supérieure à la diminution de l’évaporation.


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EVALUATION OF THE SISVAT LAND SURFACE MODEL IN THE SAHEL: MODEL SENSITIVITY ON THE MILLET CROP

Authors: G. Derive(a)*, H. Gallee(b), S. Galle(a), I. Braud(c)

(a) Laboratoire d'Etude des Tranferts en Hydrologie et Environnement

LTHE (CNRS UMR 5564), BP 53, 38041 Grenoble Cedex 9, France (b) Laboratoire de Glaciologie et Géophysique de l'Environnement,

LGGE, Grenoble, France (c) CEntre national du Machinisme Agricole, du Génie Rural, des Eaux et des Forêts,

CEMAGREF, Lyon, France

In review to Journal of Applied Meteorology

* Corresponding author. Fax: (+33)-4-76-82-52-86. E-mail address: [email protected] (G. Derive)

ABSTRACT

A precise knowledge of the rainfall regime is required to understand the hydrological impact of the climate change. In that perspective, a RCM (Regional Climate Model) called MAR (Modèle Atmosphérique Régional) is used to predict both the spatio-temporal evolution of the atmosphere and the precipitation in West Africa. This mesoscale atmospheric model is coupled to a land surface scheme named SISVAT (Soil-Ice-Snow-Vegetation-Atmosphere Transfer). This vertical 1-D model couples the heat and the mass transfers within the Soil-Plant-Atmosphere continuum. The surface processes must be accurately simulated in this region, given the major feedback of such surface conditions to the rainfall regime. The objective of this study is to assess the model’s performance on the main crop of the region and to evaluate its sensitivity to both the parameters and the variables. The stress is made on the evaporative fluxes, keeping in mind that the upward surface fluxes make feedback to the atmosphere

The HAPEX-Sahel experiment (Hydrology Atmospheric Pilot EXperiment in the Sahel) is

one of the most documented zones in Africa. The first part of this paper details the model’s behaviour on the millet crop, which represents the dominant cultivated crop in the Sahel. Few equivalent SVAT studies are available on this type of vegetation. The validation period lasts 56 days, which represents the various surface conditions. Both the energy and the water budgets were well reconstituted by the model. The focus is made on the latent heat flux, which forces the atmospheric model. The 20-min time fluctuations are well reproduced (r² > 0.80). The overestimation remains inferior to 6 %, which is close to the results found on fallow savannah.

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The second part of this study describes the model’s sensitivity to the parameter set. The results are discussed by focusing on the evaporative components (total, soil, plant) under various surface conditions (bare soil, growing period). The soil evaporation is mainly governed by the hydraulic conductivity at saturation, while the minimal stomatal resistance monitors the transpiration process. The sensitivity test is also discussed by describing the constant land cover fraction and time evolution of the vegetation characteristics. Keywords: Latent heat flux, Millet crop, SISVAT model, Model evaluation, Sensitivity test, HAPEX Sahel, Niger.

1. Introduction The validation study of the SISVAT model (Soil-Ice-Snow-Vegetation-Atmosphere

Transfer) (De Ridder, 1997; Gallée et al., 2001; Lefebre et al., 2002) appears as essential in Western Africa (Taylor and Clark, 2001). Indeed, such surface scheme represents the surface conditions of a mesoscale RCM (Regional Climatic Model), called MAR (Regional Atmospheric Model) (Gallée and Schayes, 1994; Gallée, 1995). This coupled model is devoted to run on this whole semi-arid region to reproduce the monsoon regime. As the surface processes affect the general rainfall regime, the evaluation of the SISVAT model is essential in this climatic zone. The focus must be made on the upward evaporative rates for two main reasons. Firstly, the total evapotranspiration represents the dominant component of the surface annual water budget in the Sahel. Indeed, the annual evapotranspiration is superior to 75 % of the rainfall for all the vegetation types, and especially for the millet crop (Wallace, 1991; Peugeot, 1995; Gash et al., 1997). Secondly, it was previously suggested that the water vapor coming from the surface evaporation within the atmospheric boundary layer, is responsible for a feedback to the precipitation (Taylor et al., 1997ab; De Ridder, 1998; Taylor and Lebel, 1998).

The SISVAT behaviour was previously analyzed on fallow savannah using the HAPEX-Sahel data set (Derive et al., subm.). Fallow savannah represents the dominant vegetation type in the Sahel (39 % of the total HAPEX-Sahel area). The results highlighted a significant evaporative overestimation (9 %), which is mainly due to the transpiration process. The influence of the surface representation was also emphasized by comparing these results to the ones of other SVAT schemes (ISBA, SiSPAT). The model’s behaviour was difficult to analyze any further as it wasn’t possible to validate separately the soil evaporation and the vegetation transpiration. Indeed, their relative contributions are associated and cumulated within the total evapotranspiration, which makes the sensitivity analysis hazardous.

This model’s sensitivity is now considered on the millet crop. Indeed, the available validation period for the millet crop lasts about 2-months (Days Of Year 202-215 and 223-263) and presents various surface conditions, including (i) a totally bare soil surface (DOY


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202-215) before the appearance of the leaves which enables to isolate the model response to the soil parameters and to monitor the soil processes, and (ii) a quasi-complete growing season (DOY 223-263) which activates the vegetation parameters. The first part of this paper deals with the model evaluation, while the second one analyzes the model sensitivity to the input parameterization of both the soil and the vegetation. The model’s sensitivity to the vegetation variables is also discussed, given their observed strong local variability. The contribution of both the soil evaporation and the vegetation transpiration is also determinated. Indeed, the understanding of their respective roles is one of the main requirements in this semi-arid environment (Wallace et al., 1993). No in situ measurement is however available in the data set to validate this internal partition.

Three other issues appear as interesting in the case of the millet crop. First, very few models were applied to the millet crop in this region (Daamen et al.,1995; Daamen, 1997; Boegh et al., 1999), whereas several studies were made on fallow savannah (Braud et al., 1997; Goutorbe et al., 1997b; Monteny et al., 1997; Tuzet et al., 1997; Braud, 1998; Derive et al., subm.). The SISVAT evaluation will be the first model including a coupled mass-energy balance applied under the millet crop conditions. Secondly, the millet crop represents the quasi-exclusive cultivated cereal in Niger. The population depends on the millet yield to have food. The cultivated lands have doubled in area during the last 20 years (Loireau, 1998). Therefore, its evapotranspiration fluxes must be accurately reproduced. The cultivated millet crop is associated to fallow savannah, which enables the restoration of the soil fertility. The crop rotation tends to seriously slow down because of a strong local anthropic pressure. The impact of this land cover change on the hydrologic cycle needs to be quantified. After the validation made on fallow savannah, the validation on the millet crop appears therefore as a natural step to quantify the influence of this land surface modification in a near future. 2. Materials and Methods

2.1 Site Description

The study area is located in the HAPEX-Sahel zone, in southern Niger (2-3° East, 13-14° North) (Goutorbe et al., 1997a). The vegetation consists on one side in natural forests called tiger bush (shrub bush in banded pattern) on the lateritic plateaus (28 % of the area) and on the other side in a patchwork of millet field and fallow savannah in the smooth sandy valley (61 % of the area). The remaining 11 % of the valley consists in degraded hillslope, characterized by few vegetation and crusted bare soils. A complete surface description is detailed by D'Herbes and Valentin (1997).

In the present study, the millet field is considered. In 1992, the millet fields covered 22 % of the total HAPEX-Sahel area, while fallow savannah represented 39 % (D'Herbes and Valentin, 1997). The considered experimental site (13°32.56' North, 2°40.50' East) is situated

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in the ECSS (Eastern Central SuperSite). It is covered by loamy sand and has a gentle slope (2%). The experimental millet site is located 2 km from the Banizoumbou village (13°31.93' North, 2°39.64' East).

2.2 Experimental Data Description

A micrometeorological station was locally installed by a french IRD team to study the millet crop. The flux measurements are made on several hectares. The net radiation was taken at 11.5 m high, while the sensible and latent heat fluxes were obtained at 8 m high using the Bowen ratio method. On its side, the ground heat flux was measured at 2 cm deep. The split into soil and vegetation contributions is however not available. All these fluxes are available at a 20-min time step.

Rainfall was locally measured using an automatic rain-gauge. The rainfall data are

available at a 5-min time step. The air humidity and the temperature were measured at 2 m high, using a Vaisala HMP35AC. Its absolute uncertainty was of 0.2°K. The windspeed was measured at 10 m high, using a vector anemometer. Its estimated absolute uncertainty is nearly 0.2 m.s-1. The incoming downward solar radiation was measured in the climatic station of Banizoumbou, about 2 km from the millet site. The measurement was made using both a Kipp and Zonen Pyranometer, at 2 m above the surface level. The absolute uncertainty was 7 W.m-2. The incoming long-wave radiation was measured in the Southern SuperSite (SSS), situated about 60 km from the study site.

The millet crop (Pennisetum glaucum) is sown in pockets (6-12 plants) about 1 meter apart

from one another (DOY 182). In the studied millet site, the maximum height equaled 2.2 m at the maximum of the growing season. The maximum plant cover reached 56 %. This latter value is retained to spatially split the two patches contribution (bare soil, millet plant). Note that most of the time, the bare soil areas represent more than half of the whole field superficy. Time evolution of the LAI (total, green) was locally monitored by B. Monteny (1993) (figure 1). The LAIs were deduced from the biomass measurements using a statistic linear interpolation. The total LAI reaches 4 m2.m-2. The green fraction represents about 70 % of the total leaves all over the year. Both the total and the green LAIs vary during the simulation.

The validation period (DOY 202-263) takes place during the core of the rainy season (August-September), and ends just after the last significant rainfall of the season (DOY 259). It can be divided into two sub-periods. The first period represents the bare soil conditions before the appearance of the millet plants (period n°1, DOY 202-215, 14 days). This bare soil period will enable to precisely observe the soil module reaction (behavior, sensitivity). The second period takes place during the growth of the millet crop (period n°2, DOY 223-263, 41 days), which enables to observe the vegetation module reaction (behavior, sensitivity).


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Figure 1 Time evolution of the total (g) and the green (n) leaf area index (LAI) of the millet. The millet is sown on DOY 182 and harvested 100 days later. The validation period can be divided into two periods : the first period (14 days) when only the bare soil exists (Days Of Year 202-215), and the second period (41 days) when the millet grows (DOY 223-263). (after Monteny, 1993)

2.3 Parameter Set Identification

Ten parameters must be defined in the SISVAT model, respectively five for the soil module (αgd, θsat, ψsat, Ksat, b) and five for the plant module (d, zom, ϕ0.1m, r0, rp). The set of parameters retained and the associated reference sources are described in table 1.

The soil is loamy sand (88 % sand, 5 % clay) (Peugeot, 1995). The associated soil

parameters (αgd = 0.27, θsat = 0.3 m3.m-3, ψsat = -0.1 m, Ksat = 5.4 10-5 m.s-1, b = 1.5) generally characterize more the soil of the plains. For instance, the same data set is applied to fallow savannah (Derive et al., subm.). The studies made on the millet fields, located at less than 100 km from this area, measured or used similar soil characteristics (Daamen, 1993; Allen et al., 1994). The saturated water moisture content (θsat = 0.3 m3.m-3) was measured on a millet field by Daamen (1993) and applied by Gaze et al. (1997). Vandervaere (1995) also measured a similar value on a nearby millet field (0.31). In this study, a value of 5.4 10-5 m.s-1 is retained for the soil conductivity at saturation (Vandervaere et al., 1997). This latter value is very close to the value measured on other millet fields, such as 5.7.10-5 m.s-1 (Rockström, 1997) or 6.8 10-5 m.s-1 (Gaze et al., 1997), although variations of several magnitudes can be noticed at some decimeters distance. The influence of this parameter value will be discussed. The water

0

1

2

3

4

200 220 240 260 280 300DAYS OF YEAR (1992)

LE

AF

AR

EA

IND

EX

(m²/m

²)

period n°1

period n°2

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retention curve exponent (1.5) is very close to the one (1.4) reported by Gaze et al. (1997) in another millet field.

Table 1 The soil and the vegetation parameters, and the related references.

Values Sources

Dry soil albedo (αgd, -) 0.27 Goutorbe et al. (1997b)

Saturated water content (θsat, m3.m-3) 0.3 Braud et al. (1997)

Water potential at saturation (ψsat, m) - 0.1 Gaze et al. (1997)

Hydraulic conductivity at saturation (Ksat, m.s-1) 5.4 10-5 Vandervare et al. (1997)

Water retention curve exponent (b, no unit) 1.5 Braud et al. (1997)

Displacement height (d, m) 0.2 Rockström (1997)

Roughness length for momentum (zom, m) 0.1 Rockström (1997)

Root fraction upper 0.1 m (ϕ0.1m, -) 0.25 Rockström (1997)

Minimum stomatal resistance (r0, s.m-1) 125 Hanan and Prince (1997)

Internal plant resistance (rp, s) 9.4.108 Braud et al. (1997)

The vegetation parameters identified for that millet field (d = 0.2 m, zom = 0.1 m, ϕ0.1m = 0.25) were suggested by Rockström (1997). The stomatal resistance (125 s.m-1) was measured by Hanan and Prince (1997). The internal plants resistance is the one reported by Braud et al. (1997) for fallow savannah. No in situ measurement is available yet for the latter parameter.

The cover fraction is the last vegetation parameter of the SISVAT model. It fixes the relative contribution of each patch composing the millet field i.e. the millet and the bare soil patch. An increase of that parameter directly reinforces the vegetation’s contribution (the transpiration and the underneath soil evaporation). A special attention must be made to test its influence on both the evaporation and the transpiration amounts at field scale. 3. Simulations Results and Discussion

3.1 Simulated Surface Water and Energy Balance

Table 2 summarizes the comparison between the observed and the modeled variables (mean energy value, cumulated water budget). The results are separated between the bare soil period (n°1), and the vegetation growth period (n°2).


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Table 2 The in situ observations and the SISVAT simulations for both the energy (W.m-2) and the water (mm) budgets during the first period (Days Of Year 202-215), characterizing the bare soil conditions and the second period (DOY 223-263), characterizing the millet growth.

Period n°1 Period n°2 Observed SISVAT Observed SISVAT

Energy budget

Rn 104 115 120 121

LE 64 66 81 86

H 31 55 37 39

G 5 -6 2 -4

Water budget

Rainfall 79.9 249.7

ET 31.0 32.1 114.8 121.3

Soil evaporation 31.0 32.1 - 59.2

Vegetation transpiration 0 0 - 62.1

Drainage (< 2 m) 52.1 42.6 46.6 125.3

Soil water change (0-2 m) - 3.2 5.2 88.3 3.1

The scatterograms of the simulated versus the observed energy fluxes (20 min time step) are described in figure 2 for the whole period. Their associated regression curve coefficients are given in table 3.

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Figure 2 The scatterograms of the simulated versus the observed 20-min values for (a) the net radiation, (b) the latent heat flux, (c) the sensible heat flux, and (d) the ground heat flux, during the whole period. The associated regression coefficients are summarized in table 3.

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Table 3 The characteristics of the regression curves (r², rmse, slope, intercept) for the surface energy components and the daily ET. Two periods are considered for the daily ET: period n°1 without vegetation (DOY 202-215) and period n°2 with the millet plants (DOY 223-263). Both the rmse and the intercept value are expressed in W.m-2 for the energy terms and in mm for the daily ET.

r² rmse slope intercept

Rn 0.94 58.1 0.97 6.8

LE 0.80 51.2 0.78 20.6

H 0.51 53.8 0.78 15.7

G 0.75 31.8 0.89 -7.0

Daily ET 0.57 0.64 0.72 0.89

- period 1 0.75 0.57 0.75 0.63

- period 2 0.41 0.66 0.63 1.20

(a) Energy Budget

The energy budget components are correctly predicted by the model, especially for the latent heat flux. The mean latent heat flux is slightly better reconstituted during the bare soil period (+ 3 %) than during the growing period (+ 6 %). A significant overestimation of the net radiation of + 11 % is highlighted during the bare soil period, which is relatively high for this variable. This behaviour can be explained by the 60-km distance between the long-wave measurements location and the net radiation simulation. The cloud cover is certainly not synchronous from one site to another for all the variables. However, correct results were found on the fallow savannah site, about 2 km from the millet site. The simulated ground heat flux is negative whereas a positive flux was deduced from observations. In both cases, the ground heat flux remains low and of minor contribution (5 %) to the water budget.

The dispersion of the scatter-diagram remains relatively low, except for the sensible heat

flux (r² = 0.51). This flux is mainly overestimated during the bare soil period. The diurnal cycle of the latent heat flux is better reproduced during the growing period (r² = 0.82) than under bare soil conditions (r² = 0.69). Finally, the simulated and the observed values are well correlated during the whole validation period (r² = 0.80) (table 3). An underestimation is noticed for the highest values. On the opposite, the lowest simulated values are never inferior to -10 W.m-2 during the growing period. For the whole variables, the slope of the regression ranges from 0.78 to 0.97, with a relative low intercept value, except for LE.


