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FABIENNE MATHIEU
ÉVOLUTION DE LA TRANSPIRATION APRÈS COUPE DANS LA SAPINIÈRE À BOULEAU BLANC,
FORÊT MONTMORENCY, QUÉBEC
Mémoire présenté à la Faculté des études supérieures de l’Université Laval
dans le cadre du programme de maîtrise en Sciences forestières pour l’obtention du grade de Maître ès Sciences (M.Sc.)
DÉPARTEMENT DES SCIENCES DU BOIS ET DE LA FORÊT FACULTÉ DE FORESTERIE ET GÉOMATIQUE
UNIVERSITÉ LAVAL QUÉBEC
2006 © Fabienne Mathieu, 2006
ii
Résumé Des modèles empiriques ont été développés pour prédire la transpiration de quatre
peuplements de sapin baumier (Abies balsamea L.) de la forêt Montmorency, Québec, à
partir de mesures heat pulse velocity (HPV) et de mesures dans une chambre ventilée. La
réponse à la blessure due à l’installation de la sonde HPV dans l’arbre expliquait 16% de la
variabilité des mesures HPV dans le peuplement testé. Un facteur de correction polynomial
est proposé pour l’atténuation du signal HPV dans le sapin baumier. Les taux de
transpiration des quatre peuplements étaient tous significativement différents. Les
coefficients d’atténuation des effets hydrologiques de la coupe (Ca) ont été calculés et des
relations ont été ajustées en fonction de propriétés des peuplements. Les Ca étaient
hautement corrélés avec l’âge, la hauteur et en particulier avec la surface terrière. La
fonction de Ca indique qu’une récupération hydrologique de 50% est atteinte 16 ans après
la coupe. Les valeurs estimées de Ca basés sur la transpiration étaient similaires à celles
obtenues à partir des mesures de fonte de la neige pour les peuplements de 2, 12 et 22 ans.
iii
Avant-Propos Ce mémoire comprend trois chapitres écrits sous forme d’articles. Ces articles ont été ou
seront soumis à des journaux scientifiques avec MM. André Plamondon (Chap. 1, 2 et 3),
Pierre Bernier (Chap. 1, 2 et 3) et Denis Lévesque (Chap. 3) comme co-auteurs.
L’article composant le Chapitre 1 intitulé « Relating canopy conductance to environmental
variables in two balsam fir (Abies balsamea L. Mill) stands » a été coécrit par Fabienne
Mathieu, M. Pierre Bernier et M. André Plamondon. Fabienne Mathieu est candidate à la
maîtrise en sciences forestières (M.Sc.) de l’Université Laval. M. André Plamondon,
directeur de recherche de la candidate, est Professeur titulaire en hydrologie forestière à
l’Université Laval. M. Pierre Bernier, codirecteur de recherche de la candidate, est
Chercheur scientifique en écophysiologie et productivité forestière au Service Canadien des
Forêts. Fabienne Mathieu était responsable du projet de recherche dont les résultats sont
présentés et est l’auteur principal de l’article. Elle a élaboré le protocole de recherche,
organisé le travail, administré et installé sur le terrain les outils et systèmes de cueillette de
données, analysé et interprété les résultats. L’établissement du protocole de recherche, le
suivi du projet et la rédaction de l’article ont été supervisés par MM. André Plamondon
(directeur de recherche) et Pierre Bernier (codirecteur). L’article a été soumis au journal
scientifique Agricultural and Forest Meteorology.
L’article constituant le Chapitre 2 intitulé « Quantifying and correcting for wound response
in heat pulse velocity signals in balsam fir (Abies balsamea L. Mill) » a été coécrit par
Fabienne Mathieu, M. Pierre Bernier et M. André Plamondon. Les rôles des coauteurs
étaient les mêmes que pour l’article mentionné précédemment. L’article a été soumis à la
revue scientifique Journal of Experimental Botany.
L’article formant le Chapitre 3 titré « Transpiration recovery in regenerating clearcuts,
Montmorency Forest, Quebec » a été coécrit par Fabienne Mathieu, M. Pierre Bernier, M.
André Plamondon et M. Denis Lévesque. M. Denis Lévesque est Professionnel de
recherche pour le Laboratoire d’hydrologie forestière de l’Université Laval. Les rôles de
Fabienne Mathieu, M. Pierre Bernier et M. André Plamondon dans la préparation de
l’article étaient les mêmes que pour les articles susmentionnés. M. Denis Lévesque a
iv
fortement contribué à la conception et la validation de la chambre de mesure de
transpiration de la végétation basse. L’article sera soumis au périodique Canadian Journal
of Forest Research.
Je tiens d’abord à remercier très sincèrement mes directeurs de recherche, MM. André
Plamondon et Pierre Bernier. Tout au long de ce projet, ils m’ont guidée et ont grandement
contribué à la structuration et au développement de mon esprit scientifique et de mon sens
critique. Je leur suis très reconnaissante pour l’enseignement d’une méthode de recherche et
de travail qui me guidera tout au long de ma carrière professionnelle, pour la patience et la
tolérance dont ils ont fait preuve et pour leur grande disponibilité.
Je remercie sincèrement Marie-Claude Lambert qui m’a offert un soutien constant et
professionnel pour les analyses statistiques.
Je suis reconnaissante envers Denis Lévesque qui a assuré un solide support technique au
projet. En plus de son travail pour mettre au point une chambre de mesure de transpiration,
il a fabriqué, testé et installé sur le terrain des sondes de type Granier qui ont requis
beaucoup d’ingéniosité, de travail et de patience. Elles n’ont malheureusement pas pu être
utilisées dans les conditions de cette étude. La compagnie de Denis et les discussions que
nous avons eues ont été d’un grand soutien.
Ce projet de recherche n’aurait pu se réaliser sans l’aide de plusieurs assistants sur le terrain
pour la collecte des données. Un gros merci à Jean-Philippe Brunet, Daniel Breton, Marie-
Ève Roy, Claudia Roberge, Esteban Dussart, Valérie Malka et Dominic Besner pour leur
dévouement. Le projet a aussi été rendu possible grâce à l’aide bénévole de Lester Trujillo
Gonzalez, Mathieu Gnocchini, Sylvain Gutjahr, Frank Chazalmartin, Philippe Marcotte,
Julie Talbot et Dominic Besner envers qui j’aimerais exprimer ma profonde gratitude.
Un grand merci à Sébastien Dagnault qui a fourni des mesures météorologiques de grande
qualité et m’a aidée à plusieurs reprises face à des questions techniques.
Merci à Paul Bartlett et Mike Lavigne, Chercheurs du Service Canadien des Forêts, pour
leurs conseils avisés à l’occasion de leur travail de prélecture des deux premiers chapitres.
v
Les étudiants du Laboratoire d’hydrologie forestière ont permis de réaliser ces études dans
des conditions de bonne entente voire d’amitié. Cette belle équipe était composée de Maria
del Carmen Icaza Noguera, Julie Talbot, Paola Jofre, Mathieu Gnocchini, Dominic Besner,
Arthur Périn, Philippe Marcotte, Martin Seto et Dominic Aubé.
Je remercie le Ministère des Ressources naturelles du Québec (Fonds forestier), le Service
Canadien des Forêts – Centre Forestier des Laurentides (projet Ecoleap) et le Conseil de
Recherches en Sciences Naturelles et en Génie du Canada qui ont assuré le support
financier de ce projet.
Je voudrais aussi saluer la gentillesse de l’équipe du pavillon de la Forêt Montmorency qui
m’a chaleureusement accueillie en forêt boréale: Guy Nadeau, Paul Bouliane, André
Lapierre, Julie Gagnon, Bernadette Gilbert.
Finalement, je remercie ma famille chérie et les amis fidèles qui m’ont accompagnée ces
dernières années: Monique Mathieu, Céline Mathieu, Suzanne Boursier, Fabián Cid Yanez,
Christian Mathieu, Harold Mathieu, Carolina Ribeiro Fincatti, Militza Carolina Petrinovic
Huth, Maria José Romero Agliati, Citlalli Rodriguez Ribeiro, Sonja Hausmann, Marie-
Claude Nicole, Maria del Carmen Icaza Noguera, Marcia Vidal Bastias, Emmanuelle
Garrigue, Claire Tronel, Céline Lorant, Anita Lamarre, Claudia Cecilia Chirino, XiaoJing
Guo, Luz Elena Jiménez Hoyos, Marie Simard, Susanne Talmon, Évelyne Lepron,
Natividad Merre, Julie Talbot, Louise Guilbert, Chantal Gervais, Rafael Rodriguez
Mendez, Juan Fernando Petrinovic Huth, Alejandro Ignacio Petrinovic Huth, Lester
Trujillo Gonzalez, Toni Menninger, Richard Perreault, Romualdo Retamal, Jérôme
Alteyrac, Marcelo Miranda Salas, Frédéric Bujold, Simon Boudreault, Esteban Dussart,
Bertrand Anel, Jean-Paul Lamarre, Pascal Boulanger, Laurent Merre, Dominic Gérard,
Philippe Legallo, Charles Coulombe, Mathieu Gnocchini, Alain Gagnon.
vi
Table des matières Résumé................................................................................................................................... ii Avant-Propos ........................................................................................................................ iii Table des matières .................................................................................................................vi Liste des tableaux................................................................................................................ viii Liste des figures .....................................................................................................................ix Introduction générale ..............................................................................................................1 I. Chapitre 1: Relating canopy conductance to environmental variables in two balsam fir (Abies balsamea L. Mill) stands .............................................................................................5
Abstract...............................................................................................................................5 Résumé................................................................................................................................5 Introduction.........................................................................................................................6 Materials and methods ........................................................................................................7
Experimental site ............................................................................................................7 Environmental measurements.........................................................................................9 Sap velocity measurement ............................................................................................10 The transpiration model ................................................................................................12
Results and discussion ......................................................................................................14 The transpiration model ................................................................................................14 Comparing the mature and juvenile stands...................................................................20
Conclusion ........................................................................................................................21 Acknowledgements...........................................................................................................22 References.........................................................................................................................22
II. Chapitre 2: Quantifying and correcting for wound response in heat pulse velocity signals in balsam fir (Abies balsamea L. Mill) .....................................................................26
Abstract.............................................................................................................................26 Résumé..............................................................................................................................26 Introduction.......................................................................................................................27 Material and Methods .......................................................................................................29
Experimental site ..........................................................................................................29 Environmental measurements.......................................................................................30 Sap velocity measurement ............................................................................................31 The transpiration model ................................................................................................33
Results and discussion ......................................................................................................35 Conclusion ........................................................................................................................41 Acknowledgements...........................................................................................................41 References.........................................................................................................................42
III. Chapitre 3: Transpiration as an index of peak flow recovery in regenerating clearcuts, Montmorency Forest, Quebec ..............................................................................46
Abstract.............................................................................................................................46 Résumé..............................................................................................................................46 Introduction.......................................................................................................................47 Materials and methods ......................................................................................................48
Experimental area .........................................................................................................48 Chamber system............................................................................................................51
vii
Sap velocity measurement ............................................................................................52 Scaling up .....................................................................................................................53 Transpiration model ......................................................................................................54 Comparing the sites ......................................................................................................56 Attenuation coefficient .................................................................................................57 Comparison of Ca based on transpiration rates versus Ca based on snowmelt rates ....57
Results and discussion ......................................................................................................58 The transpiration models ..............................................................................................58 Comparing the stands....................................................................................................61 Coefficients of attenuation of the effects of harvesting................................................61
Conclusion ........................................................................................................................64 Acknowledgements...........................................................................................................65 References.........................................................................................................................66
Conclusion générale..............................................................................................................68
viii
Liste des tableaux Table I-1 - Properties of the two experimental stands. ..........................................................9 Table I-2 - Environmental conditions during the study season (30 min values) ..................10 Table I-3 - Values of the parameters of the transpiration models for the 62 and 22 year old
balsam fir stands ...........................................................................................................17 Table II-1 - Dates of probe installation and ranges of time since installation ......................33 Table II-2 - Values of the parameters of the transpiration model.........................................37 Table III-1 - Properties of the four experimental stands. .....................................................49 Table III-2 - Environmental conditions during the 2001 comparison season.......................56 Table III-3 - Transpiration rates models statistics. ...............................................................61 Table III-4 - Mean predicted transpiration rates and their variances for the four sites in
2001. .............................................................................................................................61 Table III-5 - Statistics of the models of coefficient of attenuation of hydrological effects
after harvesting. ............................................................................................................62
ix
Liste des figures Figure 1 - Bassins versants des rivières à saumon atlantique (Fig. 6 de MRNFP, 2003). ….2 Figure I-1 - Montmorency Experimental Forest and stand locations, named S1 and S2 for
the 62 and 22 year old stands respectively. ....................................................................8 Figure I-2 - Comparison of measured (HPV) and predicted half-hourly transpiration from
both balsam fir stands. Also shown is the 1:1 line........................................................15 Figure I-3 - Residuals (measured - predicted canopy transpiration Ec) as a function of (a)
predicted Ec; (b) vapour pressure deficit; (c) relative available water; (d) wind velocity; (e) global radiation; (f) air temperature and (g) day of year. .........................16
Figure I-4 - Relationship between vapour pressure deficit and its gc modifier (curve) for the 62 year old stand. The observed values (circles) correspond to the gc observed divided by the gc predicted without considering the vapour pressure deficit modifier. ............17
Figure I-5 - Comparison of measured (HPV) transpiration for the 22 and 62 year old stands. Also shown is the 1:1 line.............................................................................................21
Figure II-1 - Montmorency Experimental Forest and stand (S1) location ...........................30 Figure II-2 - Configuration of a single set of heat pulse probes implanted radially into the
xylem ............................................................................................................................32 Figure II-3 - Comparison of measured (HPV) and predicted half-hourly transpiration. Also
shown is the 1:1 line. ....................................................................................................36 Figure II-4 - Relationship between time since installation and its Ec modifier (curve). The
data points correspond to daily averages of observed Ec divided by Ec predicted without considering the time since installation modifier..............................................38
Figure II-5 - Relationship between the percent variability in measured transpiration rates explained by adding the time since installation modifier in the model, and the length of data record since installation used in model adjustment...............................................39
Figure III-1 - Montmorency Experimental Forest and stands locations. ..............................49 Figure III-2 - Residuals (measured - predicted canopy transpiration Ec) as a function of
predicted Ec for (a) transpiration model for sites S22 and S62 ; (b) tree transpiration model for site S10 ; (c) herbaceous plants and seedlings transpiration model for site S10 and (d) transpiration model for site S2. .........................................................................60
Figure III-3 – Coefficients of attenuation of hydrological effects after harvesting, based on canopy transpiration rates (full circles) and based on snowmelt rates (empty circles and curve) as a function of (a) canopy height; (b) basal area and (c) stand age...........63
1
Introduction générale La coupe forestière produit une réduction des taux d’évapotranspiration (transpiration et
interception) et habituellement, une augmentation du débit de crue après la fonte de la neige
ou la pluie (Guillemette et al., 2005; Plamondon, 2004). La coupe à blanc de plus de 50-
60% de la surface d’un bassin versant peut augmenter les débits de pointe de pluie de pleins
bords de 50% ou plus (Guillemette et al., 2005; Plamondon, 2004), ce qui est considéré
ayant un effet significatif sur la morphologie du cours d’eau (Plamondon, 2004). Ainsi, au
Québec, Canada, la coupe forestière est présentement limitée à 50% de la superficie de tout
sous-bassin de 100 km² ou plus dans un bassin versant de rivière à saumon atlantique
(Salmo salar). La superficie des bassins de rivières à saumon atlantique représente environ
16% de la superficie de forêts commerciales au Québec (Robert Langevin et Guy Parent,
communication personnelle ; Figure 1).