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(b) Water Budget

The cumulated EvapoTranspiration (ET) is particularly well estimated during the first period (+ 3 %). Its relative error is of about 6 % during the growing period. Both the simulated and the observed ET were aggregated from 20-min to 1-day. The scatterogram between the simulated and the observed daily values is shown in figure 3. The daily ET is well reproduced by the model under bare soil conditions (r² = 0.75) for a large range of ET variations, while it is strongly inferior during the plant growing season (r² = 0.41). The global r² coefficient is equal to 0.57 for the complete period, with a relatively stable rmse for the three periods considered (0.57, 0.66 and 0.64 mm).

Figure 3 The scatterogram of the simulated versus the observed daily ET for the first period when only the bare soil is present (n) and the second period when the millet grows ( ).The characteristics of the regression curve are given in table 3.

The evolutions of the daily ET and its internal components are shown in figure 4 The simulated repartition between the soil evaporation and the vegetation transpiration is described even if no in situ measurements are available to validate it. In our study, the simulated evaporation of the intercepted water is negligible given the low LAI values. The daily bare soil evaporation during period n°1 ranges from 3.9 mm (the day following the rainy event) to 0.9 (few days after the rainy event). The ET rate is systematically underestimated just after the rainy event. Note that three strong rainy events (mean 30 mm) take place during that period. During the second period, the mean daily value is close to 1.5 mm for the two components (soil, vegetation). The soil evaporation rate is high just after the rainy events, but

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never superior to 2.2 mm. This diminution is due to the plant radiative attenuation, which strongly slows down the evaporative potential of the soil. The vegetation transpiration remains at the same rate between the rainy events. At the maximum of the growing season (DOY 248), the transpiration becomes superior to the soil evaporation some days after the rainy events. On specific days (for instance the DOY 233-234), the transpiration fraction can be 3-4 times greater than the soil evaporation. The total soil evaporation and the total plant transpiration are of same magnitude during period n°2. The soil evaporation represents about 60 % of the ET during the cumulated two periods. It is composed of both the evaporation of the bare soil (about 80 %) and the evaporation under the vegetation cover (20 %). The soil moisture change in the 0-2 m layer and the drainage below 2m are accurately predicted during the first period, while a significant drainage excess appears during the second phase. The drainage excess (+79 mm) is compensated by the soil water storage in the 0-2m layer (-85 mm) making the total infiltration of the second period globally correct. But a wrong repartition along the soil profile can be noticed.

Figure 4 Time evolution of the daily simulated ( ) and the measured ET (♦) split into the soil evaporation (n) and the vegetation transpiration ( ). The vertical line indicates the last rainy event of the year (DOY 259).

3.2 Discussion

One of the aims of this study is to test the SISVAT model under various surface conditions in a millet field. The diurnal energy cycle of the latent heat flux is globally correctly

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reproduced (r² = 0.80). The cumulated evapotranspiration is well simulated, with a relative error inferior to 6 %.

The total evapotranspiration is well reproduced during the two periods. However, a

significant overestimation is highlighted during the growing season. Given the reliability of the soil evaporation simulation during the first phase, a transpiration overprediction can be suspected. This trend was also highlighted on fallow savannah (Derive et al., subm.). The focus is made on the cultivated crop specific processes. In the Sahel, the convective rains fall with a high intensity and lead to soil surface crusting. The soil crusts limit the infiltration rate (Casenave and Valentin, 1992) and therefore the ET rate. Two weedings are made in the millet field during the millet crop season, in order to eliminate the weed and to reduce the crust appearance. In 1992, they were made (DOY 220, 254) one day before the strong rainy events (respectively 60 mm and 30 mm), which tends to considerably limit their efficiency. Indeed, the impact of the weeding disappears after a cumulated rainfall of about 80 mm (Peugeot, 1995). This is why the bare soil evaporation cannot be correctly reproduced by using a one and only single soil hydraulic conductivity during the whole period. If the conductivity appears as well adapted during the first period, it is not the case after the weeding. Moreover the soil crusting implies a vertical soil heterogeneity, which is not taken into account in the SISVAT scheme. On the other hand, in the millet field, a low runoff appears (11-13 % of the annual rainfall), as directly observed by Peugeot (1995). Therefore, in that case, our unidimensional representation overestimates both the infiltration and the coming evaporation, although not that much on fallow savannah (25%).

Another specificity of the observation period process lies in the fact that almost no hydric stress appears during the core of the rainy season, when the rain falls every 2-3 days. Therefore in our case, the millet crop is correctly water supplied. The simulated overestimation may not be due to the hydric stress representation.

The cover fraction is a constant of the model (56 %). This assumption tends to increase the vegetated area and then to globally increase the total ET. This is particularly true during the beginning of the second period (between DOY 223-233), when the transpiration may be highly overestimated. For instance, the current cover fraction is lower than half of the maximum value, before the DOY 233, i.e., around ten days during the validation period. The evapotranspiration is overestimated during that sub-period (figure 4).

One of our aims is to evaluate the ability of the model to accurately simulate a vegetation evaporation but also to reproduce the relative differences between the two vegetation covers. The millet crop and fallow savannah were monitored during a 45 days common period (DOY 239-263), representing the maximum vegetated period for the two vegetation types. The results found on fallow savannah are detailed in Derive et al. (subm.). During that period, the observed cumulated ET was of 8 % less in the millet crop (78 mm) than in the fallow savannah (85 mm). This result is correctly reproduced by the SISVAT model (Derive et al.,

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subm.) with respectively 80 and 85 mm. The soil evaporation is the dominant component of both millet (56 mm, 70 % of the ET) and fallow savannah (48 mm, 57 % of the ET). The soil evaporation is more significant in the millet field given its strong bare soil area. The evaporative difference between these two types of vegetation is mainly due to the stronger vegetation transpiration of fallow savannah (37 mm, 43 % of total the ET) during that period, which compensates its lower soil evaporation. The transpiration is more significant in fallow savannah given the high land cover fraction. One of the questions is to know if the studied millet field is representative of the sahelian millet crop. Indeed, its LAI appears as relatively high as compared with other authors results. For instance, Rockström (1997) observed a similar seasonal time evolution, but a lower LAI, which never exceeds 0.89 m².m-² even with fertilizers. This local high millet LAI can be explained by the good local manure and is correlated to an important crop yield (1360 kg.ha-1) in comparison with other sahelian fields where the yield is 4-10 time lower. The total ET estimated here can be considered as the superior limit of the millet ET, which strengthens the difference between millet and fallow savannah. A similar difference between millet and fallow savannah was highlighted in other sites by comparing the in situ measurements using a micrometeorological station (mainly Eddy correlation method) during the wet season (Gash et al., 1997). 4. Sensitivity Analysis

In this section, the sensitivity of the evaporative components (total evapotranspiration, soil evaporation, vegetation transpiration) to both the main soil (αgd, ψsat, θsat, Ksat, b) and the vegetation (d, zom, ϕ0.1m, r0, rp, fCover) parameters is analyzed. The objective of this study is not to make a complete sensitivity analysis of the model, but (i) to estimate the error of the model due to the parameters uncertainties, and (ii) to understand the global model behaviour. Therefore, this study does not take into account the interdependence between the different parameters. The tested combination of the vegetated parameters may be considered as realistic since the variations of these parameters are independent both from one another and from the soil parameters. The soil parameters are more or less linked to one another, except for the soil albedo. This is why the tested values are limited to the statistical range of variation of a loamy sand (Clapp and Hornberger, 1978) and are therefore coherent. In this frame the parameters independently vary within their respective range of variation (table 4). The range of variation goes from -90 % to + 380 % for the soil parameter and from -90 % to + 200 % for the vegetation parameters.

The relative variation of the flux is plotted according to the relative variation of each

parameter (%). The plots focus on the -90/+200% range as most of the parameters vary within that range and as the variations of Ksat, and b are continuous and low. Only the cumulative output values are taken into account in this study. The results are presented separately for the bare soil period (period n°1) and the vegetation growth period (period n°2). The parameters that have the greatest influence are not the same for all the processes. Other tests are made to


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evaluate both the impact of the calibrated plant cover and the uncertainty of the vegetation variables. Table 4 The parameters range of variation and the variables on the millet crop.

Reference

values Range of variation

(in absolute) Range of variation

(in relative)

Dry soil albedo (αgd, -) 0.27 0.2 - 0.4 -26 % / +48 %

Saturated water content (θsat, m3.m-3) 0.30 0.25 - 0.40 -17 % / +33 %

Water potential at saturation (|ψsat|, m) 0.10 0.01 - 0.20 -90 % / +100 %

Hydraulic conductivity at saturation (Ksat, m.s-1) 5.4 10-5 10-5 - 15.10-5 -81 % / +178 %

Water retention curve exponent (b, no unit) 1.5 1.4 - 5.7 -7 % / +280 %

Displacement height (d, m) 0.2 0.1 - 0.4 -50 % / +100 %

Roughness length for momentum (zom, m) 0.1 0.01 - 0.2 -90 % / +100 %

Root fraction upper 0.1 m (ϕ0.1m, -) 0.25 0.2 - 0.7 -25 % / +180 %

Minimum stomatal resistance (r0, s.m-1) 125

50 - 200 -60 % / +60 %

Internal plant resistance (rp, s) 9.4.108 1.108 - 15.108 -90 % / +60 %

Maximal Leaf Area Index (LAI, m2.m-2) 4 0.8 - 4.4 -80 % / +10 %

Green leaf fraction (LAIg/LAI, %) 70 % 55 - 100 % -21 % / +43 %

Covering fraction (fCover, %) 56 % 40 - 70 % -29 % / +25 %

4.1 Period n°1: Bare soil area

The soil parameters which influence the soil evaporation are first tested during the bare soil period (figure 5). During that period, the soil evaporation module is tested alone. Non-linear variations appear for all the variables, except for the water retention curve b and the bare soil albedo αgd. Ksat, , ψsat and b are the most influent parameters. A significant increase of Ksat and ψsat doesn’t affect the soil evaporation, while a low decrease implies a rapid evaporative variation. The soil evaporation reduces by 14% when Ksat decrease is of 80 % (representing a ratio of 1:5). On the opposite, the b parameters reduces the soil evaporation when it increases. Both the saturated water content θsat and the bare soil albedo αgd have a low influence (inferior to 5 %).

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Figure 5 The model sensitivity to the five soil parameters during the bare soil period (DOY 202-215). The relative variation of the cumulated bare soil evaporation is plotted according to the relative variation of each parameter (%).

Globally, the parameters variation mainly causes a significant reduction of the soil evaporation. The extreme variation of the influent parameters may lead to a decrease of -20% but to an increase of only +2 %. The current set of parameters almost corresponds to a maximum soil evaporation rate, which may explain the global overestimation by the model during the first period, and furthermore during the second period. Note that the combined variation of all the parameters may lead to a greater soil evaporation.

4.2 Period n°2: Vegetation Growing Season

(a) Vegetation parameters influence The model sensitivity to the vegetation parameters is now tested during the growing season

(period n°2) (figure 6). The minimum stomatal resistance r0 (-60 %) leads to the strongest final variations (+27 %)

of the transpiration flux (figure 6-a). This parameter is the scale parameter of the canopy stomatal resistance, which limits the transpiration process. The transpiration sensitivity is however non-linear, significantly increasing for the lowest values of r0. Another sensitive

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parameter is the plant resistance rp, which causes a maximum variation of 14 % in its variation range. The variations of the three other vegetation parameters (ϕ0.1m, z0m, d) lead to a maximum change of 7 % in their variation range.

All the soil parameters have a very limited impact on the soil evaporation (figure 6-b). Indeed, their influences are inferior to 7 % for the soil evaporation for an initial parameter variation range of ± 100 %. The sensitivity is however inverted between the soil evaporation and the vegetation transpiration. Indeed, a decrease of a variable, which increases the transpiration, decreases the evaporation, and vice versa. This trend is always true, except for the roughness length for momentum z0m, which modifies the aerodynamic resistance (through a logarithmic formulation) of the two processes.

The compensation between the evaporation and the transpiration variations tends then to

reduce the fluctuations of the total evapotranspiration (Braud, 1998; Boulet et al., 1999). The fluctuations of the total evapotranspiration with the vegetation parameters (figure 6-c) are governed by the transpiration process. Indeed, the impact of the vegetation parameters on the transpiration is logically greater than on the evaporation. Both r0 and rp are the most influent parameters, with a relative evaporative flux change of respectively 13 % and 6 %. Other parameters have a very low influence, which remains inferior to 4 % of the flux variation.

( a ) TRANSPIRATION

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Figure 6 The relative variations (%) of (a) the cumulated plants transpiration, (b) the cumulated soil evaporation and (c) the cumulated evapotranspiration depending on the vegetation parameters variation (%) during the millet growing period (DOY 223-263).

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(b) Influence of the vegetation cover fraction

The model sensitivity to the land cover fraction must be studied. This parameter remains constant all over the year in SISVAT. It defines the contribution of both the bare soil and the vegetated patchs. Its variation automatically leads to a transpiration modification of the same proportion (slope 1). Indeed, the transpiration is directly proportional to the vegetated area characteristics and then to the plant cover fraction. The sensitivity of the soil evaporation is lower, with a slope of -0.6. Indeed, the total soil evaporation is the sum of both the bare soil evaporation and the evaporation under the canopy layer. Finally, the total evapotranspiration trend is identical to the transpiration process one, but with a lower slope of about +0.2. Therefore, an increase of the vegetated area fraction tends to increase the global evapotranspiration rate. In the studied millet field, the uncertainty on the maximum land cover fraction is of about 20 % (Monteny, 1993). This value corresponds to a 4 % error of the final evapotranspiration, but is split between a -13 % for the soil evaporation and +20 % for the vegetation transpiration.

(c) Impact of the Vegetation variables Uncertainty

Two variables (LAI, LAIg) characterize the time evolution of the millet plants within the SISVAT surface scheme. To test the model sensitivity on these two variables is of a great importance, as their associated measurement uncertainties are never inferior to 15 % and can reach 45 % during the maximum growth period. The influence of the LAIs is tested in two steps. Firstly the maximal LAI varies while the green leaf fraction remains constant (70 %), and secondly the maximal LAI is fixed and the green leaf fraction varies between 55 % and 100 % of the total LAI. Their influences are not similar for all the processes. The total LAI only has an impact on the soil evaporation process, as it influences the radiative extinction. Both the green fraction and the total LAI vary accordingly in the transpiration process. The green leaf fraction variation has a higher slope, but on a limited variation range. The green fraction is the most influent variable in the total evapotranspiration, as there is no compensation between the soil and the vegetation processes. An increase of the green fraction logically tends to increase the total ET value, and vice versa. The resulting ET uncertainty varies from 3 % for the minimum measurement error (15 %), but can reach 5 % during the growing season where the LAIg uncertainty reaches 45 %. This is enough to explain half of the overestimation highlighted between millet and fallow savannah. 5. Conclusion

In this paper, the first objective was to evaluate the SISVAT model on the millet crop, where a few SVAT models have run. Our surface scheme provides accurate water and energy budgets at a 20-min time step. The diurnal cycle of both the net radiation (r² = 0.94) and the

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latent heat flux (r² = 0.80) was especially well estimated. This latter accurate simulation is very important, as the latent heat fluxes influence the upward air humidity within the atmosphere. This air humidity is a significant variable within the mesoscale MAR model to reproduce the rainy regime. During the growing period, the relative error of the cumulative ET is inferior to 6 %, with a lower error under bare soil conditions (only 3 %). The split between the soil evaporation and the vegetation transpiration was simulated. The soil evaporation appears as the dominant process in the millet crop during the rainy period (63 %). Therefore, the soil evaporation representation is of first importance. Its accuracy was demonstrated in this study during the 15-days bare soil period.

The second objective was to observe the model sensitivity to the parameter set in that case study. The soil evaporation is mainly sensitive to Ksat, ψsat and b, while the minimum stomatal resistance r0 is the most influent parameter of the transpiration process. The impact of the vegetation cover fraction was also tested. This parameter is very sensitive in SISVAT as it drives the repartition between the evaporation and the transpiration. A land cover fraction measurement uncertainty of 20% leads to a low 4 % error of the global ET, corresponding to a significant -13 % for the evaporation and +20 % for the transpiration. In the future, a multiple regression approach will be necessary to both analyze the model global behaviour and the influence of each parameter according to the other parameters within an operational configuration.

During the core of the rainy season, the millet evapotranspiration is inferior to the fallow savannah one. SISVAT correctly reproduces this behaviour. The difference is due to the higher transpiration rate of fallow savannah in spite of its lower LAI. This latter is compensated by a higher vegetation cover. In the arid area, the occasional rainfalls do not enable a full cover growth, which leads to many bare soil areas. The plants repartition is essential to accurately quantify the evaporative rate of each type of vegetation. Simioni (2002) shows that for a same vegetation cover, the spatial arrangement has an influence on the net primary production, and a very weak influence on the evapotranspiration. Many of our discussions or assumptions cannot be considered as settled because of a lack of measurement, especially with regards to the repartition between the soil evaporation and the vegetation transpiration. For instance, the lack of data does not enable to evaluate the tiger bush evaporative rate, which represents however the only vegetation in the plateau (28 % of the total HAPEX-Sahel area) (D'Herbes and Valentin, 1997).