L’effet de la récolte forestière sur le débit de pointe diminue avec la régénération du site et
permet de nouvelles coupes forestières sans accroître le débit de pointe au-delà du 50%
visé. Ainsi, les aménagistes forestiers ont besoin d’actualiser l’effet de vieilles coupes en
considérant le taux de récupération hydrologique. L’effet actualisé, équivalent à l’effet
d’une coupe à blanc récente, est appelé aire équivalente de coupe (AÉC) (Langevin et
Plamondon, 2004). L’actualisation est calculée dans cette étude en multipliant la surface
coupée par un coefficient d’atténuation (Ca) où Ca = 1 – récupération hydrologique. Les Ca
peuvent être estimés par des mesures de débit de pointe sur quelques décennies après la
coupe. Cette approche coûteuse et longue peut être complétée ou remplacée par l’utilisation
des indices de récupération hydrologique du débit de pointe. Le taux de fonte de la neige à
l’intérieur de peuplements forestiers à des stades différents de développement est considéré
comme un bon indice de la récupération du débit de pointe de fonte de la neige et cette
approche a été utilisée par Hudson (2000) et Langevin et Plamondon (2004). Le
changement des taux de transpiration avec la croissance du peuplement est présumé être un
bon indice de la récupération du débit de pointe de pluie (Plamondon, 2004). L’objectif
général de cette étude était de vérifier si les changements de couvert après la coupe dans
une sapinière à bouleau blanc ont un effet similaire sur la transpiration et sur la fonte de la
neige.
2
Figure 1- Bassins versants des rivières à saumon atlantique (Fig. 6 de MRNFP, 2003).
3
Des mesures de transpiration étalées sur deux saisons de croissance ont été prises en
utilisant la technique heat pulse velocity (HPV) et un système de chambre dans quatre
peuplements de sapin baumier (Abies balsamea L.) de 2, 10, 22 et 62 ans près de Québec,
Canada. Des modèles de transpiration empiriques ont été ajustés afin de prédire les taux de
transpiration de chaque peuplement pour une année commune. La méthodologie HPV, qui
requiert une insertion de sondes dans le bois de l’arbre, a dû être approfondie afin d’étudier
un effet de réponse à la blessure qui compliquait l’interprétation des résultats.
Les objectifs spécifiques du premier chapitre étaient de développer et paramétrer un modèle
simple et efficace de la transpiration, de quantifier les contrôles environnementaux et de
déterminer l’effet de l’âge du peuplement sur la conductance à la vapeur d’eau du couvert
de sapin baumier (Abies balsamea L.). Des mesures semi horaires de vitesse de sève ont été
prises dans les deux peuplements de sapin baumier de 22 et 62 ans, pendant une saison de
croissance. Les données de transpiration ont été utilisées pour ajuster un modèle empirique
de transpiration dérivé de l’équation de Monteith et Unsworth (1990) dans laquelle la
conductance moyenne du couvert devient un paramètre ajusté. La particularité du modèle
proposé est l’addition de modificateurs climatiques qui modulent la conductance moyenne
du couvert.
Le second chapitre étudie la réponse à la blessure succédant à l’installation de sondes HPV
dans l’arbre. Les objectifs de cette section étaient de déterminer s’il y avait une atténuation
du signal HPV avec le temps après installation de sondes dans le sapin baumier (Abies
balsamea L.), de vérifier si cette atténuation était reliée à la période phénologique durant
laquelle une sonde HPV est installée et de proposer une méthodologie par laquelle cet effet
pourrait être corrigé a posteriori. Des mesures semi horaires de vitesse de sève ont été
prises dans le peuplement de 62 ans pendant une saison de croissance. Les données de
transpiration à l’échelle de l’arbre ont été utilisées pour ajuster le modèle empirique de
transpiration dans lequel nous avons incorporé des modificateurs qui prenaient en compte
des variables climatiques, ainsi qu’une possible dérive causée par la réponse due à la
blessure et des effets probables de la date d’installation de la sonde HPV.
Le troisième et dernier chapitre avait pour objectif de vérifier si les changements de couvert
après la coupe dans une sapinière à bouleau blanc ont un effet similaire sur la transpiration
4
et sur la fonte de la neige.. Les modèles ajustés sur les mesures de transpiration prises dans
les quatre peuplements ont été appliqués à une même saison de croissance. Les coefficients
d’atténuation des effets hydrologiques de la coupe (Ca) ont été calculés et des relations ont
été ajustées en fonction de caractéristiques des peuplements. Ces dernières relations ont été
comparées à celles obtenues à partir des taux de fonte de la neige (Talbot et Plamondon,
2002) pour la forêt Montmorency.
Références
Guillemette F, Plamondon AP, Prevost M, and Levesque D. 2005. Rainfall generated stormflow response to clearcutting a boreal forest: peak flow comparison with 50 world-wide basin studies. Journal of Hydrology 302, 137-153.
Hudson R. 2000. Snowpack recovery in regenerating coastal British Columbia clearcuts. Canadian Journal of Forest Research 30, 548-556.
Langevin R, et Plamondon AP. 2004. Méthode de calcul de l’aire équivalente de coupe d’un bassin versant en relation avec le débit de pointe des cours d’eau dans la forêt à dominance résineuse. Québec: Gouvernement du Québec, Ministère des Ressources naturelles, de la Faune et des Parcs, Direction de l’environnement forestier et Université Laval, Faculté de foresterie et de géomatique, code de diffusion 2005-3008. 24 p.
Ministère des Ressources naturelles, de la Faune et des Parcs, 2003. Objectifs de protection et de mise en valeur des ressources du milieu forestier proposés pour les plans généraux d'aménagement forestier 2005-2010 – Document de consultation – Automne 2003. Page 16.
Monteith JL, and Unsworth M. 1990. Principles of Environmental Physics. Edward Arnold, London. 291.
Plamondon AP. 2004. La récolte forestière et les débits de pointe - État des connaissances sur la prévision des augmentations des pointes, le concept de l'aire équivalente de coupe acceptable et les taux régressifs des effets de la coupe sur les débits de pointe. Québec: Direction de l'environnement forestier, Ministère des Ressources naturelles du Québec. 236 p.
Talbot J, et Plamondon AP. 2002. The diminution of snowmelt rate with forest regrowth as an index of peak flow hydrologic recovery, Montmorency Forest, Quebec. 59th Eastern Snow Conference. Stowe, Vermont USA, 22, 85-92.
5
I. Chapitre 1: Relating canopy conductance to environmental variables in two balsam fir (Abies balsamea L. Mill) stands
Abstract The objectives of this work were to develop and parameterize a simple and effective
transpiration model, to quantify the environmental controls and to determine the effect of
stand age on the canopy conductance to water vapour of balsam fir (Abies balsamea L.).
Half-hourly measurements of sap velocity were taken in two stands of balsam fir, 22 and 62
years old, near Quebec City, Canada, during a growing season. These measurements, taken
at a point in the trunk with HPV (heat pulse velocity) probes, were transformed into
transpiration for the tree and the stand by using the radial profile of conductivity of the sap
flow and the sapwood area. The transpiration data were used to adjust an empirical model
of transpiration derived from the equation of Monteith and Unsworth (1990) in which the
average canopy conductance becomes an adjusted parameter. The particularity of the
proposed model is the addition of climatic modifiers that modulate an average canopy
conductance. The model explains 75% of the variability in the half-hourly transpiration of
both stands. Of all climatic modifiers tested, only the air vapour pressure deficit modifier
captures more than 5% of this variability. The average canopy conductance is significantly
higher in the mature stand than in the younger one, and is also affected differently by
vapour pressure deficit. Differences, however, are small. Hence, the model can predict the
transpiration of both stands using only air vapour pressure deficit with only a slight loss in
its goodness of fit.