The next objective will be to estimate the influence of the surface within the rainfall regime. The upward latent heat flux directly supplies by water vapor the boundary layer (Taylor et al., 1997). This step is made by forcing the upward water flux from the surface (using SISVAT) to the atmosphere (using MAR). In this perspective, three steps must be made. The first one can be considered as completed, even though the evaluation of the SISVAT model was made during a limited 2-months period. Indeed the evapotranspiration during the dry season is of no influence on the rainfall regime. A validation during a dry period should be made in order to


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simulate the complete hydrological cycle, as the soil water retention was not tested yet eventhough this is of a great importance. The second step is the aggregation of the various land covers at the mesoscale resolution (40 km). At this scale, the various land covers are represented. Given the lack of measurements, an automatic parameterization (for both the soil and the vegetation) is used. The impact of this simplification must be quantified. Then, the final step will be to follow the upward water flux, (i) firstly within the atmosphere boundary layer through the air humidity evolution and the mechanism of the storm, and (ii) secondly within the mesoscale MAR model to generate the ground rainfall. This will enable to quantify the consequence of the land cover changes (deforestation, reduction of the crop rotation) (Loireau, 1998) on the hydrological cycle, because of an anthropic pressure.

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Gash, J.H.C., P. Kabat, B.A. Monteny, M. Amadou, P. Bessemoulin, H. Billing, E.M. Blyth, H.A.R. DeBruin, J.A. Elbers, T. Friborg, G. Harrison, C.J. Holwill, C.R. Lloyd, J.P Lhomme, J.B. Moncrieff, D. Puech, H. Soegaard, J.D. Taupin, A. Tuzet, and A. Verhoef, 1997: The variability of evaporation during the HAPEX-Sahel Intensive Observation Period. J. Hydrol., 188-189, 385-399.

Gaze S.R., L.P. Simmonds, J. Brouwer, and J. Bouma, 1997: Measurement of surface redistribution of rainfall and modelling its effect on water balance calculations for a millet field on sandy soil in Niger. J. Hydrol., 188-189, 267-284.

Goutorbe, J.P., T. Lebel, A.J. Dolman, J.H.C. Gash, P. Kabat, Y.H. Kerr, B. Monteny, S.D. Prince, J.N.M. Stricker, A. Tinga, and J.S. Wallace, 1997a: An overview of HAPEX-Sahel : a study in climate and desertification. J. Hydrol., 188-189, 4-17.

Goutorbe, J.P., J. Noilhan, P. Lacarrere, and I. Braud, 1997b: Modeling of the atmospheric column over the Central sites during HAPEX-Sahel. J. Hydrol., 188-189, 1017-1039.

Hanan, N.P., and S.D. Prince, 1997: Stomatal conductance of West-Central Supersite vegetation in HAPEX-Sahel: measurements and empirical models. J. Hydrol., 188-189, 536-562.


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Le Barbe, L., and T. Lebel, 1997: Rainfall climatology of the HAPEX-Sahel region during the years 1950-1990. J. Hydrol., 188-189, 43-73.Lefebre F., Gallée H., Van Ypersele J.P., Grevell W., 2002. Modeling of snow and ice melt at ETH_Camp (West Greenland) : a study of surface albedo. Journal of Geophysical Research, accepted.

Lefebre, F., H. Gallée, J.P. Van Ypersele, and W. Grevell, 2002: Modeling of snow and ice melt at ETH_Camp (West Greenland) : a study of surface albedo. J. Geophys. Res., accepted.

Loireau, M., 1998. Espace, ressources, usages : Spatialisation des interactions dynamiques entre les systèmes sociaux et les systèmes écologiques au Sahel nigériens., PhD Thesis, University Montpellier III, France.

McCumber, M.C., and R.A. Pielke, 1981: Simulation of the effects of surface fluxes of heat and moisture in a mesoscale numerical model. Part I : soil layer. J. Geophys. Res., 86, 9929-9983.

Monteny, B.A., 1993: HAPEX-Sahel 1992, SuperSite Central Est, campagne de mesure. Report available on request at IRD, Montpellier, 230 pp.

Monteny, B.A., J.P. Lhomme, A. Chehbouni, D. Troufleau, M. Amadou, M. Sicot., A. Verhoef, S. Galle, F. Said, and C.R. Lloyd, 1997: The role of the Sahelian biosphere on the water and CO2 cycle during the HAPEX-Sahel Experiment. J. Hydro., 188-189, 516-535.

Peugeot, C., 1995: Influence de l'encroûtement superficiel du sol sur le fonctionnement hydrologique d'un versant sahélien (Niger) - Expérimentations in situ et modélisation. PhD Thesis, LTHE Grenoble (France), 305 pp.

Rocksröm, J., 1997: On-farm agrohydrological analysis of the Sahelian yield crisis : Rainfall partitioning, soil nutrients and water use efficiency of pearl millet. PH-D of the Natural Resources Management, Department of Systems Ecology, Stockholm University, S-10691 Stockholm (Sweden).

Simioni, G., 2002: Importance de la structure spatiale de la strate arborée sur les fonctionnements carboné et hydrique des écosystèmes herbe-arbre. Exemple d'une savane d'Afrique de L'Ouest. PhD Thesis, ENS ecologie (Paris, France), 181 pp.

Taylor, C.M., F. Said, and T. Lebel, 1997a: Interactions between the land surface and mesoscale rainfall variability during HAPEX-Sahel. Mon. Wea. Rev., 125, 2211-2227.

Taylor, C.M., R.J. Harding, A.J. Thorpe, and P. Bessemoulin, 1997b: A mesoscale simulation of land surface heterogeneity from HAPEX-Sahel. J. Hydrol., 188-189, 1040-1066.

Taylor, C.M., and T. Lebel, 1998: Observational evidence of persistent convective-scale rainfall patterns. Mon. Wea. Rev., 126, 1597-1607.

Taylor, C.M., and D.B. Clark, 2001: The diurnal cycle and African easterly waves: a land surface perspective. Quart. J. Roy. Meteor. Soc., 127, 845-867.

Tuzet, A., Castell J-F, Perrier A., Zurflush O., 1997: Flux heterogeneity and evapotranspiration partitioning in a sparse canopy : the fallow savannah. Journal of Hydrology 188-189, 482-493.

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Vandervaere, J.P., C. Peugeot, M. Vauclin, R.A. Angulo Jaramillo, and T. Lebel, 1997: Estimating hydraulic conductivity of crusted soils using disc infiltrometers and minitensiometers. J. Hydrol., 188-189, 203-223.

Wallace, J.S., 1991: The measurement and modeling of evaporation from semiarid land. Soil water balance in the Sudano-Sahelian zone, Proceedings of the Niamey workshop (February 1991), IAHS Publication 199, 131-148.

Wallace, J.S., Lloyd C.R., and Sivakumar M.V.K., 1993: Measurements of soil, plant and total evaporation from millet in Niger. Agricultural and Forest Meteorology, 63, 149-169.


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INFLUENCE DU CHOIX DU JEU DE PARAMETRES

SUR LA SIMULATION DES FLUX DE CHALEUR LATENTE AU SEIN DU SCHEMA DE SURFACE SISVAT

De la Validation au mode Opérationnel

Le schéma de surface SISVAT a correctement reproduit les bilans hydriques et énergétiques à l'échelle micro-météorologique (quelques hectares) sur les deux végétations testées, à savoir une zone de jachère (chapitre 2) et un champ de mil (chapitre 3). Cette réponse du modèle est d'autant plus satisfaisante que son estimation s'est produite dans des conditions environnementales extrêmes (ETP fort, évènements pluvieux intenses), sur des états de surfaces contrastées. La zone de jachère est constituée d’une association de deux espèces végétales, avec un LAI toujours faible, une couverture végétale étendue, et un encroûtement permanent. Au contraire, le mil possède un LAI élevé, une couverture végétale faible et le sarclage y réduit la présence de croûtes. Cette évaluation du modèle s’est faite sur des conditions variées, allant d'une zone de sol nu en période de pluie, à une zone végétale durant la saison sèche. Par conséquent, le schéma de surface SISVAT peut être considéré comme relativement fiable et robuste sur les deux types de végétation mil et jachère qui couvrent plus des 60% de la zone étudiée.

L’impact des différents paramètres sur les simulations du schéma de surface SISVAT a précédemment été décrit à l'échelle micro-météorologique (quelques hectares) sur la zone de mil. C’est à cette échelle seulement que le modèle peut être validé avec des mesures in-situ. Cependant, dans le contexte de couplage avec le modèle atmosphérique MAR à l’échelle de l’Afrique de l’Ouest, le schéma de surface SISVAT fonctionne à une résolution de 40×40 km2. A cette résolution, deux différences majeures apparaissent : (i) l’intégration de différents états de surface (types de sol et de couverts végétaux) et de conditions environnementales (précipitations, aéronomie), et (ii) des paramètres de surface définis non plus à partir de mesures locales mais d’après des classifications prédéfinies dans des tables adaptées. Le second point entraîne des simplifications réductrices, l’ensemble des couverts ne pouvant être représentés dans les tables. Il est donc crucial de quantifier les erreurs produites en utilisant ce paramétrage automatique. Une modélisation utilisant cette paramétrisation en mode opérationnel est comparée aux résultats obtenus précédemment en utilisant des paramètres provenant de mesures in situ. Les erreurs produites sur les flux de chaleur latente sont donc observées sur les mêmes zones d'étude que précédemment et sur les mêmes périodes d'observation.

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L'impact de la Paramétrisation au sein du modèle SISVAT

Paramétrisation choisie en mode Opérationnel A l’échelle de l’Afrique de l’Ouest, le schéma de surface SISVAT fonctionne en mode opérationnel avec des paramètres automatiquement sélectionnés dans deux tables de classification valables sur l'ensemble du globe : USDA (United State Department of Agriculture) pour le sol (12 classes disponibles), et IGBP (International Geophere-Biosphere Program) pour la végétation (17 classes disponibles). Pour les deux zones de végétation (jachère et mil) étudiées dans les chapitres précédents, on peut comparer les paramètres mesurés sur le terrain (utilisés précédemment) avec ceux déduits des classifications USDA et IGBP en mode opérationnel.

Le type sol sélectionné dans la classification USDA correspond au sable limoneux (loamy

sand). Les paramètres associés sont indiqués dans le tableau 1. Les différences majeures concernent la teneur en eau à saturation (+37 %), mais surtout pour l'exposant de la courbe de rétention (+192 %) et la conductivité hydraulique à saturation (+188 %). Cette dernière valeur doit être relativisée par rapport à la gamme de variation de ce paramètre. Tableau 1 Différence des paramètres du sol entre les mesures in-situ et la classification USDA.

In-situ Sable

limoneux Diff. (%)

Contenu en eau à saturation (θsat, m3.m-3) 0.30 0.41 +37

Potentiel à saturation (ψsat, m) -0.10 -0.09 -10

Conductivité hydraulique à saturation (Ksat, m.s-1) 5.4 10-5 15.6 10-5 +188

Exposant de la courbe de rétention (b, no unit) 1.5 4.38 +192

Pour la végétation, le millet est considéré comme une 'culture' (cropland) dans la classification IGBP, associé à une 'culture médium' (crop medium) dans SISVAT. La jachère est représentée comme une 'savane' (savannah) dans la classification IGBP, assimilée à un 'couvert herbeux haut' (grass high) dans SISVAT. Les différences notables pour les deux végétations sont liées à la résistance stomatique minimale et la rugosité (tableau 2), compte tenu de la sensibilité du modèle étudié dans le chapitre précédent.


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Tableau 2 Différence des paramètres de végétation entre les mesures in-situ et la classification IGBP.

Mil

In Situ Classe % Jachère

In Situ Classe %

Hauteur de déplacement (d, m) 0.20 0.21 +5 0.38 0.7 +84

Longueur de rugosité (zom, m) 0.10 0.03 -70 0.08 0.1 +25

Fraction de racine dans 0.1 m (ϕ0.1m, -) 0.25 0.30 +20 0.25 0.7 +180

Résistance stomatique minimale (r0, s.m-1) 125 50 -60 80 50 -38

Résistance interne de la plante (rp, s) 9.4 108 5.0 108 -47 9.4 108 5.0 108 -47

Comparaison des résultats

Afin d’observer les effets de la paramétrisation utilisée sur les résultats du modèle SISVAT, les résultats de simulations obtenus avec les paramètres tabulés sont comparés aux résultats obtenus avec les paramètres in situ. Afin de tester à la fois l’influence du jeu de paramètre du sol et celui de la végétation, les simulations sont effectuées en changeant (i) uniquement les paramètres de sol, (ii) uniquement les paramètres de végétation, puis (iii) les paramètres de sol et de végétation conjointement. Au total, en reprenant les zones précédemment étudiés, trois simulations différentes ont été effectuées, à savoir sur (i) la période de sol nu dans le champ de mil, (ii) la période où la culture de mil a été effective, et (iii) la période entière sur la jachère. Les résultats obtenus sont donc comparés avec ceux provenant des simulations originelles (tableau 3).

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Tableau 3 Résultats des simulations effectuées utilisant (i) seulement les paramètres USDA pour le sol, (ii) seulement les paramètres IGBP pour la végétation, et (iii) les deux paramétrisations réunies. Les pourcentages sont calculés par rapport aux valeurs observées.

Obs. Simulation Sol

(USDA) Végétation

(IGBP) Sol +

Végétation

Mil (Période 1)

Evaporation du sol 31.0 32.1 (+ 3 %) 26.8 (-13 %) - -

Mil (Période 2)

Evapotranspiration 114.8 121.3 (+6 %) 101.1 (-12 %) 128.4 (+14 %) 115.5 (+1 %)

Evaporation - 59.2 56.6 53.6 52.0

Transpiration - 62.1 44.6 74.8 63.5

Jachère

Evapotranspiration 150.7 164.1 (+9 %) 109.8 (-27 %) 167.8 (+18 %) 109.2 (-27 %)

Evaporation - 60.3 53.2 59.6 55.0

Transpiration - 103.8 56.6 108.2 54.2

Les trois principaux résultats de cette comparaison des performance du modèle avec différents types de paramètres sont les suivants :

• Les paramètres du sol issus de la classification USDA réduisent la transpiration comme l'évapotranspiration globale (entre -12 et -27 % par rapport à l'observation, entre –16 et –38 par rapport aux estimations antérieures). Par conséquent, l'évaporation du sol nu aura tendance à être sous-estimée en mode opérationnel. Cette sous-estimation peut être principalement mise sur le fait du paramètre de forme b, compte tenu de la sensibilité du modèle décrite en début de ce chapitre.

• Les paramètres de végétation issus de la classification IGBP augmentent

l'évapotranspiration globale (entre +14 et +18 % par rapport à l'observation, +8 et +9% par rapport aux estimations antérieures). La résistance stomatale minimale en est la principale responsable.

• l'utilisation conjointe des deux jeux de paramètres opérationnels ne tend pas

systématiquement à compenser les erreurs respectives. Sur le mil, l'évapotranspiration cumulée est pratiquement égale à la valeur mesurée (+1 %), alors que chaque jeu de paramètres avait une erreur de l'ordre de 10 %. Par contre, une telle compensation n'a pas eu lieu sur la jachère et une sous-estimation est conséquente (- 27 % par rapport à l’observation). Cette dernière est expliquée pour 70 % par l'estimation sur la saison


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sèche. Le modèle a tendance à sous-estimer les flux latents durant les périodes sèches. Si on enlève la période sèche sur la période totale de la zone de jachère, le modèle tend toujours à sous-estimer l'évapotranspiration totale (-8 % par rapport à l’observation).

Au final, les paramètres automatiquement utilisés en mode opérationnel au sein de SISVAT donnent des résultats corrects, même si l'évapotranspiration durant les périodes sèches est systématiquement sous-estimée.

De l’Echelle micro-météorologique à la méso-échelle

Les simulations précédentes, obtenues à l’échelle micro-météorologique, ont indiqué que le schéma de surface SISVAT a tendance à envoyer moins de vapeur d'eau en direction de l'atmosphère sur le sol nu ou en période sèche, ce qui induit globalement une sous-estimation. Des résultats récents effectués à la méso-échelle avec le système SISVAT-MAR ont indiqué une sous-estimation de la pluviométrie simulée sur l'ensemble de l'Afrique de l'Ouest (de -15 à -30 %) (Moufouma-Okia, 2003). Sur la zone sahélienne d'HAPEX-Sahel, le modèle couplé tend à (i) sous-estimer les pluies au début de la saison des pluies, et (ii) à les surestimer au cœur de la saison des pluies, d'autant plus les années sèches. Le premier point peut être expliqué par le biais du modèle SISVAT puisque le début de la saison des pluies cumule à la fois des périodes de sol nu et de nombreuses périodes sèches compte tenu des évènements pluvieux qui sont très espacés. Le cœur de la saison est sans doute surestimé pour une autre raison qui reste à définir. Conclusion

Les simulations précédentes ont montré le rôle et l’importance de la paramétrisation sur les résultats du schéma de surface SISVAT. Cela a été particulièrement été mis en avant dans le cas de la jachère, entre la simulation utilisant des paramètres in-situ (+ 9 % par rapport aux observations) et celle utilisant des paramètres tabulés (- 27 % par rapport aux observations). De plus, certaines erreurs observées sur les simulations SISVAT-MAR sur l’ensemble de l’Afrique peuvent être expliquées par le jeu de paramètres utilisés. Il sera donc crucial dans le futur de chercher à mieux paramétrer la surface, afin de limiter ce type d'erreurs dû à cette seule paramétrisation automatique en mode opérationnel.