Résumé Les objectifs de cette étude étaient de développer et paramétrer un modèle simple et
efficace de la transpiration, de quantifier les contrôles environnementaux et de déterminer
l’effet de l’âge du peuplement sur la conductance à la vapeur d’eau du couvert de sapin
baumier (Abies balsamea L.). Des mesures semi horaires de vitesse de sève ont été prises
dans deux peuplements de sapin baumier, de 22 et 62 ans, près de la ville de Québec,
Canada, pendant une saison de croissance. Ces mesures, prises en un point du tronc avec
6
des sondes HPV (heat pulse velocity), ont été transformées en transpiration de l’arbre et du
peuplement en utilisant le profil radial de conductivité du flux de sève et la surface
d’aubier. Les données de transpiration ont été utilisées pour ajuster un modèle empirique de
transpiration dérivé de l’équation de Monteith et Unsworth (1990) dans laquelle la
conductance moyenne du couvert devient un paramètre ajusté. La particularité du modèle
proposé est l’addition de modificateurs climatiques qui modulent une conductance
moyenne du couvert. Le modèle explique 75% de la variabilité de la transpiration semi
horaire des deux peuplements. De tous les modificateurs climatiques testés, seul le
modificateur basé sur le déficit de pression de vapeur de l’air capture plus de 5% de cette
variabilité. La conductance moyenne du couvert est significativement plus élevée dans le
peuplement mature que dans le plus jeune, et est aussi affectée différemment par le déficit
de pression de vapeur. Les différences, cependant, sont faibles. Ainsi, le modèle peut
prédire la transpiration des deux peuplements en utilisant le déficit de pression de vapeur de
l’air avec seulement une légère perte dans sa qualité d’ajustement.
Introduction Forests cover large areas and therefore make a major contribution to the global energy and
mass fluxes between the ground and the atmosphere (Granier et al., 2000). Transpiration, a
major component of these fluxes, is determined by canopy properties and by the state of
key environmental variables in the atmosphere and in the ground (Granier and Bréda,
1996). Although transpiration is a stomata-level process, the population of stomata
comprised within forest canopies behaves in predictable ways, leading to the “big leaf”
representation of canopies (Monteith, 1973). Transpiration of dry and homogeneous
vegetation canopies can therefore be estimated from simple models in which the canopy
response to environmental drivers of transpiration is captured by the canopy conductance
(gc), a proportionality term considered to be the sum of the stomatal conductance (gs) of all
the leaves. Canopy conductance can be expressed as a function of global radiation, vapour
pressure deficit, air temperature, wind speed and soil water availability (Granier and
Loustau, 1994; Jarvis and McNaughton, 1986). In situ studies of transpiration offer the
possibility to quantify these relationships and, hence, capture an essential component of
forest-atmosphere interactions.
7
Transpiration models can play a key role in the improvement of photosynthesis and carbon
balance models. Water and CO2 exchanges between the vegetation and the atmosphere are
strictly connected through the canopy conductance term because of the close physiological
link between water use and CO2 uptake. In effect, the stomatal aperture results from a
compromise between water loss and CO2 assimilation (Farquhar et al., 1980; Leuning,
1995; Mott, 1990; Stanghellini and Bunce, 1993; Wolfe, 1994). The quantification of
canopy conductance through transpiration studies is therefore a useful independent
methodology for improving carbon uptake and growth models.
Many models have been proposed for incorporating the effect of environmental variables
on canopy conductance. Recent work by Bernier et al. (2002) shows that gc can be
expressed successfully as a function of solar radiation and vapour pressure deficit.
However, the type of formulation proposed in that work and in other similar efforts (Arneth
et al., 1999; Ewers et al., 2001; Granier and Bréda, 1996; Jarvis, 1976; Landsberg and
Waring, 1997; Magnani et al., 1998; McCaughey and Iacobelli, 1994; Ogink-Hendriks,
1995; Stewart, 1988) presents at least two drawbacks. The first is the incorporation of a
value of maximum stomatal conductance (gcmax) that usually falls outside the domain of
observations used to fit the models. Values of gcmax thus obtained are therefore unstable and
prone to large departures from the "real" value. The second is the incorporation of the effect
of environmental variables on gc in the form of functions whose shapes are determined by
prior analysis of the impact of single variables on transpiration. In reality, the
environmental variables are often partially correlated to one another, and "pure" functions
may not properly represent the true relationship of a given variable to gc. The objective of
this work was therefore to propose a simple model of canopy transpiration that
circumvented these problems, and offered the flexibility to incorporate any number of
variables as modifiers to gc. The model was used to determine gc for two adjacent stands of
balsam fir (Abies balsamea) of different ages.
Materials and methods
Experimental site The experiment was carried out in a mature stand (S1) and a juvenile stand (S2) of balsam
fir in the Montmorency Experimental Forest (Figure I-1) during the 2002 growing season.
8
The Montmorency Forest (71°06'00'' W; 47°19'00'' N) is located 80 km north of Quebec
City in the Laurentian Uplands of the Canadian Shield, within the regional landscape unit
of the Bastican and Martens Lakes (Robitaille and Saucier, 1998). The two adjacent stands
were 62 and 22 years old. Both stands have a closed canopy with a sparse ground cover of
mosses and small vascular plants. Additional properties are shown in Table I-1. The stands
originate from clearcuts and occupy a west-exposed hillslope at elevations of 803 and 774
m respectively, with slope ranging from 30 to 45%. The well-drained soils have a coarse
fraction, a sand content and a clay content of 22%, 45% and 5% respectively. Total rooting
depth is estimated to be 60 cm owing to the presence of a compacted till layer at that depth.
Figure I-1 - Montmorency Experimental Forest and stand locations, named S1 and S2 for the 62 and 22 year old stands respectively.
9
Table I-1 - Properties of the two experimental stands.
Mature stand Juvenile stand
Age 62 22
Basal area (BA, m².ha-1) 44.6 20.2
Mean diameter (cm) 16.9 8.1
Density (stems.ha-1) 1860 3300
Mean height (m) 16.7 7.7
Leaf area index 7.7 7.9
% balsam fir (% of total BA) 92 93
The projected leaf areas are derived from the sapwood-leaf area relationship of Coyea and Margolis (1992) for balsam fir
Environmental measurements Environmental variables have been monitored since 1997 in the mature stand as part of the
ECOLEAP project (Bernier et al., 1999). Wind speed (Met One 014A, Campbell
Scientific), incoming solar radiation (LICOR LI200SZ), rainfall, relative humidity (Vaisala
HMP45C, Campbell Scientific), and air temperature (CSI 107, Campbell Scientific) are
measured from a suite of instruments positioned along a 22-m tower. Precipitation is
obtained from an automatic tipping bucket rain gauge located above the canopy. Automatic
TDR sensors (CS615, Campbell Scientific) are inserted vertically in the soil around the
meteorological tower and integrate soil water content down to 30 cm. A segmented TDR
sensor is also installed alongside the CS615 to a depth of 60 cm, and is read periodically
using the portable MP917 TDR measurement unit (E.S.I Environmental Sensors Inc.,
Victoria, BC, Canada). All instruments, except for the MP917 probe, are scanned
automatically every 5 min and the results are compiled for every 30 min period on
Campbell CR10 data loggers. The environmental conditions met during the present
experimental period are presented in Table I-2.
10
Table I-2 - Environmental conditions during the study season (30 min values) Variable Mean Minimum Maximum
Da (Pa) 635.9 46.29 1768.77
u (m.s-1) 3.71 0.45 9.93
Rg (W.m-2) 397.32 0.06 1091
θr 0.91 0.75 1
Ta (C) 16.43 5.36 25.74
Da: vapor pressure deficit, u: wind speed, Rg: global radiation, θr: relative available soil water, Ta: air temperature
Portable MP917 measurements of volumetric water content were used to develop a
relationship between water content in the 0-30 cm and the 30-60 cm soil layers. The strong
(r2 = 0.90, n = 14) relationship was used to extend the 0-30 cm half-hourly readings of
volumetric soil water content down to the full 60 cm profile. Relative available soil water
(θr) was computed as:
wfc
wmr θθ
θθθ−−
= (1)
where θm, θfc and θw are measured water content, water content at field capacity and at
permanent wilting point respectively. We used the equations proposed by Saxton et al.
(1986) and the measured sand and clay contents of the soil to obtain estimates of 0.24
cm3.cm-3 and of 0.08 cm3.cm-3 for θfc and θw respectively. We then multiplied these
estimates by 0.78 to account for the 22% coarse fraction of the soils. This procedure
yielded a maximum potential usable water content of 0.12 cm3.cm-3, which, for the 60 cm
soil profile, represents a water reserve of 7.2 cm, or enough water to sustain an average
transpiration for about 24 days. The value of θr was not allowed to exceed 1 (θm ≤ θfc).
Sap velocity measurement Transpiration was monitored using heat pulse velocity (HPV) systems (Model SF300,
Greenspan Technology, Warwick, Queensland, Australia) (Greenspan Technology, 2002a;
2002b). Each system consists of a dedicated logger to which are attached four probes. Each
probe has a heating element and two temperature sensing elements that are positioned on
11
either side of the heater in an asymmetrical fashion. Details of the system and its operation
can be found in Becker (1998), Smith and Allen (1996) and Pausch et al. (2000). HPV
probes were installed in eight trees per stand, with one probe per tree. The system
configuration and cable lengths required to select the trees in four-tree clusters centred on
one dedicated logger. Trees were selected randomly in a two-pass choice. The first tree of
each four-tree cluster was selected randomly within the plot. The three other trees within
that cluster were then selected randomly among the trees within a 4 m radius of the first.
Trees were randomly selected in each plot, with a weight applied to the random factor equal
to the tree basal area, in order to bias the sampling towards the size classes that contribute
the most to plot-level transpiration (Martin et al., 1997; Raulier et al., 2002) and to
maintain the conceptual continuity with the scaling up procedure. The net result of this
sampling technique is to reduce the variability of plot-level estimations, when scaling up
from the sample trees to the plot, by improving sap flux estimation in the largest diameter
classes. The sampling excluded the trees previously sampled or presenting a wound or a
deformation.
Previously-determined relationships between tree diameter at breast height and sapwood
area were used to properly position the probes in the trees. Sap velocity measurements were
made at 30-min intervals from 9 July to 3 September 2002, using a 1.6 s heat pulse. The
probes were moved every two weeks in order to avoid wounding response. A total of 32
trees per stand were therefore sampled during the measurement season.
Several 5-mm diameter increment cores were taken at breast height on trees next to the
sampled trees in order to measure the volume fractions of sap and woody matrix of the
sapwood (Fl and Fm). Core samples were taken several times during the season in order to
account for the seasonal variations. Volume fractions were calculated from measurements
of the fresh mass, oven-dry mass and volume of the sapwood samples (Greenspan
Technology, 2002b; Pausch et al., 2000). Liquid and woody matrix volume fractions were
used for relating HPV to sap velocity for the total sapwood area.
Radial profiles of sap velocities had been measured prior to the experiment in order to
account for the variation of sap velocity with radial depth (Edwards and Warwick, 1984;
Green and Clothier, 1988; Hatton et al., 1990; Smith and Allen, 1996) in the calculation of
12
sap flows. We developed a normalized radial velocity profile using data obtained during a
single sunny day on four trees per stand. In each tree, sap velocities had been obtained from
a reference probe set at 3 mm depth in the sapwood, and a mobile one set for 90 min at one
of several depths d. A linear decrease of sap velocity with depth below cambium (data not
shown) was evident in each of the studied trees. This relationship was used to estimate total
sap flow in trees measured during the season.
The scaling up from single-tree transpiration to stand-level transpiration was performed by
multiplying the sap flow calculated in the sampled tree by the ratio of sapwood area of the
whole stand to that in this tree. Scalars of flux based on sapwood area have been shown to
be excellent scalars of flux at the stand level (Hatton, 1995). The canopy transpiration was
calculated as the average of these individual-tree estimates.
The transpiration model Canopy transpiration can be calculated according to Monteith and Unsworth (1990) as:
γλρp
acc
cDgE = (2)
where Ec is the canopy transpiration (mm.h-1), gc the canopy conductance (m.h-1), Da the
vapour pressure deficit (Pa), cp the specific heat of dry air (1010 J.kg-1.C-1), ρ the density of
the air (1.204 kg.m-3), γ the psychrometric constant (66.1 Pa.C-1) and λ the latent heat of
vaporization (2.454E6 J.kg-1). This simplification of the Penman-Monteith equation is
based on the assumption that the vegetation being sampled is well coupled to the
atmosphere, and that the aerodynamic resistance is far less than the stomatal resistance
(Phillips and Oren, 1998). As mentioned above, the proportionality term, gc is dependent on
different environmental variables. In an earlier paper, Bernier et al. (2002) proposed a form
of modifiers that accounts for the effects of radiation and vapour pressure deficit on gc. In
the current model, we used modifiers in a form that permits the use of a wider variety of
external variables for estimating the time course of gc:
TarRguDcxcc fffffgfgga θ∏ == (3)
13
where cg is the average canopy conductance (m.h-1), fx are modifiers that account for the
effect of vapour pressure deficit (Da), wind speed (u, m.s-1), global radiation (Rg, W.m-2),
relative available soil water (θr) and air temperature (Ta, ºC). The modifiers are computed
as (Raulier et al., 2000):
2
,,1 ⎟⎠⎞
⎜⎝⎛ −+⎟
⎠⎞
⎜⎝⎛ −+=
xxx
xxxf xqxlx αα (4)
where fx is the modifier for the variable x, x is the mean value of this variable within the
observation data set and αl,x and αq,x are parameters to be estimated along with cg . Stand
comparison was carried out by introducing p, a dummy variable, into Equations 3 and 4:
( )( )γλ
ρβ p
axcc
cDfpgE ∏+= (5)
and
( ) ( )2
,,,,1 ⎟⎠⎞
⎜⎝⎛ −++⎟
⎠⎞
⎜⎝⎛ −++=
xxxp
xxxpf xqxqxlxlx βαβα (6)
where p takes on the value of 0 for the mature stand and of 1 for the juvenile stand. The
difference between the two stands shows up in the significance of the β, βl,x and βq,x terms.
In the following text, Eqs. 5 and 6 are referred to as the full model and Eqs. 2, 3 and 4 are
referred to as the reduced model.
Prior to adjustment, all periods with relative humidity above 95% were rejected in order to
eliminate the effect of wet foliage. We also rejected periods with a null or negative global
radiation in order to remove periods of null or very low nocturnal transpiration rates since
the heat pulse method is of limited value for measuring low rates of sap flow in woody
plants (Becker, 1998; Burgess et al., 2001). All other records were used for the empirical
fit.
Equations (5) and (6) were adjusted to the combined half-hourly data of both stands using
the PROC MODEL function of the SAS software (SAS Institute, Cary, NC). Modifier
14
variables were included in a forward fashion from the null model (gc = cg ) and were
retained only if they explained more than 5% of the variability in observed transpiration
rates. No other provision was made to account for the repeated nature of the measurements.
Results and discussion
The transpiration model The forward analysis of both the full and reduced models revealed that, for both stands,
only the modifier based on Da explained more than 5% of the variability in half-hourly
transpiration. Modifiers based on Rg, θr, u and Ta are therefore absent from the final
equation. Adjustment of the resulting transpiration model to the field data shows a very
good correspondence between predicted and observed stand-level transpiration, with r²
value of 0.75 (Figure I-2). Values of the parameters are listed in Table I-3. Residuals
(measured minus predicted Ec) show no apparent bias with respect to Da, θr, u, Rg, Ta,
predicted Ec, nor to the day of year (Figure I-3 a-g). The average canopy conductance
value, cg , explains 63.6% of the observed variability in Ec, while the vapour pressure
deficit modifier explains another 7.9%. The parameter β that captures the difference in
cg between the two stands (Eq. 5), explains an additional 3.5% of the variability in
measured transpiration. Within the vapour pressure deficit modifier, the linear parameters
αl and βl (Eq. 6) explain 7.5% and 0.4% of the variability respectively. The significance of
these parameters reflects the generally linear decrease in gc with an increase in air dryness
(Figure I-4) along with a slightly greater sensitivity of the mature stand to Da (Table I-3).