References Moufouma-Okia W., 2003: Modélisation du Climat de l’Afrique de l’Ouest avec le Modèle Atmosphérique Régional MAR. Thèse de l’INPG, Laboratoire LTHE (France), 177 pp.

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Quatrième Chapitre

Evaluation du modèle conceptuel de Ritchie sur les trois principaux types

de végétation

MODEL CONCEPTUEL DE RITCHIE

133

CHAPITRE 4 EVALUATION DU MODELE CONCEPTUEL DE RITCHIE SUR LES TROIS PRINCIPAUX TYPES DE VEGETATION Résumé du Chapitre

Contexte de l'Etude

Le schéma de surface SISVAT, a correctement reproduit les flux évaporatifs (chapitre 2 et 3). Cependant, un tel schéma de surface demeure difficile à mettre en œuvre sur une longue période ou sur de nouveaux couverts végétaux compte tenu de l'importance du jeu de données nécessaires. En effet, cette approche est très gourmande en données, avec pas moins de onze paramètres (pour le sol et la végétation) et six variables qui expriment l’évolution de la végétation et des conditions atmosphériques. Ce manque de souplesse n'a par exemple pas permis (i) d'examiner ce modèle sur une zone de brousse tigrée, et (ii) de faire fonctionner le modèle sur une année complète qui caractérise pourtant le cycle hydrologique. Par conséquent, il demeure nécessaire de développer et/ou valider des approches de modélisations simples comme ont pu le stipuler Wallace et al. (1993).

Dans cette optique, nous avons choisi une méthodologie résolument simple basée sur le modèle conceptuel de Ritchie (1972) qui estime l'évapotranspiration à un pas de temps journalier et distingue l'évaporation du sol et la transpiration des plantes. Son faible nombre de paramètres (3) et de variables (4) permet une mise en œuvre facile. Son module "sol" est séparé en deux phases à partir d'un événement pluvieux : une première phase durant laquelle l'évaporation (maximale) est contrôlée par la demande atmosphérique, et une seconde phase durant laquelle le taux d'évaporation est gouverné par le potentiel du sol. La transpiration est quant à elle directement reliée à l'indice foliaire de la plante et à la demande évaporative de l'atmosphère.

Ce modèle a été évalué sur les trois principaux types de végétation au Sahel, à savoir les champs de mil et les zones de jachère dans les plaines sableuses, ainsi que la brousse tigrée sur les plateaux latéritiques. Leur période de validation correspond à la période d'observation intense de la campagne HAPEX-Sahel. Les bons résultats de ce modèle nous ont autorisé à effectuer des simulations au pas de temps annuel, ce qui a permis de les comparer avec les résultats obtenus à ce pas de temps par d'autres méthodes (indirectes).

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Malgré la simplicité du modèle, l'évapotranspiration journalière a été globalement bien estimée par le modèle. L'erreur relative sur le cumul en 2 mois s'est avérée de l'ordre de 15 % sur l'ensemble des trois types de végétation. Les meilleurs résultats ont été obtenus sur la culture de mil (13 %) et sur la brousse tigrée (14 %). Les évolutions temporelles sont apparues relativement correctes, avec des coefficients de détermination variant de 0.48 pour la jachère à 0.68 pour la brousse tigrée. Le point important est que les erreurs sur les diverses valeurs simulées restent proches de celles des modèles dits complexes, comme ceux évoqués dans la synthèse du chapitre 1 (Shuttleworth et Wallace, SVATs) et les simulations des chapitres 2 et 3 (SISVAT).

Le test de sensibilité du modèle qui a été effectué sur la culture de mil a mis en avant une influence majeure de l'Evapotranspiration Potentielle (ETP) qui joue un rôle à la fois sur l'évaporation du sol durant les premières phases, mais aussi directement sur la transpiration. Par exemple, un écart de l'ETP de seulement 10 % conduit à une modification significative de l'ET (7 %). Dans l'ensemble, les caractéristiques de la végétation (fraction de couverture végétale, LAIg, LAI) sont très influentes, d'autant plus que l'incertitude sur leurs mesures est importante. Les paramètres du sol utilisés dans les autres études effectuées sur cette région varient largement (+/- 40%), cependant leur influence reste toujours inférieure à 6 % durant l’ensemble de la période d’étude du mil.

La simulation effectuée sur une année hydrologique complète montre des résultats cohérents avec les observations indirectes effectuées sur des zones proches. L'évaporation du sol est majoritaire sur tous les couverts (entre 64 et 78 %), et est maximale sur la zone de mil et celle de jachère. La transpiration devient prépondérante pendant le mois suivant le dernier événement pluvieux.

Il faut cependant rester vigilant sur les performances du modèle puisque ce modèle a été validé sur une partie seulement de la saison des pluies, lorsque le stock hydrique est maximal. La plante est donc moins souvent susceptible d'être affectée par le stress hydrique. Il serait donc bon de valider ce modèle lors du début de la saison des pluies et en période sèche. De plus, ce type de modélisation a quelques difficultés à reconstituer la transpiration sur de faibles LAI comme sur la jachère.


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EVALUATION OF THE SIMPLE CONCEPTUAL RITCHIE MODEL IN THE SAHEL

G. Derive*, S. Galle

Laboratoire d'Etude des Transferts en Hydrologie et Environnement LTHE (CNRS UMR 5564), BP 53, 38041 Grenoble Cedex 9, France

Submitted to the Journal of Hydrology

June 30th, 2003

* Corresponding author. Fax: (+33)-4-76-82-52-86 E-mail address: [email protected] (G. Derive)

Abstract In the Sahel, evapotranspiration represents the main component of the water balance for

every type of vegetation. It is thus essential to both understand and model the spatio-temporal evolution of this surface evaporative flux during a complete hydrological cycle. However, two requirements must be fulfilled. The first is to determinate the evaporative rate of all types of vegetation, to quantify their relative contributions to the overall landscape. The second is to quantify the internal distribution between the contribution of soil and vegetation .

With this aim, the Ritchie (1972) two-phase conceptual model was applied to the HAPEX-Sahel area located in South Niger (2-3° East, 13-14° North). This model can easily be implemented given the few input parameters and variables required. The model was run in 1992 for the three main vegetation types of this Sahelian area : millet crop, fallow savannah and tiger bush forest. The simulations of the model were compared with measurements at both daily and annual time steps.

The simulation provided satisfying daily results (r² > 0.48, rmse < 0.96 mm) for the three

different types of vegetation, in comparison with the in-situ micrometeorological measurements. The cumulative relative error was always inferior to 6 % over a two-month period. The response of the model highlights an overestimation during the first phase combined with an underestimation during the second phase. Evaporation from the soil represents the main component of the global ET (superior to 2/3). The vegetation cover fraction appears to be the most influential input data. This model is a realistic tool to quantify the impact of a potential climatic or anthropic change in the Sahel. Keywords: Evapotranspiration, Ritchie's model, Millet, Fallow savannah, Tiger bush, HAPEX-Sahel.

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1. Introduction

Understanding the water budget in a semi-arid environment is essential; firstly to estimate the impact of global climate variations and secondly to improve local living conditions. One of the aims of the HAPEX-Sahel experiment (Hydrology-Atmospheric Pilot Experiment in the Sahel) (Goutorbe et al., 1997a) was both to understand and quantify the surface water balance in a typical Sahelian zone, in South Niger (2-3° East, 13-14° North). In this Sahelian area, the main component of the water budget is EvapoTranspiration (ET). Indeed, more than 75 % of the incident rainfall is evaporated on all types of vegetation, at all time steps and spatial scales. This latter value was observed by many authors (Culf et al., 1993; Wallace et al., 1993; Peugeot, 1995; Gash et al., 1997; Ehrmann, 1999; among others) using various in situ techniques (Bowen ratio, Eddy correlation, lysimeter, soil moisture monitoring). In return, the surface fluxes strongly influence the rainfall regime in this climatic region. For instance, the evaporative flux directly modifies the necessary air humidity which influences the appearance of convective systems (Taylor et al., 1997; Taylor et al., 1998; Wang and Eltahir, 2000), and consequently precipitation. This feedback has been observed in West Africa by various authors, either by using models (de Ridder, 1997, Wang and Eltahir, 2000), or by making measurements (Taylor and Lebel, 1998). Therefore, we must take into account the need for modeling of evapotranspiration in the Sahelian zone.

In order to estimate the evapotranspiration rate, this study focuses on 1-day temporal

resolution. Indeed, the daily time step represents an interesting time resolution between the high resolution (typically 20 min), which requires a lot of data, usually unavailable in the Sahel, and the monthly time step, which is only based on statistical methods. The necessary final spatial resolution is the hydrological unit where the surface water budget is closed. Within this perspective, two requirements must be fulfilled. The first is to quantify the relative contribution of each type of vegetation in the area. The evaporative behaviour of each vegetation type must be studied in order (i) to estimate the evapotranspiration of a watershed where various types of land cover exist, and (ii) to predict the influence of external factors such as a possible climatic change which may reduce total rainfall, or a land cover change due to anthropic pressure. This land cover change is a progressive tendancy to replace natural vegetation by crops, to reduce the rotation period between the millet fields and fallow savannah, and to cut the tiger bush forests. The second condition is to know the internal distribution between soil evaporation and plant transpiration. Indeed, knowledge of this distribution is one of the minimum requirements to understand the respective roles of both soil and vegetation within the sparse Sahelian cover (Wallace et al., 1993).

In the Sahel, meeting these two conditions is rather difficult mainly because of the few available data sets. In fact, the majority of the models ran on only one type of vegetation (mostly fallow savannah) during a limited validation period (far less than two months) (Wallace et al., 1993; Braud et al., 1997; Goutorbe et al., 1997-b; Monteny et al., 1997; Ehrmann, 1999). As suggested by Wallace et al. (1993), a simple model is thus recommended,


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in order to be applicable to all vegetation types, during a complete hydrological cycle. In the present study, a simple model based on the conceptual scheme formulated by Ritchie (1972) was implemented and tested. This model requires few input data, but enables both the soil and the vegetation fraction to be quantified at a daily time step. Although it was developed for irrigated crops, it was partially applied to various climatic areas and provided satisfying results. This model was used on fallow savannah (Kabat et al., 1997) and the bare soil area of the tiger bush (Wallace and Holwill, 1997). This paper describes the evaluation of such a model on the three main vegetation types in the studied area. The required spatial resolution is the elementary vegetation cover (several hectares), which corresponds to the micrometeorological flux measurement resolution (Bowen ratio or Eddy correlation methods) (Gash et al., 1997). The evaluation of the model is made by using the available data collected in 1992 during the Intensive Observation Period (IOP) of the HAPEX-Sahel experiment (Niger). This period lasted 2-months during the core of the rainy season. Another issue that will be discussed is the sensitivity of the model. It was tested on millet field, which represents one of the simplest examples with only one type of vegetation. Therefore, no simplification was required. The cumulated ET during the whole validation period was quantified and discussed according to the uncertainty of both the parameters and the variables. Finally, the model was run during a complete hydrological year. The simulated water balance was compared, for the three types of vegetation, to the various available data types. 2. Study Area and Applied Methodology

2.1 Study Area Description

The HAPEX-Sahel zone (2-3° East, 13-14° North) in South Niger (West Africa) represents a typical Sahelian area. It has a low annual rainfall amount during one short rainy season. In Niamey, about 90 % of the average annual rainfall (564 mm) falls between June and September (Le Barbé and Lebel, 1997). Rainfall is mainly due to convective storms, which implies a high spatial and temporal variability. The median rain intensity is 35 mm.hour-1. One third of the annual precipitation falls at a rate of more than 50 mm.hour-1. Half of the annual rain falls in five hours. In the Sahel, the difference between the wet and the dry years is mainly due to the number of rainy events rather than to their total quantity or their intensity (Le Barbé et al., 2002).

The landscape is divided into plains (72 % of the HAPEX area) and plateaus (28 %)

(D'Herbes and Valentin, 1997). It is characterized by a gentle relief in both the valleys (2-8 % slope) and the flat plateaus (0-2 % slope). In the plains, the soil type is deep reddish brown sand (88 % sand, 8 % silt, 4 % clay) (Nagumo, 1993) and it is mainly covered by both millet fields and fallow savannah areas. Fallow savannah represents the dominant vegetation type in the HAPEX-Sahel zone (39 % of the total area) (D'Herbès and Valentin, 1997). It is a combination of low annual grass layers and sparse bushes. Millet is the main cereal crop in

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Niger (Serpantie and Milleville, 1992). The millet fields covered about 22 % of the total HAPEX-Sahel area. The remaining 11 % in the valleys consists of degraded hillslopes characterized by sparse vegetation and crusted bare soils. The lateritic plateaus (maximum 240 meters high) are made of a shallow sandy clay loam soil (41 % sand, 39 % clay, 20 % silt) (Nagumo, 1993; Ambouta, 1997), with one-meter deep underlying ironcrust. They are mainly covered by natural vegetation called “tiger bush”, composed of forest bands separated by crusted bare soil areas. This specific organization, which looks like tiger skin when seen from the sky, enables efficient use to be made of the limited and irregular water supply (Galle et al., 2001).

In the present study, all the data were collected in 1992 during the core of the rainy season

(15 August - 9 October). The experimental sites are located about 70 km East of Niamey, near the village of Banizoumbou (13°31.93'N, 2°39.64'E).

2.2 Model Description

The suggested methodology is based on Ritchie’s simple approach (1972). This conceptual model was implemented to estimate the daily ET on homogeneous covers. The calculation of both the soil evaporation and the vegetation transpiration is made separately in two distinct modules. Each evaporative term is expressed in mm.day-1. Evaporation within the soil module is divided into two phases after a rainy event. During the first phase, the real soil evaporation (Es1) is equal to the potential soil evaporation (Es0) (eqn 1). The evaporative amount is then only limited by the atmospheric conditions. In Ritchie's initial model, the aerodynamic term, which represents the aerodynamic power within the ETP value, was neglected. This latter component represents, however, a significant fraction of the ETP (15-40 % of the ETP) under these Sahelian conditions (Wallace and Holwill, 1997). Therefore, this term is taken into consideration in this study. The first phase lasts until the total amount of the evaporated water is equal to a parameter called U (mm). This latter parameter depends exclusively on the type of soil, ranging from 3 mm on a very sandy soil (Daamen et al., 1993) to 14 mm on a more clayey soil (Lascano, 1991). This cumulative evaporation is associated with a time t1 (in days), which corresponds to the moment when the cumulative U evaporation is reached. Then, in the second phase, the cumulative soil evaporation (ΣEs2) increases as the square root of time (t-t1) (eqn 2). During the second phase, the soil evaporation is assumed as only limited by the soil water content. The decrease depends on a single parameter called αs (mm.day-0.5), which is linked to the soil’s hydrodynamic properties.

t ≤ t1 UEs Es1tt

0t01 =∑ ∑=

=

= (1)

t > t1 ∑ −×α= )tt(Es1

s2 (2)


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During the first phase (eqn 1), the potential soil evaporation (Es0) under vegetation cover is expressed by a classical Beer's law allowing the radiation extinction of the plants to be taken into account (eqn 3). In this equation, the k-parameter characterizes the plant radiation attenuation, mainly depending on leaf characteristics (surface area, inclination). In this study, the retained k parameter equals 0.4 as suggested in Ritchie's initial version (1972). This latter value is very close to the value found by Wallace et al. (1990) on a pearl millet in Niger (0.41). In this formulation, the total Leaf Area Index (LAI m2.m-2) describes the time evolution of the vegetation.

( )LAIk-expETPEs0 ××= (3) In the Ritchie model (1972), the plant transpiration is governed by the total LAI. This assumption is true for the millet crop where Wallace et al., (1993) highlighted that the transpiration fraction was controlled more by the area of the leaf than by the stomatal conductance. In the present study, the green LAI (LAIg), i.e. the surface area of the green leaves, is used instead of the total LAI. In the initial Ritchie model, transpiration is only controlled by atmospheric demand, since the water quantity is not limited in the root zone (eqn 4). A hydric stress can however appear on the vegetative system under Sahelian conditions. This assumption is discussed later below. Note that the LAIg values measured in our studied Sahelian area never exceed 3 m2.m-2.