15
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.00 0.10 0.20 0.30 0.40
Ec measured (mm.h-1)
Ec
pred
icte
d (m
m.h
-1)
Figure I-2 - Comparison of measured (HPV) and predicted half-hourly transpiration from both balsam fir stands. Also shown is the 1:1 line.
16
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.00 0.05 0.10 0.15 0.20 0.25 0.30
Ec predicted (mm.h-1)
Res
idua
ls (m
m.h
-1)
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0 500 1000 1500 2000
Vapour pressure deficit (Pa)
Res
idua
ls (m
m.h
-1)
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.74 0.79 0.84 0.89 0.94 0.99
Relative available water (fraction)
Res
idua
ls (m
m.h
-1)
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0 2 4 6 8 10 12
Wind velocity (m.s-1)
Res
idua
ls (m
m.h
-1)
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0 200 400 600 800 1000 1200
Global radiation (W.m-2)
Res
idua
ls (m
m.h
-1)
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0 5 10 15 20 25 30
Air temperature (C)
Res
idua
ls (m
m.h
-1)
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
190 200 210 220 230 240 250
Day of year
Res
idua
ls (m
m.h
-1)
a b
c d
fe
g
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.00 0.05 0.10 0.15 0.20 0.25 0.30
Ec predicted (mm.h-1)
Res
idua
ls (m
m.h
-1)
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0 500 1000 1500 2000
Vapour pressure deficit (Pa)
Res
idua
ls (m
m.h
-1)
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.74 0.79 0.84 0.89 0.94 0.99
Relative available water (fraction)
Res
idua
ls (m
m.h
-1)
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0 2 4 6 8 10 12
Wind velocity (m.s-1)
Res
idua
ls (m
m.h
-1)
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0 200 400 600 800 1000 1200
Global radiation (W.m-2)
Res
idua
ls (m
m.h
-1)
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0 5 10 15 20 25 30
Air temperature (C)
Res
idua
ls (m
m.h
-1)
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
190 200 210 220 230 240 250
Day of year
Res
idua
ls (m
m.h
-1)
a b
c d
fe
g
Figure I-3 - Residuals (measured - predicted canopy transpiration Ec) as a function of (a) predicted Ec; (b) vapour pressure deficit; (c) relative available water; (d) wind velocity; (e) global radiation; (f) air temperature and (g) day of year.
17
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
0 500 1000 1500 2000
Vapor pressure deficit (Pa)
Vap
or p
ress
ure
defic
it m
odifi
er
Figure I-4 - Relationship between vapour pressure deficit and its gc modifier (curve) for the 62 year old stand. The observed values (circles) correspond to the gc observed divided by the gc predicted without considering the vapour pressure deficit modifier.
Table I-3 - Values of the parameters of the transpiration models for the 62 and 22 year old balsam fir stands
Parameters Estimates Standard Error t Value
Full model cg (m.h-1) 37.07484 0.3621 102.38
β -8.19549 0.5320 -15.41
αl,Da -0.26875 0.0109 -24.68
βl,Da 0.098283 0.0190 5.17
Reduced model cg (m.h-1) 33.28767 0.2862 116.31
αl,Da -0.23021 0.00970 -23.74
For all parameters, Pr > |t| is less than 0.0001 The important contribution of the average canopy conductance in explaining the observed
transpiration rates is a direct consequence of Fick’s law and shows the strong
proportionality between the transpiration stream and the concentration gradient driving this
mass flow. The significant vapour pressure deficit modifier fDa (Figure I-4) captures an
additional component of this otherwise proportional relationship that can be interpreted as a
feedback phenomenon between the vapour pressure deficit or the transpiration rate and the
18
stomatal conductance (Monteith, 1995; Mott and Parkhurst, 1991). The variable resistance
to water transfer in the soil-plant-atmosphere continuum, as presently captured by fDa, is
often attributed to a closure of the stomata (Dang et al., 1997; Hogg and Hurdle, 1997;
McCaughey and Iacobelli, 1994). However, passive mechanisms such as the resistance to
water transport within the tree's hydraulic pathways (Yang and Tyree, 1993) or the
temporary depletion of soil water around fine roots would also result in a similar response.
The linearity of the fDa modifier is shown in Figure I-4 where the points correspond to the
observed gc divided by the gc predicted without considering the vapour pressure deficit
modifier. The values are therefore ratios, not residuals, and incorporate all other
uncertainties not explained by the model. The model adjustment is done by least square fit
to Ec, and is therefore adjusted to reduce the errors in transpiration, and not gc. The very
high “observed” values in Figure I-4 correspond to values of very low vapour pressure
deficit and consequently low transpiration rates which carry little weight in a least square
fit on Ec. The apparent large errors on gc at low transpiration rates may also be attributable
to known deficiencies of the HPV method at low sap velocities. In fact, we obtain very low
values of r2 (0.3) if we adjust the model on gc instead of Ec because of large errors in the
estimation of the few high gc values associated with low transpiration rates. In the context
of carbon exchange modelling, this may represent a weak point in gc values obtained from
HPV measurements.
The inverse relationship between gc or gs (gs = gc / leaf area index) and air vapour pressure
deficit has also been demonstrated for many tree species. These include maritime pine
(Pinus pinaster) (Granier and Loustau, 1994), Larix gmelinii (Arneth et al., 1996), jack
pine (Pinus banksiana) (Dang et al., 1997), black spruce (Picea mariana) (Dang et al.,
1997), sessile oak (Quercus petraea) (Granier and Bréda, 1996), European beech (Fagus
sylvatica) (Granier et al., 2000; Magnani et al., 1998), white birch (Betula papyrifera)
(McCaughey and Iacobelli, 1994), Eucalyptus grandis (Dye and Olbrich, 1993), aspen
(Populus tremuloides) (Dang et al., 1997; Hogg and Hurdle, 1997; McCaughey and
Iacobelli, 1994) and sugar maple (Acer saccharum) (Bernier et al., 2002). However, we did
not find a threshold in Da as reported by Hogg and Hurdle (1997), nor did we find their
curvilinear relationship. One reason for this last difference may be the much higher values
19
of Da measured at their drier Western Canada study site (up to 4000 Pa), as compared with
our site (up to 1500 Pa).
All modifiers based on environmental variables other than Da failed to explain more than
5% of the observed variability in transpiration. Not surprisingly, Da is correlated with
global radiation (Spearman correlation coefficient of 0.51) and temperature (Spearman
correlation coefficient of 0.61) and so partially captures the effects of these variables. Jarvis
and McNaughton (1986) also propose that, in aerodynamically rough forest canopies that
are well coupled to the atmosphere, transpiration is relatively insensitive to solar radiation,
as was found for Pinus radiata canopies (Kelliher et al., 1990). This good coupling with
the atmosphere is demonstrated by the non-significance of the wind modifier, providing a
further justification for using the Monteith and Unsworth (1990) simplification of the
Penman-Monteith equation for estimating canopy transpiration. Finally, in spite of an
apparent dry spell during the measurement period, inclusion of a soil water content
modifier failed to improve the model. Relative water content only dropped down to 0.75
during the measurements, a value that is far above the threshold value of 0.4 found to
trigger a drop in canopy conductance in sugar maple (Bernier et al., 2002) and in Douglas-
fir (Pseudotsuga menziesii) (Black, 1979). Granier (1987) found a threshold value of 0.3
for Douglas-fir.
The model used in this study presents several advantages over previous empirical models of
transpiration that incorporate the effect of environmental variables on gc (e.g. Bernier et al.,
2002; Granier and Bréda, 1996). The model accounts for inevitable interactions between
the various environmental variables, and it does so in two ways. First, it permits the
rejection of a variable whose effect is captured by a related variable. Secondly, by using a
flexible quadratic function instead of a more pre-determined response form (e.g. Bernier et
al., 2002) for the modifiers, the model enables more flexibility in the expression of the
interaction between a given environmental variable and the measured transpiration, given
that other variables are also included in the equation.
A second advantage of the model is that it is centred around mean values and does not use
the concept of optimality. For example, in the model of Bernier et al. (2002), the a1
parameter of Eq. 5 corresponds in effect to a supposedly maximum value of gc, when Rg is
20
high and Da is low. However, these conditions are seldom met in the field, and the
estimated value of maximum gc therefore falls outside the data domain. In the current
approach, it is the mean gc that is estimated, providing for a stable model and a robust value
of the conductance parameter. The same reasoning goes for modifiers whose values are
centred on 1 for average conditions, again providing stable and robust estimates. Finally,
the relative simplicity of the model improves flexibility and facilitates interpretation of
results. For example, in a separate trial (unpublished results), a gradual effect of wounding
was detected through a simple modifier and factored out of the analysis.
Comparing the mature and juvenile stands The transpiration rates of the mature and juvenile stands are linearly related with a R² of
0.78 (Figure I-5). The 0.83 slope is significant at α = 1% and indicates a greater
transpiration rate for the mature stand. The relationship shows no significant offset from the
origin. The measured transpiration means for the measurement period are 0.159 mm.h-1 and
0.132 mm.h-1 for the mature and juvenile stands respectively. As previously mentioned for
the full model (Eqs. 5 and 6), parameters β and βl,Da are significant, suggesting a difference
in the general proportionality relationship between Da and transpiration, and, also, in the
apparent feedback that results in an overall non-linear response of transpiration to Da.
Because of the lower height of the juvenile stand, we were expecting differences to show
up in the wind speed modifier, but this hypothesis is not supported by the analysis. The
similarity in leaf area index (LAI) between the two stands rules out obvious structural
canopy differences, as found by Raulier et al. (2002) for two sugar maple stands. Rooting
depth is also of no consequence as soil water failed to produce a significant effect on
transpiration. The observed difference of canopy conductance between both stands may
reflect an increase in sapwood permeability with age, as shown by Coyea and Margolis
(1992) for Abies balsamea. It is also possible that uncertainties surrounding the estimation
of LAI and of stand-level sapwood area may hide functional differences between these two
stands.
21
y = 0,8298x - 0,0053r2 = 0,7795
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4
Ec measured in the 65 year old stand (mm.h-1)
Ec
mea
sure
d in
the
25 y
ear
old
stan
d(m
m.h
-1)
Figure I-5 - Comparison of measured (HPV) transpiration for the 22 and 62 year old stands. Also shown is the 1:1 line.
However, in spite of these differences, the transpiration streams from the two stands are
quite similar. The more general predictive model of transpiration for balsam fir stands,
fitted without the inclusion of the dummy variable p, accounts for 70.9% of the
transpiration variability. Parameter values of both models are shown in Table I-3.
Conclusion We have demonstrated that transpiration and canopy conductance of forest stands could be
modeled using a simple, yet powerful, approach that permits the inclusion of multiple
external variables in the predictive equation, as well as the accounting for between-variable
interactions. As mentioned above, the model is based on the concept of mean values and
departure from means. Most ecophysiological models that rely on canopy conductance use
maximum values that are then reduced by limiting factors. However, the two approaches
are not irreconcilable, since it is possible to use the equations adjusted in this work to
compute actual values of canopy conductance for long meteorological records, and define a
maximum value in a statistical sense rather than an absolute one.
22
Acknowledgements The authors thank Marie-Ève Roy for help in the field, Sébastien Dagnault for provision of
meteorological data and Marie-Claude Lambert and Xiao Jing Guo for statistical support in
SAS analyses. They are also grateful to Paul Bartlett for helpful comments on the initial
draft and Pamela Cheers for her editorial work. The study was supported by the Ministère
des Ressources naturelles du Québec (Fonds forestier) and the Canadian Forest Service,
Laurentian Forestry Centre (ECOLEAP project).
References
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26
II. Chapitre 2: Quantifying and correcting for wound response in heat pulse velocity signals in balsam fir (Abies balsamea L. Mill)
Abstract Heat pulse velocity (HPV) methodology permits the continuous measurement of tree level
transpiration, but wound response following probe placement sometimes poses a problem
in the interpretation of the results. The objectives of this study were to determine if there
was an attenuation of the HPV signal with time since probe installation in balsam fir (Abies
balsamea L.), to verify if this attenuation was related to the phenological period during
which a HPV probe is installed and to propose a methodology by which this effect could be
corrected for a posteriori. Half-hourly measurements of sap velocity were taken in a 62
years old stand of balsam fir, near Quebec, Canada, during a growing season. The tree-level
transpiration data were used to adjust an empirical transpiration model based on the
Monteith and Unsworth equation (1990) in which we incorporated modifiers that accounted
for climatic variables, as well as possible drift due to wounding response and possible
effects of HPV probe installation date. Wounding response, as captured by the number of
days since probe installation, explained 16% of HPV measurement variability, while
phenological effect on wounding, as captured by the date of probe installation, explained
less than 5% of the variability. A polynomial correction factor is provided for the HPV
signal attenuation in balsam fir.
Résumé La méthodologie « heat pulse velocity » (HPV) permet la mesure en continu de la
transpiration à l’échelle de l’arbre, mais la réponse à la blessure succédant à l’installation
de sonde pose un problème dans l’interprétation des résultats. Les objectifs de cette étude
étaient de déterminer s’il y avait une atténuation du signal HPV avec le temps après
installation de sonde dans le sapin baumier (Abies balsamea L.), de vérifier si cette
atténuation était reliée à la période phénologique durant laquelle une sonde HPV est
installée et de proposer une méthodologie par laquelle cet effet pourrait être corrigé a
posteriori. Des mesures semi horaires de vitesse de sève ont été prises dans un peuplement
27
de sapin baumier de 62 ans, près de Québec, Canada, pendant une saison de croissance. Les
données de transpiration à l’échelle de l’arbre étaient utilisées pour ajuster un modèle
empirique de transpiration basé sur l’équation de Monteith et Unsworth (1990) dans
laquelle nous avons incorporé des modificateurs qui prenaient en compte des variables
climatiques, ainsi qu’une possible dérive due à la réponse à la blessure et des effets
probables de la date d’installation de la sonde HPV. La réponse à la blessure, telle que
capturée par le nombre de jours depuis l’installation de la sonde, expliquait 16% de la
variabilité des mesures HPV, tandis que l’effet phénologique sur la blessure, tel que capturé
par la date d’installation de la sonde, expliquait moins de 5% de la variabilité. Un facteur de
correction polynomial est proposé pour l’atténuation du signal HPV dans le sapin baumier.
Introduction Obtaining estimates of whole-tree water use (transpiration) has become increasingly
important in ecophysiological, ecosystems and catchment hydrology investigations (Pausch
et al., 2000; Wullschleger et al., 1998). Although ecosystem or stand-level measurements
of total evapotranspiration can now readily be obtained from eddy-covariance systems
(Blanken and Black, 2004), tree-level measurements of transpiration scaled up to the plot or
stand offer particularly interesting information with respect to environmental controls on
canopy conductance, as well as being far less constrained by site properties than most
micro-meteorological systems.
There are numerous methods that can be used to estimate transpiration in mature trees, but
sap flow techniques hold definite advantages over other techniques (Smith and Allen,
1996). Sap flow techniques are portable (Olbrich, 1991; Pausch et al., 2000), nearly non-
destructive methods that permit continuous measurements of xylem sap flow with high
time resolution (Dye et al., 1996; Olbrich, 1991; Smith and Allen, 1996). The thermal
dissipation method developed by Granier (1985) permits continuous measurements across a
radial sapwood profile. The heat pulse, or compensation method (heat pulse velocity, HPV)
samples sap flow velocity intermittently at points along a radial flow profile. With its well-
developed theoretical background (Swanson and Whitfield, 1981), the HPV method offers
a robust methodology with a very low power requirement (Burgess et al., 2001; Olbrich,
1991; Swanson and Whitfield, 1981).