−×

= EsETP;3LAIgETP

minEv (4)

We must keep in mind that the Ritchie model was created to be applied to a homogeneous cover. Vegetated zones however, are very sparse in the Sahelian area. For instance, the maximum plant cover equals 56 % in the millet field and 72 % in fallow savannah in the studied area (Monteny, 1993). Therefore, in this study, the surface is represented by a mosaic composed of two elements: a bare soil area and a homogeneous vegetated zone. A simplification is required for the vegetation composed of various contrasted strata, such as (i) fallow savannah comprising a low grass layer and some isolated bushes (Monteny et al., 1997), or (ii) tiger bush forests composed of many species (Combretum micranthum and Guiera senegalensis covering 73 % of the surface area of the forest ) (Ehrmann, 1999). The daily total ET is then, the addition of the bare soil area (ETs) and the evapotranspiration (soil evaporation plus plant transpiration) of the vegetated area (ETv), weighted by the cover fraction (fCover), as follows:

( ) ETvfCoverETsfCover1ET ×+×−= (5) Finally, this model requires only two parameters for the soil (U, αs) and one for the vegetation (fCover). Two variables are needed to characterize the meteorological conditions (rainfall, ETP), and two others for the time evolution of the plants (LAI, LAIg).

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3. Available Data

The studied millet field (13°32.56'N, 2°40.50'E) and the fallow savannah (13°33.49'N, 2°40.93'E) sites are less than 2 km from one another. The tiger bush area is located 10 km from these two previous sites (13°29.'N, 2°35'E). The parameters and variables, that are needed in order to apply the model to the three local sites, are described below.

3.1 Atmospheric Forcing

During the HAPEX-Sahel experiment, rainfall (mm) was collected using a network of 100 automatic rain-gauges spread over the square degree (Lebel et al., 1997). Rainfall is measured locally on the three sub-sites of this study. The input data of the model are daily values. In 1992, the rainy season lasted four months at Banizoumbou site, between May, 11th (Day Of Year 132) and September, 20th (DOY 264) (figure 1). The annual rainfall equals 429 mm, which represents a 24 % drop compared to the mean annual rainfall (562 mm) observed at Niamey during an 85-year period (1905-1989). This annual amount is still low in comparison with the mean annual rainfall (490 mm) during the 1970-90s drought period (Le Barbé and Lebel, 1997).

Figure 1 Annual time evolution of both daily rainfall and potential evapotranspiration in 1992. The rainy season lasted only four months from May 11th to September 20th.

The daily Potential EvapoTranspiration (mm) was measured at the Banizoumbou station, 4

km from both the millet and the fallow savannah sites, and 10 km from the tiger bush site. The daily ETP is calculated by using the Penman formulation (1948), which separates the

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equilibrium and the aerodynamic components. The equilibrium fraction characterizes the incident radiation power and the aerodynamic fraction represents the local aerodynamic conditions. Their proportions are respectively 80 % and 20 % of the total ETP. The daily ETP values range from 2 to 8 mm (figure 1). The minimal values were observed during the rainy season, especially during the rainy days. The annual ETP equaled 1968 mm in 1992, which is close to the annual average value (2057 mm) measured in Niamey (1953-62) by Sivakumar (1987). This global atmospheric demand represents 4-5 times the annual rainfall.

3.2 Soil Textural Characteristics Nagumo (1993) makes a distinction between the two main types of soil in this Sahelian

area : the deep reddish brown sandy soils located in the valleys, and the shallow reddish brown clayey soils characterizing the plateaus. The soil parameters are thus determinated by topographic position. The U-parameter was measured as similar on both the sandy soil (6 mm) (Black et al., 1969; Ritchie, 1972) and the sandy clay loam (5.6 mm) (Wallace and Holwill, 1997). Given the high evaporative demand, the associated first phase only lasts one day after the rainy event, as observed in previous studies (Kabat et al., 1997; Wallace and Holwill, 1997). The αs parameter may be directly linked to both the soil texture and to the moisture diffusion within the soil (Black et al., 1969; Salvucci, 1997). An empirical expression was suggested by Savabi and al. (1989) which only links αs (mm.day-0.5) to the soil texture fractions (%) respectively for sand (SA) and clay (CL), as follows:

²SA0004.0CL01703.0SA02456.016.4s ×−×−×+=α (6)

When this formulation is applied to a dataset of ten soils in the plains and to two soils in plateaus (Nagumo, 1993), the mean calculated αs value is respectively 3.1 and 3.8 mm.day-0.5 (table 1). The calculated absolute uncertainty is 0.3 mm.day-1/2 (inferior to 10 %). In the sandy millet field, two very short periods under bare soil conditions allow an independent estimate of this αs parameter. It ranges, in the same magnitude, from 3.3 to 3.7 mm.day-1/2 after a strong rainy event (near 30 mm). Table 1 The soil parameters (U, αs) associated with both sandy plains and loamy plateaus.

Sandy plains Loamy plateau U (mm) 6.0 5.6 Percent of sand (%) 88 (± 7) 41 (± 9) Percent of clay (%) 4 (± 1.6) 39 (± 11) αs (mm.day-1/2 ) 3.1 (± 0.3) 3.8 (± 0.3)

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3.3 Vegetation Characteristics Monitoring The characteristics of the plants (fCover, LAI, LAIg) were locally measured in 1992 for

each vegetation type. The main vegetation activity takes place during a 5-month period (DOY 150-300) including the rainy season (DOY 146-264) and the following first month of dry season (figure 2).

Millet is sowed after the first strong rainy event (about 20 mm). It is cultivated in pockets (6-12 plants) spaced at 1 meter intervals. The traditional local species (Pennisetum glaucum) grows in 120 days (Rockström, 1997). In the studied area, the time evolution was monitored by Monteny (1993). In 1992, millet was sowed at the end of June (DOY 182). Plant growth is not significant before mid-August (DOY 218). At millet pocket scale, the green LAI reaches its maximum (2.8 m2.m-2) one month after sowing (DOY 248). During the maximum extension period (DOY 252), the plants cover only 56 % of the field. This percentage characterizes the millet patch. The remaining surface is a bare soil patch. Millet was harvested about three weeks after the last rainy event.

Fallow savannah is composed of two vegetation layers: bushes (Guiera senegalensis) (3-4 m high) and a herbaceous underlayer consisting of annual grass species (20 cm high). Barren old termite mounds appear regularly. The time evolution of the plant characteristics was monitored by Monteny (1993). In such a study, it is assumed that the fallow savannah LAI is the sum of LAI of both grasses and bushes. This assumption leads to a maximum LAI value of 1.2 m2.m-2 at the beginning of October (DOY 274). LAI rapidly decreases after this date. The maximal vegetated area covers 72 % of the overall surface area (DOY 290). This latter value is retained to weigh the bare soil and the vegetated patches.

Tiger bush consists of natural forest bands spaced by large crusted bare soil bands (Ambouta, 1997; Galle et al., 1999). The "forest band / bare soil band" observed ratio is close to 1/3-1/4 in the Eastern Super-Site (Peugeot, 1995; Galle et al., 1997). The bare soil area covers nearly 75 % of the total plateau surface area. The time evolution of LAI was measured in 1995-1996 (Ehrmann, 1999) and was compared to the mean LAI obtained in tiger bush forest in the whole Sahelian area (Hiernaux et al., 1994). Two main species represent 73 % of the total basal area: Guiera senegalensis bush may be active all year long, while Combretum micranthum is strictly limited to the rainy season (Seghieri and Galle, 1999). It is assumed that bush leaves are entirely green (LAI = LAIg). The maximal LAI (2.6 m².m-²) is observed at the beginning of September (DOY 244).


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Figure 2 Time evolution of the total LAI ( ) and the green LAI ( ) for (a) millet, (b) fallow savannah in 1992 (Monteny, 1993), and (c) tiger bush in 1995 (Ehrmann, 1999). Monitoring period for each type of vegetation and the end of the rainy season are also indicated.

3.4 Latent heat Flux Measurements The modeled fluxes are compared to the in situ data based on the turbulent transfer

monitoring, i.e., the Bowen ratio method on the millet crop and the Eddy correlation method on both the fallow savannah and the tiger bush areas. The relative uncertainty is about 10 % at hourly time steps (Wallace, 1991). The spatial resolution is of some square hectares (typically 100×300 square meters). Measurements on millet were made using the Bowen ratio technique on an 8 meter high mast (Monteny, 1993). The measurement period lasts about 2-months

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(Days Of Year 202-263). Two periods can be distinguished: the first when LAI remains negligible and the whole surface can be considered as a bare soil (DOY 202-215), and the second when the millet growth is in place (DOY 223-263). For fallow savannah, the Eddy correlation method was monitored on an 8-meter mast during a shorter period (44 days, DOY 239-292). The latent heat flux measurements were made on tiger bush by using the Eddy correlation technique at 11 meters high, in order to measure the evaporative flux of both the bare soil and the forest bands. Measurement lasted 41 non-consecutive days over a 53-day period (DOY 231-283). Note that the measurement period for millet takes place during the rainy season, while for fallow savannah and tiger bush it also continues during the beginning of the dry season.

4. Model Sensitivity

The model sensitivity was tested on the millet crop as (i) no simplification is made on its surface representation (only one vegetation stratum exits), and (ii) its validation period is rather representative, as it includes both a bare soil and a growing period. This sensitivity test is essential (i) to know the overall behaviour of the model, and (ii) to quantify the possible model error due to parameter and variable uncertainties. The sensitivity of evapotranspiration to both the soil (αs, U) and the vegetation (cover fraction) input parameters was analyzed. Note that soil and vegetation parameters are independent, and the link between αs and U is tiny. The tested variables are the precipitation, the ETP and the LAI. Since total LAI and green fraction of LAI are strongly linked, the fraction of green LAI as a percentage of total LAI is considered here. The relative variation of the flux is plotted according to the relative variation of each parameter (figure 3). In this study, only cumulative output values are taken into account. The range of variation of each parameter and variable is given in table 2. This maximum variation ranges from -80 % to + 50 %. Table 2 Range of variation of the parameters tested on millet crop.

Reference values Range of variation

(in absolute) Range of variation

(in relative)

αs (mm.day-1/2) 3.1 1.0 - 3.8 -68 % / +22 %

U (mm) 6 3 - 7.2 -50 % / +20 %

Fcover (%) 56 40 - 70 -29 % / +25 %

Rainfall (mm) 329.6 260 - 400 -27 % / +21 %

ETP (mm) 288 230 - 350 -20 % / +22 %

LAImax (m2 m-2) 4 0.8 - 4.4 -80 % / +10 %

Green fraction (%LAI) 70 55 - 100 -21 % / +43 %


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Figure 3 Relative variations of cumulated ET during the whole validation period on millet crop according to both relative variation of the parameters and the variables of the model.

4.1 Model Response to the Parameters

The estimated values of αs in the studied area, vary considerably according to the authors. Kabbat et al. (1995, 1997) found the lowest value of 1.0 mm.day-1/2 for αs , in the valley sandy soils. If this value is taken into account, the soil evaporation equals 0.3 mm only two days after rainfall, which appears greatly underestimated given the observed soil evaporation behavior. Bley (1989) stressed a different value of 2.2 mm.day-1/2 in the sandy soils, i.e., 30 % less than our value of 3.1. However, such a difference leads to a low evaporative difference, of less than 4 % for the cumulated ET during the validation period (figure 3). Wallace and Holwill (1997) estimated a similar value of 2.1 mm.day-1/2 for αs, in loamy plateau soils. This latter value was fitted during a single evaporation period after low rainfall.

The two soil parameters (U, αs), in their range of variation, have a similar low influence. Indeed, the maximal cumulated ET change of these two parameters is less than 8 %. A decrease of these parameters implies a decrease in the cumulated ET, and vice versa. A decrease of U tends to reduce the duration of the first phase, when the ET rate is maximum. αs only plays a role in the second phase, which takes place over a short time (about 25 % of the

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period) given the rainfall distribution. The ET relative change of the αs parameter is nearly 1 % for the experimental αs uncertainty (9 % see table 1). This explains why Wallace and Holwill (1997) noted that, despite the difference in soil texture between sandy plains and more loamy plateaus, the evaporation rate of these soils during the second phase appears very similar.

The cover fraction is clearly the most influential parameter. Monteny (1993) reported a relative error of 8-24 % for the cover fraction. Sensitivity remains almost constant with a 0.4 slope. The maximal ET error is of about 10 % for a cover fraction variation of 24 %. Both the plant cover and the cumulated ET react in the same way, even if the soil evaporation and the vegetation transpiration processes can compensate each other. This trend demonstrates that a plant zone evaporates more than a bare soil area.

4.2 Model Response to Variable Uncertainty The model sensitivity was also tested on the environmental variables. ETP appears as the

most sensitive variable. ETP influences both the soil evaporation during the first phase, and the vegetation transpiration for each day. The slope of the ETP variation in the sensitivity analysis equals 0.7. For instance, that slope would be equal to one if only the first phase took place. Slope expresses the proportion between the first and the second phases. When ETP decreases, the first phase takes more time to reach the cumulated U quantity. The number of days of the first phase (atmospheric limited) increases. Another factor is the number of rainy events, which start a new first phase.

The same percentage variation is applied for each rainy event for the rainfall influence test. Time distribution of the rainy events remains unchanged. In the Ritchie model, a new evaporative first phase begins if the rainy event is strong enough. The rainfall quantity over this threshold has almost no influence on the ET rate. For instance, a high rainfall increase (+20 %) has a very low impact on ET (+1 %). Whereas, if rain falls below the threshold, the number of new first phase is then reduced and thus the resulting ET (-4 % ET for -20 % P).

Vegetation characteristics (LAI, LAIg) may have a strong impact on evapotranspiration because of their wide variation range. Indeed, Monteny (1993) reported a high relative error of 15-45 % for both the total and the green LAI. In this study, the influence of the LAIs is tested in two steps. Firstly the maximal LAI varies while the green leaf fraction remains a constant percentage of LAI (70 %). This test shows a low evaporative variation, as an increase of the maximal LAI slowly reduces the soil evaporation and slowly increases the transpiration rate. This parameter, therefore, has a limited impact on the total evapotranspiration, but significantly influences the distribution between the soil evaporation and the plant transpiration. Secondly, an analysis test is made by fixing the maximal LAI and by varying the green leaf fraction (between 55 % and 100 %). The green leaf fraction influence is very


147

similar to that of the cover fraction. Indeed, the transpiration rate is directly proportional to this term, as in the case of the cover fraction. However, this influence is limited by the potential transpiration value for the high green leaf fraction values. 5. Model evaluation

5.1 Daily Evapotranspiration The scatter diagrams of the modeled versus the observed daily ET are described in figure 4

for the three types of vegetation. Table 3 provides the coefficients characterizing each regression curve (r², rmse, slope, intercept). The determination coefficient (r²) characterizes the cofluctuation, while the root mean square error (rmse) quantifies the relation dispersion. Table 3 Regression coefficient between observed and modeled daily evapotranspiration for the three types of vegetation, during their respective validation period (1992).

Millet Fallow savannah Tiger bush Validation Period Days of years 202-263 239-292 231-283 Duration (used day) 55 54 41 Simulation r² 0.59 0.48 0.68 Rmse 0.75 0.96 0.77 Slope 0.89 1.15 0.69 Intercept 0.61 -0.79 0.41

The results of the millet field are divided into two successive periods, respectively, before

the appearance of the canopy (period n°1), and during plant growth (period n°2). Two major points can be highlighted during the first period. Firstly, an overestimation of about 1.3 mm (+ 32 %) is made the day after the rainy event, which corresponds to the first phase. Indeed, the daily ET is estimated as equal to the ETP rate, while the observed soil evaporation never exceeds 75 % of ETP, except after one of the most significant rainy events of the year (34.5 mm) (91 % of the ETP). Secondly, during the second phase, observed soil evaporation is still influenced by ETP contrary that the Ritchie formulation (eqn 2). Once the vegetation grows (period 2), the daily ET is correctly estimated (figure 4a). During this period, the modeled and the observed ET remain very close to each other, and also to ETP. These close results during the second period are due to two main environmental conditions. Firstly, because of both a greater amount of rainy events and stronger rainy events, the number of first phases significantly increases when the soil evaporation rate is at a maximum. Secondly, the transpiration process remains maximal given its high LAI values. Therefore, the total

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evapotranspiration is closer to the ETP value. Unfortunately, the decrease after the last rainy event of the season cannot be evaluated given the lack of latent heat flux observation.

The bare soil period on fallow savannah (figure 4b) is non existent during the validation

period thus the evaporation module cannot be tested on its own. The total LAI always remains superior to 0.5 m2.m-2 during the observation period. An ET overestimation is observed for this vegetation type during phase one. This trend was also highlighted for the millet crop. Conversely, a systematic underestimation is observed during the second phase. As compared with millet field, this can be attributed to limited transpiration, due to the very low LAI values. This trend is also stressed after the last rainy event (DOY 264). During that period, the ET decrease is underestimated by about 0.75 mm per day. This latter period alone explains 80 % of the total cumulative ET error.

The daily ET of the tiger bush was correctly estimated during the rainy season (figure 4c). The simulated daily ET was, however, slightly underestimated (0.5 mm) during the dry season (20 days). This period itself was the reason for the overall underestimation. Note that transpiration became the dominant process after the last rainy event.

The determination coefficient (r2) of all vegetation types, was always greater than 0.48.

The best result was found on the tiger bush site (r2 = 0.68). Rmse of all the sites was always less than 1 mm. Furthermore, both slope and intercept showed no significant bias. Finally, the results are correct according to the simplicity of the model.


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Figure 4 Scatterplot of modeled versus observed daily evapotranspiration (ET) ( ) for millet (a), fallow savannah (b) and tiger bush (c). ET is also plotted during two specific periods ( ): the bare soil period in the millet crop site, and the dry season period in both fallow savannah and tiger bush sites.

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5.2 Cumulated Evapotranspiration The observed and the modeled cumulated ET were compared during the respective

observation period for the three vegetation types (table 4). The millet field ET was overestimated (13 %) while fallow savannah and tiger bush were underestimated (-14 and -18 % respectively). The best results were found for the millet field (+13 %), where the overestimation was mainly due to an evaporative excess during the first phase of the bare soil period. Tiger bush had a similar error (-14 %), which was mainly due to underestimation during the dry season. Finally, fallow savannah had the strongest relative error (-18 %), because of the dry season estimation but also due to a significant underestimation a few days after a rainy event. Therefore, these specific days tended to emphasize a low transpiration rate. It must be remembered that (i) instrumentation uncertainty is about 10 % (Wallace, 1991), and (ii) the spatial inter-sites variability at micrometeorological scale is about 20 %, as reported by Gash et al. (1997) on the three fallow savannah sites. Moreover, the performance of the model varies according to whether the complete validation period includes a part of the dry season or not, since ET is systematically underestimated (after DOY 264). Table 4 Comparisons between observed and simulated water budget (mm) for the three types of vegetation (millet, fallow savannah, tiger bush) in 1992, during (a) their respective complete evaluation period and (b) a common period (DOY 239-263).

Millet Obs. Sim.

Fallow savannahObs. Sim.

Tiger bush Obs. Sim.

(a) Complete Period Rainfall 329.6 114.4 225.6 ET 145.9 165.1 150.7 122.9 120.6 103.7 - Evaporation - 95.1 - 75.0 - 53.4 - Transpiration - 70.1 - 47.9 - 53.3 (b) Common period (DOY 239-263) Rainfall 166 166 166 ET 71 79 78 79 64 59 - Evaporation - 26 - 62 - 35 - Transpiration - 53 - 17 - 24

A model must simulate not only each vegetation behaviour but also their relative difference. With this perspective, a common observation period is selected. The observed and the modeled cumulated ET are compared during a 25-day period (DOY 239-263) by the end of the rainy season. During that period one third of the annual rain falls (166 mm). The model reacts correctly during that rainy period and the cumulated errors are very low. The cumulated


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fallow savannah evapotranspiration is well modeled while the millet field ET is overestimated (11 %) and tiger bush underestimated (-5%). It should be remembered that fallow savannah shows a significant underestimation (–15%) during its complete validation period (55 days), mainly due to the dry period. During the common period the modeled evapotranspiration of the millet field and fallow savannah are similar (79 mm), but the internal distribution is very different. The millet field is at the height of its growing period and transpiration is very significant (53 mm) while soil evaporation dominates on the fallow (62 mm). The lowest flux is observed on the tiger bush (64 mm) and is simulated by the model (59 mm). These results highlight the ability of the model to simulate evapotranspiration during the core of the rainy season. The model validation during other specific periods, such as the dry season or at the beginning of the growing period, should also be performed when data will be available.

5.3 Comparison of Results and Discussion One of the aims of this study is to assess the performance of the Ritchie model in order to

reproduce the daily ET for the three main types of vegetation. The results of this simple model are thus compared to physically based models which solve the equations of both the water and the energy budgets (SVAT). These complex models represent the current most reliable methods to simulate ET.

SISVAT (De Ridder, 1997) and two other surface schemes, ISBA (Goutorbe et al., 1997b) and SiSPAT (Braud et al., 1997), were applied to the same fallow savannah site during the same period. The cumulated ET was overestimated by both SiSPAT (+ 12 %) and SISVAT (+ 9 %) and underestimated by ISBA (- 8 %). The three surface schemes correctly simulate the daily evaporative fluctuations (r² > 0.65). The results of the Ritchie model on fallow savannah are weaker (r² = 0.48, -18 %). The SVAT model, however, requires more than ten parameters (sol, vegetation) and many variables (atmospheric conditions, vegetation). For this reason, no SVAT model was applied to tiger bush.

Amadou et al. (1996) tested Shuttleworth and Wallace’s two-source model (1985) on millet crop. This model was applied during a representative 46-day period, including various vegetation states (bare soil, growing plants, senescence period). The cumulated ET was overpredicted with a relative error of about +12 %, which is very close to that found by the Ritchie model (+13 %). The daily variations (r² = 0.60, rmse = 0.80) are also very close to those estimated here (r² = 0.59, rmse = 0.75). Similar results (r² = 0.57, rmse = 0.64) were found by SISVAT in the same millet field during the same period (Derive et al., subm-b), but a lower cumulated error (+ 5 %) was emphasized. Therefore, on the millet crop, Ritchie’s simple model provides similar and satisfying results at a daily time step.

The internal distribution between evaporation and transpiration can be compared to the SISVAT scheme, as it runs on both fallow savannah (Derive et al., subm.-a) and millet field

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(Derive et al., subm.-b). The transpiration of the millet field, is 13 % superior in the Ritchie model than in the SISVAT model, and evaporation is only 4 % higher. The transpiration of fallow savannah is largely underestimated in comparison with the SISVAT results (-60 %), whereas an overestimation of soil evaporation takes place (+20 %). This strong underestimation of transpiration was observed during the dry season. The linear relationship between LAI and transpiration tends to underestimate transpiration of low cover. The millet has a double LAI and does not suffer underestimation.

Most of the models tend to overestimate ET the day following the rainy event. During the

first phase, the soil evaporation rate seems to depend on the previous rainfall quantity, ranging from 40 % of ETP after a small rainy event (5 mm) to 90 % of ETP after a significant rainy event (superior to 30 mm), as observed in the millet field. Taylor et al. (1997) modulated ETP value with a vegetation limiting factor. Vegetation limiting value is 0.5-0.7 in millet crop, 0.65-0.7 in fallow savannah and 0.85 in tiger bush. These values represent the evaporative fraction of each vegetation firstly when the soil moisture is not limited and when atmospheric demand is dominant (phase one in the Ritchie model). These coefficients are coherent with our estimations. Another cause of overestimation is runoff. One-dimensional models do not take into account the runoff phenomenon, which reduces first the infiltration amount, and then the exfiltration phenomenon (Braud et al., 1997). In the Sahel runoff may be locally enhanced by soil crusting.

The studied millet field grows under very favorable conditions due to the local manure. For instance, the grain production is 1360 kg.ha-1, which is considerably higher than the classical mean yield production observed in Niger (ratio 2-4). Indeed, the LAI reaches high values in comparison with the values for the neighboring fields. For instance, Wallace et al., (1993) measured LAI values that never exceeded 1.7 m2.m-2 during various successive years. Other authors report a maximum LAI of 0.69 m2.m-2 using added fertilizers (Rockström, 1997). Both the use of fertilizers and cultural techniques lead to a strong variability in both the millet growth and the related LAIs. The studied field represents almost a maximum in Sahelian conditions. 6. Annual Hydrological Cycle Simulation

The previous results appear relatively satisfying as they react very positively to the analysis test. The results are, however, limited to a specific period of the hydrological cycle. Therefore, a complete year run was also tested. The simulation began the first rainy day of 1992 (DOY 146) and ended with the first rain of the next season, in 1993 (DOY 149). The simulated annual water budget is described in table 5. No direct evaporative in situ measurement is available at annual scale. The results are then compared to those of studies that use indirect techniques.


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Table 5 Simulated annual water budget for the three types of vegetation (millet, fallow savannah, tiger bush) during 1992.

Millet Fallow savannah Tiger bush Rainfall (mm) 429 429 429 EvapoTranspiration (% of P) 340 (79 %) 364 (85 %) 421 (98 %) - Soil evaporation (% of ET) 244 (72 %) 284 (78 %) 268 (64 %) - Vegetation transpiration (% of ET) 96 (28 %) 80 (22 %) 153 (36 %)

6.1 Annual Evapotranspiration rate The simulated annual ET of the millet field, represents 79 % of the 1992 annual rainfall.

Peugeot (1995) indirectly estimated a higher percentage (88 %) by considering runoff measurements made on a nearby millet field (1992-93). The water balance model SWATRER estimated the annual ET under various rainfall conditions (211-527 mm) in 1950-78 (Bley et al., 1991) and 1989 (rainfall 493 mm) (Fetcher et al., 1991) in Niger. The annual ET ranged from 60 % to 100 % of the annual rainfall. A relationship can be made between the total ET and the annual rainfall by adding the results of different studies in the Sahel (figure 5). A fitted logarithmic expression ( 980)Plog(218ET −×= ), has an accurate explained variance (r² = 0.88). When rainfall increases, the current ET is limited by the atmospheric demand (ETP). Conversely, in an arid climate, ET is equal to rainfall. The rainfall distribution throughout the season causes the scattering around this relationship. The Ritchie model was applied to the millet crop for a period of four complete years. The results found using this model are illustrated in the latter figure (circle). Annual ET represented respectively 72 % (410 mm), 72 % (377 mm) and 52 % (374 mm) of the annual rainfall in 1991 (567 mm), 1993 (523 mm) and 1994 (715 mm). The estimated annual ET for the four years complies with the regression curve. The low evaporative fraction found in 1994 can be explained by the peculiar distribution of the rainy events during the rainy season, as the strongest rainy events took place at very short time intervals.

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Figure 5 Annual evapotranspiration ET (g) plotted against annual rainfall P over several years and in various locations in the Sahel. The circles (n) are the results of the Ritchie simulations.

The simulated annual ET of fallow savannah, is 364 mm (85 % of the annual rainfall). Such a percentage is close to the value estimated by Peugeot (1995) by making runoff measurements (77 %P). The simulated annual ET of the tiger bush is almost equal to the input rainfall (98 %). At global tiger bush system scale, Culf et al. (1993) estimated a similar evaporative rate (95 % of the rainfall in 1990) by considering the Eddy correlation measurements when they were available, or by taking into account the difference between the net radiation and the sensitive heat flux. Bromley et al. (1997) estimated a mean annual aquifer recharge of 13 mm (range 10-19 mm) below a tiger bush plateau, by using the chloride profile method. A similar 8-mm value was found in 1992 at tiger bush scale by using the Ritchie model.

In this study, the simulations highlight that both the millet and the fallow savannah

vegetation types have a similar annual ET (about 80 % of the annual rainfall), while tiger bush evaporates significantly more (about 100 %). The difference is due to the huge transpiration amount conveying the good water harvesting of the tiger bush pattern. Although the millet field grows rapidly due to the local manure, it has a lower latent heat flux (-6 %) than the fallow savannah site, as reported by Gash et al. (1997) who highlighted a similar trend during the core of the rainy season. Peugeot et al. (1997), however, found the opposite result (+7 %) at annual scale, by making runoff measurements. Their two plots were located on sloping areas and the impact of the crust was dominant. Runoff is more significant on the crusted fallow savannah than on the weeded millet field.

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6.2 Partitioning between Soil and Vegetation The distribution between the soil evaporation and the vegetation transpiration at annual

time steps is a significant issue. The annual internal distribution is provided in table 5, while the time evolution of both ET and the plant transpiration is described in figure 6. No in situ measurements are however available to compare the simulated fractions.

The soil evaporation of the millet field represents the main component of the total ET (72

%). This significant percentage, which was also observed by Nourri and Reddy (1991), can be explained by the long bare soil period. Indeed, during the bare soil period, the soil evaporation equals 80 % of the total soil evaporation, which represents about 60 % of the total ET. The soil evaporation of fallow savannah is a main component of the total ET as well (78 %), even if the simulated transpiration is under-estimated as observed within the validation period. For tiger bush, internal ET repartition is divided into transpiration (36 % of the total ET) and evaporation (64 %). The bare soil evaporation equals 229 mm, i.e., 85 % of the total soil evaporation and 53 % of the rainfall. Ehrmann found a similar distribution with a transpiration rate of 40 % at annual scale, and a bare soil representing 78 % of the total soil evaporation (50 % of the rainfall). This latter 50 % value is very similar to the 42 % value estimated by Peugeot (1995) by making runoff measurements in 1993 during a relatively wet year. The forest thicket, which only covers 25 % of the plateau area, contributes to about 50 % of the total plateau evapotranspiration, i.e. twice the local annual rainfall. This is possible as the significant runoff of the bare soil areas over-supplies the downslope forests (Galle et al., 1999): the forest thicket develops a high LAI for this type of climate which leads to a high transpiration rate. The soil evaporation of all the vegetation cover represents the main component of the water budget (from 64 % to 78%).

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Figure 6 Time evolution of both total ET and the plant transpiration for millet (a), fallow savannah (b) and tiger bush (c) in 1992. The validation period is indicated on the abscise axis, and the rainy season between the two vertical lines.

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7. Conclusion Evapotranspiration represents the main component of the surface water budget in the

Sahelian zone. For the first time, a single model was run on the three main types of vegetation (millet, fallow savannah, tiger bush), which represent 89 % of the total area of the HAPEX-Sahel area. Eventhough the methodology is very simple, this approach is considered as both efficient in this Sahelian area and respectable in comparison with sophisticated SVAT schemes. Indeed, the daily ET is globally correctly reproduced by the model (0.48 < r² < 0.68) for the three types of vegetation during the rainy season. At the beginning of the dry season, some low but systematic underestimations can be noticed. These underestimations may lead to significant errors for the cumulated ET. The soil evaporation represents the main contribution to the total evapotranspiration (64%-78%) for any studied vegetation at annual scale. Therefore the systematic overestimation during the first phase can be avoided by using, for example, the vegetation correction coefficient, as suggested by Taylor et al. (1997). Reciprocally, the absence of the water limitation for the plant in the Ritchie model does not lead to a noticeable transpiration overestimation on these low LAI covers. The cumulated evapotranspiration during the 2-month validation period is evaluated with a relative error close to 15 % for the three types of vegetation. The results of the model are encouraging given the severe specific environmental conditions (vegetation heterogeneity, association of various vegetation strata, strong rainy events). Furthermore, this model reacts in a satisfying way in comparison with the results of more complex models such as SISVAT. This is particularly true in the millet field where the relative error is similar to that of the SVAT. The low LAI value of fallow savannah drastically limits the simulated transpiration rate, which is rather underestimated after the last rain. Tiger bush is correctly reproduced even though it is composed of contrasted areas. The annual scale results are coherent with the ones of other independent water balance models, which consider either the runoff or the water stock variations. The soil evaporation at annual scale is the main evaporative component (superior to 63 %), mainly because of its spatial extension. The forest thickets transpirate twice the rainfall they receive. The estimated evapotranspiration at annual scale is higher in the plateaus covered by tiger bush (98 % of the rainfall) than in the valleys, where millet is cultivated along with fallow savannah (79-85 %).

In the future, this model will be a tool to address three main concerns. Firstly, the

quantification of the impact of a potential climatic change in the Sahel. The annual rainfall quantity is, indeed, mainly governed by the number of rainy events (Lebel et al., 1997). In the Sahel, the difference between the wet and the dry years lies in the number of rainy events while the mean rainfall amount does not significantly change (Le Barbé et al., 2002). Thus, during a dry year, longer dry spells take place. This implies a lower evapotranspiration rate as the evaporation rapidly decreases after the first phase (one day), but an increase in the amount of evaporated water as there is more time to get rid of the soil water, which means that the next rain will fall on a dry soil. The second concern is the assessment of the water budget at

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watershed scale. For this, two additional sets of data are required: a spatial distribution of the land cover and a time evolution of LAI of each elementary unit. Remote sensing data may provide this information. With this in mind, the Ritchie model should be coupled with a hydrological model managing the lateral redistribution of the water. The third concern is the impact of anthropic pressure on the evapotranspiration of a watershed. Human intervention has an impact mainly on (i) deforestation of the plateaus, and (ii) reduction of the crop rotation by peasants, which increases the proportion of millet field to the detriment of both fallow savannah and the natural landscapes in the valleys (Loireau, 1998). According to our results, these two changes lead to a decrease in evapotranspiration and thus in the available water in the atmosphere. Acknowledgements The authors would like to thank B. Monteny for providing the ETP data and the EPSAT team for the rainfall data. Judy Byrne kindly accepted to re-read the manuscript and to improve the english language.


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Conclusion Générale et

Perspectives

CONCLUSION et PERSPECTIVES

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CONCLUSION GENERALE ET PERSPECTIVES Les Observations de Terrain

Le bilan des Observations de Terrain d'HAPEX-Sahel

Dans la région sahélienne d'HAPEX-Sahel l'évapotranspiration annuelle représente plus de 75 % des précipitations, et cela sur les trois principaux types de couvertures végétales (mil, jachère, brousse tigrée). Les surfaces cultivées en mil ainsi que les zones de jachère possèdent des taux évaporatifs relativement proches (de l'ordre de 75-90 % des précipitations), tandis que les zones de brousse tigrée évapotranspirent quasiment la totalité de l'eau de pluie reçue (de l'ordre de 95-100 % des précipitations). La différence évaporative entre une surface de mil et une de jachère n'est cependant pas clairement établie à l'échelle de l'année, malgré la nécessité de connaître cette différence pour comprendre le rôle et l'impact des modifications de l'occupation des sols et des couverts végétaux. Par exemple, le rapport entre ces deux couverts varie en fonction de la pente compte tenu de l'important rôle de l'encroûtement. Il est bon de noter que les valeurs d'évapotranspiration observées ont une marge d’incertitude importante étant donné (i) l'incertitude de mesure micro-météorologique (de l'ordre de 10 %), et (ii) la variabilité spatiale observée pour un même couvert entre des parcelles proches (de l'ordre de 20 %). Cette dernière est liée à la variabilité de l'encroûtement, de la fertilité du sol et de l'hétérogénéité des pluies.

Durant l’expérience HAPEX-Sahel, les jeux de données complets pour l’étude des flux évaporatifs se sont avérées limitées aussi bien spatialement (peu de mesures sur les caractéristiques physiques des forêts de brousses tigrées) que temporellement (principalement sur deux mois successifs durant la saison des pluies). Il serait donc appréciable dans le futur d'effectuer des observations de terrain (i) continues dans le temps, c’est à dire sur au moins une année complète, et (ii) sur tous les principaux couverts. De plus, la répartition évaporation-transpiration n'a été que rarement mesurée (seulement quelques jours), compte tenu de la difficulté même des mesures. Par conséquent, les modélisations n'ont pu être validées que sur des périodes courtes représentant seulement une partie des conditions environnementales, c'est à dire au cœur de la saison des pluies lorsque le stock en eau est maximal et donc apparaît moins comme un facteur limitant. On a cependant pu noter que l’évaluation des modèles est moins bonne en tout début de saison sèche ce qui laisse supposer des problèmes spécifiques à cette période, que ce soit avec le schéma de surface SISVAT sur la jachère (surestimation de l'évapotranspiration) ou avec le modèle de Ritchie sur la jachère et la brousse tigrée (sous-estimation de l'évapotranspiration). Des données supplémentaires sont nécessaires pour approfondir cette analyse.

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166

Les prochaines Expériences de Terrain : l’ORE AMMA-CATCH

L'ensemble de ces travaux de recherche s'est focalisé en zone sahélienne à partir des données recueillies durant l'expérience HAPEX-Sahel qui s'est déroulée au Niger principalement au cours de l'année 1992. Les mesures de suivi à long terme (10 ans) sur cet observatoire concernent les autres termes du bilan hydrique : les précipitations, les hauteurs de mare, les hauteurs de nappe. Cette longue période d’observation ne peut donc apporter des estimations d’evapotranspiration que comme résultante du bilan hydrologique et seulement sur des surfaces sur lesquelles le bilan peut être bouclé. Compte tenu des nombreuses hypothèses qui sous-tendent un tel bilan, les résultats seront grossiers. Cela est regrettable compte tenu des enjeux climatiques qui ont été largement exposés et des nombreuses questions qui restent en suspens.

Dans ce contexte a été proposé un Observatoire de Recherche pour l'Environnement (ORE) dédié à l’Afrique de l’Ouest et ayant pour objectif l’étude du Couplage entre l'Atmosphère Tropicale et le Cycle Hydrologique (CATCH). L’observation et la simulation des flux de chaleur latente en zone sahélienne et soudanienne font partie des objectifs de ce programme. De façon plus générale cet observatoire participe au programme international d’Analyse Multidisciplinaire de la Mousson Africaine (AMMA) qui regroupe des atmosphériciens, des chimistes et des hydrologues. L’ORE CATCH étudie un transect sahélo-soudannien (200 à 1300 mm de pluviométrie annuelle). Des observations à long terme (2001-2010) du cycle de l’eau continental (pluie, débit, nappe, ETP) seront complétées par une période de mesures intensives (2004-2005) au cours de laquelle des mesures de flux de chaleur latente sont prévues (mâts micrométéorologiques, scintillomètre, télédétection). Au cours de cette expérience de terrain, il semble important de prévoir dès aujourd’hui le suivi d'un cycle hydrologique complet (début et cœur de la saison des pluies, ainsi que le début de la saison sèche) et de le compléter si possible par des mesures simultanées d'évaporation et de transpiration à partir de mesures de flux de sève. Le schéma de surface SISVAT

D'excellentes performances à l'échelle micro-météorologique

Le schéma de surface SISVAT a correctement reproduit les bilans hydrique et énergétique à l'échelle de quelques hectares sur les deux végétations testées (mil et jachère) (Derive et al., sub.-a; Derive et al., subm.-b). La réponse du modèle est excellente dans ce milieu sahélien sur les deux végétations pourtant bien différentes que sont la jachère (association de deux strates végétales, LAI toujours faible, couverture étendue, encroûtement permanent) et les champs de mil (LAI élevé, couverture faible, sarclage limitant l’encroûtement). De plus, les périodes suivies ont représenté des conditions variées, allant d'une zone de sol nu en période de pluie, à une zone végétalisée durant la saison sèche. Par conséquent, le schéma de surface


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SISVAT peut être considéré comme relativement fiable et robuste sur cette zone sahélienne.

Les flux de chaleur latente ont été au centre des discussions puisqu'ils influent sur le modèle atmosphérique de méso-échelle nommé MAR qui simule en retour le phénomène de mousson. L’évolution temporelle des flux évaporatifs a été particulièrement bien reproduite, au pas de temps de 20 minutes (r² > 0.80) et correctement à l'échelle de la journée (r² > 0.57). Sur l'ensemble des périodes d'évaluation des végétations étudiées, l'excès sur le cumul d'évapotranspiration n'a jamais dépassé 9 %. Cette erreur est plus qu'acceptable si on la compare à celle obtenue par d'autres modélisations, comme celle du modèle SiSPAT (+ 12 %). Cependant certaines spécificité du Sahel mériteraient d’être prise en compte. Ce sont pour la végétation l’hypothèse d’un taux de couverture végétale constant et la paramétrisation de la fonction de stress, dans cette région où la végétation doit réagir rapidement pour répondre à des stress répétés. D’autre part, la présence de croûtes à la surface du sol modifie ses capacités hydrauliques d’infiltration et d’évaporation en jouant le rôle d’une impédance. Ces trois spécificités devraient être prises en compte dans une perspective d’amélioration du schéma de surface qui reste un compromis entre une réalité complexe et la connaissance de nouveaux paramètres qu’elle impose. Dans une perspective de couplage avec le MAR, ces paramètres devront être fournis sur l’ensemble de la région sahélienne. L’imprécision sur la détermination des paramètres peut affecter largement les résultats du modèle. L'influence des onze paramètres du modèle a été testée sur les cumuls des flux évaporatifs (sol, végétation, total). L'évaporation du sol est particulièrement sensible à deux paramètres d’échelle des caractéristiques hydrodynamiques (la conductivité hydraulique à saturation et la succion à saturation), tandis que la transpiration des plantes dépend principalement du facteur d’échelle de la résistance stomatique du couvert (la résistance stomatique minimale). La modification du taux de couvert végétal entraîne également des écarts significatifs, tout comme les incertitudes sur la mesure de LAI. Il faudra donc par la suite apporter une attention toute particulière à l'évaluation de ces entrées du modèle, et à leur variabilité spatiale.

Les prochaines étapes pour SISVAT

Les résultats de cette évaluation sont cependant limités par trois principaux points. Tout d'abord, les évaluations produites ne se sont déroulées qu'à partir d'un jeu de données réparti sur une période limitée d'environ deux mois. Par exemple, on n'a pas pu évaluer la réaction du modèle en début de saison des pluies qui apparaît comme potentiellement important pour la suite de la saison des pluies. En effet, en début de saison des pluies, l’évaporation du sol pratiquement nu et sec est directement liée à la dernière pluie, ce qui crée des contrastes spatiaux d’évaporation plus marqués qu’en fin de saison lorsque la végétation peut exploiter une couche de sol plus profonde avec un stock plus élevé et plus régulier. Les flux de chaleur latente en début de saison induisent une humidité initiale de l’atmosphère qui pourrait


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expliquer les gradients pluviométriques systématiques observés lors de la saison des pluies (Taylor et al., 1997). Deuxièmement, seulement deux types de végétation ont été évalués. Pour achever cette évaluation en zone sahélienne, il serait indispensable de tester SISVAT sur le troisième type de végétation qui recouvre près de 28 % de la superficie de HAPEX-Sahel, à savoir la brousse tigrée. Cela reste cependant difficile car les paramètres caractéristiques de cette végétation ne sont pas tous disponibles. Enfin, il faut encore valider ce modèle sur l’ensemble des climats de la zone de circulation de la mousson en Afrique de l’Ouest. En particulier la zone soudanienne caractérisée par une pluviométrie pratiquement double et une végétation plus dense et plus variée. C’est un des objectifs de l’ORE AMMA-CATCH.

SISVAT, le MAR et la Mousson Ouest Africaine …

Dans le cadre du couplage avec le modèle atmosphérique MAR à l’échelle de l’Afrique de l’Ouest, le schéma de surface SISVAT fonctionne avec une résolution de 40�40 km2. A cette résolution, deux différences majeures apparaissent : (i) les paramètres de surface sont définis non plus à partir de mesures locales mais d’après des classifications prédéfinies dans des tables adaptées, et (ii) la maille élémentaire du modèle comprend un patchwork de couverts végétaux, de types de sols, de zones en pente ou planes et de conditions environnementales (précipitations, aéronomie) différentes. L'utilisation des paramètres tabulés a clairement modifié les performances du modèle. L’évapotranspiration simulée qui était plutôt surestimée (+3% / +9%) est diminuée avec les paramètres tabulés (-27% / +1%) sur la période testée. Là encore, la période de saison sèche a accumulé toutes les erreurs. Le second point concerne l’agrégation d’un paysage complexe en une classe de végétation type, et son impact sur les performances du modèle. A cette échelle seule la simulation peut apporter une réponse. Le couplage de SISVAT avec un modèle hydrologique décrivant toutes les unités élémentaires et leurs échanges à l’intérieur d’une maille de 40�40 km2 devra être comparé à la maille homogène équivalente du modèle opérationnel. A cette échelle, la télédétection fournira la description spatiale des états de surface. Le couplage de tels modèles est l’objet de la thèse de C. Messager en cours au LTHE. Le modèle conceptuel de Ritchie

Un modèle simple, mais robuste

Le modèle conceptuel de Ricthie a donné de très bons résultats et ceci sur les trois principaux types de végétation (mil, jachère, brousse tigrée). Les fluctuations d'évapotranspiration ont été bien reproduites (0.48 < r² < 0.68) sur la période de validation et à l'échelle de la parcelle. L'erreur sur le cumul simulé est plutôt correcte, de l'ordre de 15 %. Malgré la simplicité de son concept, ce modèle apparaît donc comme relativement fiable et robuste. Ses résultats sur le mil se sont même avérés comparables à ceux provenant de modèles de type SVAT. Il est cependant bon de noter que ces résultats ont été majoritairement


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obtenus dans des conditions hydriques favorables, principalement au cœur de la saison des pluies. Les quelques simulations en période sèche ont généré des différences notables, particulièrement sur la jachère. Les résultats ont montré que le module d’évaporation du sol nu est à améliorer, compte tenu de sa surestimation après la pluie. Le module de transpiration n’est pas réaliste dans la mesure où il ne prend pas explicitement en compte la limitation en eau du couvert.

Malgré ces réserves et compte tenu des résultats encourageants donnés par le modèle sur

les périodes de validation, des simulations annuelles ont été menées. L'évapotranspiration annuelle simulée est très proche des valeurs estimées par d'autres moyens (mesures indirectes, modèles). Sur l’année, l'évaporation du sol représente le terme majoritaire (64-78 % de l'évapotranspiration), et cela pour les trois types de végétation, même si la fraction de transpiration est relativement plus forte sur la brousse tigrée. Tous les types de végétation participent encore activement à l'évapotranspiration un mois après la dernière pluie (13 % ET). C’est également la période ou la transpiration est supérieure à l’évaporation (60-75 % ET).

Les avantages de la simplicité …

Le modèle de Ritchie a correctement reproduit les flux évaporatifs sur les trois principaux types de végétation à l'échelle annuelle. Il est par conséquent possible d'appliquer ce modèle dans deux cas qui intéressent l'hydrologue et le climatologue, à savoir :

• Ce modèle peut être appliqué très facilement à l'échelle hydrologique du bassin

versant. Une étude préliminaire a été faite sur le bassin endoréique de Samadey (6.1 km²) en estimant le LAI à partir d’une série d’images SPOT (Derive et Galle, 2001, en annexe). Les caractéristiques des sols sont attribuées en fonction de leur position géomorphologique (plaine ou plateau). Les flux de chaleur latente ainsi estimés montrent que sur l’ensemble du bassin qui est couvert d’une végétation de faible LAI (maximum inférieur à 2.5 m².m-²), l’évaporation du sol nu est prépondérante. Seuls les champs de mil situés aux abords de la mare atteignent un LAI suffisant pour avoir une transpiration notable. Les conclusions sur cette étude restent cependant limitées puisqu’on n’a pas pris en compte le ruissellement qui redistribue l’eau sur le bassin, si ce n’est de façon indirecte à travers le LAI du couvert. Les ordres de grandeurs à l’échelle du bassin sont cependant réalistes si on compare l’estimation de la recharge de la nappe en 2002 (85 mm) à celle de Esteves et Lenoir (1996) sur les ruissellement observés sur le même bassin (138 mm) ou aux mesures de hauteur de nappe de Leduc et al. (2001) sur le degré carré (entre 10 et 50 mm.an-1).


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• Le modèle de Ritchie permet de simuler la relation entre le cumul pluviométrique et l’évapotranspiration annuelle ainsi que l’influence de la répartition des évènements pluvieux pour une année donnée. Pour cela une représentation schématique de la saison des pluies a été choisie. On sait que c’est principalement le nombre d’évènements convectifs qui influe sur le cumul pluviométrique annuel au Sahel plutôt que le cumul de chaque évènement pluvieux (Le Barbé et al., 2002) et que ces événements proviennent de systèmes convectifs organisés (Mathon, 2001). Dix ans de données du réseau de pluviographes (1991-2001) ont d’autre part permis à Balme et al. (subm.) de vérifier que 90 % du cumul pluviométrique annuel tombe en 105 jours (± 13 jours). Les années sèches peuvent donc être décrites comme des années où peu de systèmes convectifs organisés balaient le Sahel, ce qui implique que leur espacement augmente, c’est à dire que les périodes sèches inter-événement augmentent. Pour simuler des cumuls annuels différents, des évènements pluvieux identiques (13 mm) ont été répartis tous les 2, 3 ou 4 jours sur une durée totale de 105 jours. On voit qu’au delà de 300 mm lorsque la pluie augmente l’évapotranspiration augmente dans une moindre mesure (le rapport ET/P diminue), au bénéfice du stockage dans le sol et du drainage vers la nappe (figure 1). Malgré la simplicité du modèle et des hypothèses testées, il est remarquable de voir que le modèle simule des évapotranspirations comparables aux mesures faites sur des champs de mil par différents auteurs au Sahel.

Figure 1 : Relation entre la pluviométrie et l’évapotranspiration annuelle simulée par le modèle de Ritchie à partir de pluies schématisées (trait plein) et de mesures effectuées par différents auteurs sur des champs de mil au Sahel (carrés).


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Vers une compréhension intégrée des processus …

Si les pluies sur le Sahel diminuent dans le prochain siècle comme le laissent prévoir les résultats actuels des modèles climatiques, l’impact sur l’évapotranspiration est prévisible par des relations de ce type qui peuvent et doivent bien entendu être affinées notamment en couplant les schémas de surface à la fois avec des modèles atmosphériques et hydrologiques. Ce qui permettra de comprendre le cycle de l’eau, en suivant le parcours du flux d'eau de la surface (flux de chaleur latente) à l'atmosphère (humidité de l'air), jusqu’au déclenchement des évènements convectifs (saturation de l'air), et aux précipitations observées au niveau du sol, puis leur redistribution sur un bassin versant par infiltration et ruissellement. Il serait alors possible de tester la sensibilité du système de mousson aux processus de surface et de quantifier l'impact de leur rétroaction qui a souvent été mis en avant, mais trop peu souvent quantifié.

Références Balme, M., Lebel, T. et Galle, S., 2003. Démarrage de la saison des pluies au Sahel :

variabilité à des échelles hydrologique et agronomique. Submitted to Sécheresse. Esteves, M., et F. Lenoir F., 1996. Un exemple de fonctionnement hydrologique dans la

région de Niamey : le bassin de SamaDey., Interactions surface continentale / atmosphère : l'expérience HAPEX-Sahel, 10th hydrological days, Montpellier, 13-14 Septembre 1994, p.225-240.

Le Barbé, L., Lebel, T. et Tapsoba, D., 2002. Rainfall variability in West Africa during the years 1950-1990. Journal of Climate, 15(2), 187-202.

Leduc, C., Farreau, G. et Schroeter, P., 2001. Long term rise in a sahelian water-table : the continental terminal in South-West Niger. Journal of Hydrology, 243, 43-54.

Mathon, V. , 2001. Etude climatologique des systèmes convectifs de méso-échelle en Afrique de l'Ouest. Thèse de doctorat, MSE Montpellier (France), 238 pp.

Taylor, C.M., F. Said, et T. Lebel, 1997a. Interactions between the land surface and mesoscale rainfall variability during HAPEX-Sahel. Monthly Weather Revue, 125, 2211-2227.


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Annexe :

A simple method to estimate evapotranspiration in semi-arid areas

using remote sensing data. A case study in the Sahel (Niger).

( IAHS publication )

A SIMPLE METHOD TO ESTIMATE EVAPOTRANSPIRATION IN SEMI-ARID AREAS USING REMOTE SENSING DATA.

A CASE STUDY IN SAHEL (NIGER).

Gaël Derive - Sylvie Galle

LTHE (Laboratoire d'Etude des Transferts en Hydrologie et Environnement)

B.P. 53, Grenoble, 38041, France (33) 4-76-82-82-50, [email protected]

( IAHS publication - Montpellier 2001 )

ABSTRACT In the sahelian area, the EvapoTranspiration (ET) flux represents the main component of the water balance because of the high temperature and solar radiation along the year. Therefore, it is necessary to estimate the spatio-temporal variations of the ET flux but also its partition between soil evaporation and vegetation transpiration to understand their respective roles. Given the few available in situ data, the Ritchie's model was selected as few input data are required. This methodology is tested using the HAPEX-Sahel (Niger) data obtained in 1992. Firstly, punctual tests (1-D) are performed against measurements for each of the two main vegetation types in the sahelian plains : millet field and fallow (r²=51% and 58% respectively). The annual estimation of AET represents more than 78% of the annual rainfall for both millet and fallow. As the LAI never exceeds 3, the soil evaporation plays the major role (63-73%) within the total ET. Secondly, the methodology is extended (2-D) to a small endoreic watershed (6.1 km²) which supplies a central pool. The LAI is estimated using the remote sensing data (SPOT, 20m). The estimated annual ET (84% of the annual rainfall) enables us to estimate ground water recharge (85 mm). This value is in correct agreement with independent direct measurements of water table recharge (138 mm).

INTRODUCTION The sahelian area is characterized by annual rainfalls (200-700 mm) concentrated in one rainy season (May to September). Given the high air and soil temperature leading an important evaporative demand of the atmosphere [Wythers et al., 1999], the main component of the water budget is EvapoTranspiration (ET). Therefore, it is necessary to quantify this flux, representing more than 2/3 of the annual rainfall [Eagleson, 1991; Peugeot, 1995]. Additionally, it appears essential to identify both soil and vegetation effects to understand their respective roles [Wallace et al, 1990; Wallace, 1991] within the hydrological balance. Our objective is to estimate the spatial and temporal evolutions of evaporation and transpiration on a watershed in Niger. Very few data are generally available in this area. Therefore, we will choose a simple robust model (Ritchie, 1972). This paper presents the model's evaluation using the data obtained during the HAPEX-Sahel (Hydrological-Atmosphere Pilot EXperiment in the Sahel) [Goutorbe et al., 1997] experiment in the square degree of Niamey in the south of Niger (fig. 1). This area typically represents a sahelian area with sandy soil covered by sparse vegetation (millet field, fallow) in dominant gentle slope plains (72 % of the total superficy) and more loamy soil on plateaus (28 %) covered by natural forest called 'tiger bush'. The experimental site was located in the East Central

SuperSite (ECSS), about 70km east from Niamey, near the SamaDey village. After comparison with local measurements on millet field and fallow, the model ran on a small endoreic watershed using remote sensing data for LAI estimation.

Figure 1 : Geographical localization of the square degree of Niamey (2-3°East,13-14°North) in south Niger (West Africa).

METHODOLOGY The conceptual Ritchie's model [1972] separates both bare soil evaporation and vegetation transpiration using the Leaf Area Index (LAI). This variable, varies both on space and time. It will be estimated with a simple relation using remote sensing data.

Bare soil Evaporation The bare soil evaporation takes place in two distinct phases from a rainfall event (fig. 2). The first part is atmosphere limited, the second is soil water limited.

Figure 2 : Two phases of bare soil evaporation : the first one when soil evaporation (Es) equals potential soil evaporation (Es0) (atmosphere limited), and the second one when evaporation decreases as a function of time. During the first phase, the soil evaporation (Es1) equals potential soil evaporation (Es0) (equ. 1-2) as soil water is not limited. Since the last rainfall event, this phase has been lasting until time t1 when the total amount of evaporated water equals U. The U parameter ranges between 3-5 mm in sandy

soil to 14 mm in sandy clay loam [Daamen et al., 1993; Lascano, 1991]. For instance, this first phase lasts 1 day in the tiger bush [Wallace and Holwill, 1997] in Niger. The radiation attenuation due to the vegetation is expressed by a Beer's law (equ. 2). In this expression, the extinction parameter kEXT equals 0.4 [Ritchie, 1972].

UEs Est1t

0t01 =∑ ∑=

=

= ( 1 )

( ).LAIk-ETP.expEs EXT0 = ( 2 ) After the end of the first phase, the cumulative soil evaporation (ΣEs2) decreases as the square root of time (t) until the next rainy event (equ. 3). This approach has recently been used in tiger bush [Wallace and Holwill, 1997].

∑ α= t.Es s2 ( 3 ) The evaporation of the second phase is proportional to an hydrodynamic soil parameter (αs) mainly depending on the soil type and the moisture diffusion within the soil [Black and al., 1969; Salvucci, 1997].

Vegetation Transpiration The plants transpiration depends on the leaf area index (LAI). For low value (0-3), the transpiration is limited whereas for higher values, it equals potential transpiration (equ. 4).

−

×= EsETP;

3LAIgETPminEv ( 4 )

This formulation does not take into account the water limitation which can be important in the sahelian area. Therefore a limitation should be performed as soil moisture becomes a limiting factor. Some authors take into account this formulation but they introduce an extra variable : the soil moisture [Bouraoui, 1995]. AVAILABLE DATA AND PARAMETERS ESTIMATION

Environmental (P, ETP) characteristics Only two daily meteorological variables are required (fig. 3) : rainfall (P) and Potential EvapoTranspiration (ETP). The rainfall estimations were measured during the EPSAT project (Estimation des Pluies par SATellite) [Lebel et al., 1992] with 5-minutes temporal resolution. In this sahelian zone, 90% of the annual rain falls during one rainy season from May to September. In Niamey, the average annual rainfall on a 85-year period (1905-1989) equals 563 mm [Le Barbe and Lebel., 1997]. In the studied area, the annual rainfall in 1992 was 428 mm characterizing a dry year. The high spatial variability of the seasonal rainfall can bring a huge local difference. For example, in 1992 in the South of the square degree, Lebel et al. [1997] shows a difference of 54% between the extremes values of annual rainfall, approximately at a 10-km distance. The average rainfall intensity is 35 mm.hour-1 during a rainfall event. One third of total precipitation falls with an intensity of more than 50 mm.hour-1. The daily Potential ET (ETP) was estimated using the Penman [1948] equation including net radiation, air pressure and wind speed measurements. In 1992, the annual ETP was 1968 mm close to the average value of 2057 mm from 1953 to 1962 [Wallace et

al., 1990]. In all cases, the annual ETP represents near 4 times the annual rainfall, divided into both equilibrium (80%) and aerodynamic (20%) parts.

Figure 3 : Evolution of the daily environmental conditions in 1992 : rainfall (from May to September) and ETP always greater than 4 mm.

Soil characteristics (αs) In the HAPEX-Sahel area, the soil can be split into two main soil types : deep reddish brown sandy soil located on hill-foot slope and pediplain, and shallow reddish brown clayey soil on plateau [Nagumo, 1993]. The soil types are characterized by contrasted texture : sandy soil in plains and sandy loam on the plateaus (table 1). As soil textural properties plays a major role in the hydrodynamic behavior, an empirical relation was suggested to link soil texture and the αs soil parameter [Savabi and al., 1989].

²SA0004.0CL01703.0SA02456.016.4s ×−×−×+=α ( 5 )

Where SA and CL are respectively the sand and clay fraction (%) of the soil. Using this relation, the αs values (table 1) are very close to those given in the literature by previous studies on the similar soil type [Black and al., 1969; Ritchie, 1972; Lascano, 1991]. The uncertainly of αs on each soil type is estimated to 9 %.

sandy plains loamy plateaus % sand 88.0 ( ± 6.6 ) 41.5 ( ± 9.4 ) % clay 3.9 ( ± 1.6 ) 38.8 ( ± 11.6 ) αs (mm.day-1/2) 3.15 ( ± 0.27 ) 3.83 ( ± 0.28 )

Table 1 : Textural characteristics of plain and plateau (after Nagumo) and associated value of the soil parameter αs.

Vegetation characteristics (f, kG, LAI) The three main vegetation types are millet field, fallow and tiger bush. The millet canopy locally reaches high LAI (near 3-4) due to the local manure. Its covering never reaches more than 50% of the global field area. Other authors measured similar temporal evolution but LAI never exceeded 0.44 without fertilizer [Rockström, 1997]. The fallow is essentially composed of tree (Guiera Senegalisis) with 2-4 meters high, associated with annual grasses (20 cm high). The average density

is near 1 tree on 10 m² [Seghieri and Simier, in press]. Its LAI never exceeds 1.5 but its covering can reach 70%. The tiger bush is composed of alternence of forest band (Combretum micranthum and Guiera senegalensis) (25%) and bare soil (75%) which enables for optimal utilization of limited and highly variable water supply [Galle et al., 2001]. The trees leafing (fig. 4) begins in June slightly before the first rain for tiger bush, and one month later for fallow. The millet is sowed in the beginning of July after the first 10 mm rainfall event and does not reach a significant LAI before August.

Figure 4 : Evolution of leaf area index (LAI) for the three main vegetation types : the millet field (blue diamond), the fallow (green triangle) and the thicket of tiger bush (pink circle).

PUNCTUAL VALIDATION (1-D) The model is tested locally on two sites monitored during the HAPEX-Sahel experiment. The fluxes were measured using the Bowen ratio method and have been compared to the estimations obtained from the Ritchie's model on both millet and fallow (fig. 5).

Figure 5 : Correlation in 1992 between daily observed and estimated ET over the two vegetation types : millet field (a) and fallow (b).

The Ritchie's model provides correct results on millet and fallow (table 2). The overestimation on millet field is mainly due to the Ritchie's soil module. After a rainfall event during the first phase, the soil evaporation is assumed to be equal to potential soil evaporation, while the measurements are fairly lower. The soil moisture depletion is not taken into account for vegetation in the Ritchie's model has not an important impact during this observation period. On the fallow site, the Ritchie's model slowly underestimates the fluxes measured using the Bowen ratio method. On this bushy area composed of heterogeneous vegetation, the Bowen ratio overestimates the fluxes compared to the Eddy correlation measurements.

n r² RMSE slope intercept Millet 60 0.51 0.83 0.89 0.49 Fallow (Bowen) 41 0.58 0.74 0.70 0.76 Fallow (Eddy) 54 0.56 0.78 0.66 1.34

Table 2 : Results of ET estimations in 1992 in both millet and fallow site. RMSE is expressed in mm.

The annual AET rate equals respectively 78% of the annual rainfall on millet field and 101% on fallow (table 3). The soil evaporation represents the major component in the annual water balance respectively with 73% of the total AET in millet field and 63% on fallow. The annual water balance provides realistic estimation for millet field compared to other studies (fig. 6) performed in the Sahelian zone (Senegal, Mali, Niger).

Annual water balance (mm) Millet Fallow Rainfall 429 mm

AET (%P) 334 (78%) 434 (101%) Soil Evaporation (%AET) 245 (73%) 274 (63%) Vegetation transpiration 89 (27%) 160 (37%) Resulting runoff (%P) 95 (22%) -5 (-1%)

Table 3 : Punctual estimation of annual water balance in 1992 for both millet and fallow.

Figure 6 : Relation between annual AET and annual rainfall for a millet field in the sahelian area (200-700 mm).

WATERSHED ESTIMATION (2-D)

Watershed Description This endoreic watershed is located near the SamaDey village. The general description and classification of this watershed (soil, vegetation, slope) was given by Esteves [1996] (fig. 7). This small watershed (6.1 km²) is representative of this region, with a gentle slope about 0-4%. In sandy plains, the surface is divided into millet field (32% of the total area), fallow (32%) and spreading area (11%) surrounding the central pool. In more loamy plateaus, the vegetation is exclusively composed to tiger bush (25% of the watershed) divided into bare soil zone (3/4) and forest thicket (1/4).

Figure 7 : Soil and vegetation classification in the Samadey watershed : millet field (yellow), fallow (orange) and spreading area (blue) in sandy plains, and tiger bush in more loamy plateaus (brown) [Esteves, 1996]. The central pool where water concentrate is in green.

Model Implementation The ETP values were measured in the Banizoumbou village (about 10 km). The rainfall was gauged in the center of the Samadey watershed. The LAI evolution throughout the rainy season is performed using seven Spot scenes. The Leaf Area Index (LAI) is deducted from a Vegetation Index (VI) through a modified Beer's law [Baret and Guyot, 1991] :

( ) [ ]LAI.kexp.VIVIsVIVI VI−−+= ∞∞ ( 6 ) where VIs represents VI for bare soil, VI∞ the asymptotic value of VI when LAI tends toward the infinity (practically this limit is always reached when LAI is greater than 8-10), and kVI the extinction coefficient. The chosen vegetation index is the NDVI (Normalized Difference Vegetation Index) [Rouse et al., 1974]. The chosen extreme values are respectively 0.1 for bare soil (VIs) and 0.9 for full canopy (VI∞). The kVI coefficient was calibrated by comparing both satellite and in-situ LAI measurements performed during the HAPEX-Sahel experiment. This coefficient equals 0.05

for the two vegetation types (millet, fallow). It is equally applied for tiger bush thicket, composed of similar bushes species. Finally, a linear extrapolation is used between each punctual daily values in order to obtain the annual LAI time evolution.

Figure 8 : Comparisons between observed and estimated LAI respectively for the millet field (a) and the fallow (b).

Results and Discussion The model is applied in 1992 on the Samadey watershed. It demonstrates a high evapotranspiration (84% of the annual rainfall) mainly composed of soil evaporation (78% of the annual AET). Except around the pool where water concentrates and well developed vegetation grows, the LAI hardly reaches 2.5. In the major part of the watershed the soil evaporation is dominant (Fig. 9). At annual scale, the difference between rainfall and evapotranspiration equals the deep drainage or groundwater recharge (table 4). This resulting value (85 mm) is close to the independent estimation of the recharge (138 mm) based on pool level and surface runoff monitoring performed by Esteves and Lenoir [1996]. This local value is higher than the annual groundwater recharge of the square degree estimated by Leduc et al. [2001] respectively of 10 mm and 47 mm in 1997 and 1998. The Ritchie model provides a realistic estimation of the evapotranspiration in this sahelian watershed.

Annual water balance (mm) Esteves (1996) Ritchie Rainfall 524 mm AET 364* (73%) 439 (84%)

Soil evaporation - 342 (78%) Vegetation transpiration - 97 (22%) Water table recharge 138 (27%) 85* (16%)

Table 4 : Comparisons between Ritchie estimations and annual water balance of the Samadey watershed estimated by Esteves and Lenoir [1996]. * resulting data.

Figure 9 : Spatial distribution of annual AET (a), soil evaporation (b) and vegetation transpiration (c). Temporal evolution of both soil evaporation and vegetation transpiration (d). CONCLUSION AND PERSPECTIVES The Ritchie's model successfully estimates the evapotranspiration flux on a punctual scale (millet and fallow) in 1992 and provides realistic estimations on the small Samadey watershed. In all cases, AET represents the major term within the water balance with more than 78% of the annual rainfall. Moreover, the main part in the AET is the soil evaporation which reaches close to 70% of the global annual AET. Therefore, the soil evaporation module plays the major role. In the next step, the tiger bush contribution, covering 25% of the total area of the watershed, must be tested against measurements. This simple approach may also be compared to a more sophisticated model (SISPAT). In a final step, the model will allow us to quantify the impacts of a climatic change due to i) the modification of the number of rainy events as observed by Le Barbe et al. (in press) ii) or the anthropic pressure which leads peasants to reduce the crop rotation [Loireau, 1998] and therefore increases the proportion of millet field in the landscape.

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