28
Both thermal dissipation and heat pulse velocity methods are invasive in that they require
the insertion of millimetre-diameter probes into the xylem. Installing sensors in xylem
tissue causes mechanical damage with removal of wood by the drill bit and disruption of
tracheids at the edge of the drill hole (Barrett, 1992; Barrett et al., 1995; Burgess et al.,
2001; Smith and Allen, 1996; Swanson, 1983; Swanson and Whitfield, 1981). This results
in interruption of the sap flow by the probes and in their immediate neighbourhood
throughout a wounded, lens-shaped band parallel to stem (Dunn and Connor, 1993;
Edwards et al., 1997; Smith and Allen, 1996; Swanson, 1983; Swanson and Whitfield,
1981). Methodologies incorporated in both the Granier and the HPV methods permit the
correction of measurements for these initial wounds.
A complication may arise when a further wounding response develops gradually during the
measurement period. Over time, sap flow may be disrupted further and further away from
the sensor probes if a wound reaction develops. This situation would seriously impair the
accuracy of measurements of either the Granier or HPV technique as the magnitude of the
measured sap flow may decrease gradually regardless of the actual transpiration rate (Smith
and Allen, 1996; Swanson, 1983). Wound size was identified as the major source of error
in the estimate of sap flow (Hatton et al., 1995; Olbrich, 1991). On Eucalyptus grandis
trees, errors of 10%, 20%, -20% and 50% in measured wound size result in 18%, 40.3%, -
29.7% and 120.3% errors in sap flow respectively (Table 3 of Olbrich, 1991). So it is
essential to test and correct the sap velocity measurements not only for the immediate
wound effect but also for the long-term signal degradation.
Over the past few years, we have carried out sap flow measurements in sugar maple (Acer
saccharum, Marsh), a temperate hardwood (Bernier et al., 2002), and balsam fir (Abies
balsamea Mill), a highly resinous tree (e.g. Mathieu et al., submitted). Analysis of sap flow
measurements in sugar maple failed to reveal any drift during the summer-long
measurements that could indicate a gradual wounding response (Bernier et al., 2002).
However, during an early trial in balsam fir, we noticed a gradual decline in measured
transpiration after probe placement. Interpretation problems arose, however, because of the
difficulty of separating a putative wounding response from foliage phenology and seasonal
drift in climatic variables. Hence, a measurement experiment was carried out in order to
29
meet three objectives. The first one was to quantify the wounding response with time. The
second one was to determine if this response interacted with phenology. Finally, the third
one was to develop a simple methodology to account for such response in the interpretation
of tree-level transpiration measurements.
Material and Methods
Experimental site The experiment was carried out during the 2003 growing season in a pure balsam fir stand
located within the Montmorency Experimental Forest (Figure II-1). The site (71°06'00'' W;
47°19'00'' N) is located 80 km north of Quebec City in the Laurentian Uplands of the
Canadian Shield, within the regional landscape unit of the Bastican and Martens Lakes
(Robitaille and Saucier, 1998). The closed-canopy balsam fir (Abies balsamea (L.) Mill.)
stand was 62 years old, with balsam fir making up 92% of the total basal area of 44.6 m².ha-
1 with 1860 stems.ha-1. Mean balsam fir diameter and height were respectively 16.9 cm and
16.7 m. The projected leaf area of 7.7 is derived from the sapwood-leaf area relationship of
Coyea and Margolis (1992) for balsam fir. The stand originates from clearcut and occupies
a west-exposed hillslope at elevation of 803 m, with slopes ranging from 30 to 45%. The
well-drained soils have a coarse fraction, a sand content and a clay content of 22%, 45%
and 5% respectively. Total rooting depth is estimated to be 60 cm owing to the presence of
a compacted till layer at that depth.
30
Figure II-1 - Montmorency Experimental Forest and stand (S1) location
Environmental measurements Environmental variables have been monitored in situ since 1997 as part of the ECOLEAP
project (Bernier et al., 1999; Bernier et al., 2001). Wind speed (Met One 014A, Campbell
Scientific), incoming solar radiation (LICOR LI200SZ), rainfall, relative humidity (Vaisala
HMP45C, Campbell Scientific), and air temperature (CSI 107, Campbell Scientific) are
measured from a suite of instruments positioned along a 22-m tower. Precipitation is
obtained from an automatic tipping bucket rain gauge located above the canopy. Automatic
TDR sensors (CS615, Campbell Scientific) are inserted vertically in the soil around the
meteorological tower and integrate soil water content down to 30 cm. A segmented TDR
sensor is also installed alongside the CS615 to a depth of 60 cm, and is read periodically
using the portable MP917 TDR measurement unit (E.S.I Environmental Sensors Inc.,
Victoria, BC, Canada). All instruments, except for the MP917 probe, are scanned
automatically every 5 min and the results are compiled for every 30 min period on
Campbell CR10 data loggers. Portable MP917 measurements of volumetric water content
were used to develop a relationship between water content in the 0-30 cm and the 30-60 cm
soil layers. The strong (r2 = 0.90, n = 14) relationship was used to extend the 0-30 cm half-
31
hourly readings of volumetric soil water content down to the full 60 cm profile. Relative
available soil water (θr) was computed as:
wfc
wmr θθ
θθθ−−
= (1)
where θm, θfc and θw are measured water content, water content at field capacity and at
permanent wilting point respectively. We used the equations proposed by Saxton et al.
(1986) and the measured sand and clay contents of the soil to obtain estimates of 0.24
cm3.cm-3 and of 0.08 cm3.cm-3 for θfc and θw respectively. We then multiplied these
estimates by 0.78 to account for the 22% coarse fraction of the soils. This procedure
yielded a maximum potential usable water content of 0.12 cm3.cm-3, which, for the 60 cm
soil profile, represents a water reserve of 7.2 cm, or enough water to sustain an average
transpiration for about 24 days. The value of θr was not allowed to exceed 1 (θm ≤ θfc).
Sap velocity measurement Transpiration was monitored using heat pulse velocity (HPV) systems (Model SF300,
Greenspan Technology, Warwick, Queensland, Australia) (Greenspan Technology, 2002a;
2002b). Each system consists of a dedicated logger to which are attached four probes. Each
probe has a heating element and two temperature sensing elements that are positioned on
either side of the heater in an asymmetrical fashion (Figure II-2). Details of the system and
its operation can be found in Becker (1998), Smith and Allen (1996) and Pausch et al.
(2000). The analytical theory is described in numerous references (Barrett et al., 1995; Dye
et al., 1996; Edwards et al., 1997; Edwards and Warwick, 1984; Olbrich, 1991; Smith and
Allen, 1996; Swanson, 1994; Swanson and Whitfield, 1981; Wullschleger et al., 1998). We
randomly selected three trees to be instrumented. Trees were randomly selected in each
plot, with a weight applied to the random factor equal to the tree basal area. This procedure
biases the sampling towards the larger size classes that contribute the most to plot-level
transpiration (Martin et al., 1997; Raulier et al., 2002). The sampling excluded the trees
previously sampled or presenting a wound or a deformation.
32
Figure II-2 - Configuration of a single set of heat pulse probes implanted radially into the xylem
Probes were installed in the three selected trees in order to measure sap velocity at several
times after insertion. A first probe was installed in each tree on June 4th (Day Of Year or
DOY 155) at a randomly chosen azimuthal angle. This probe was left in place for the
duration of the experiment (Table II-1). Three additional probes (probes 2 to 4) were
installed in each tree at DOYs 163, 169 and 176 respectively, 7 cm, 28 cm and 42 cm
respectively to the right of the first probe along the tree’s circumference, at same depth and
same height as the first one. Probes 2 and 3 were removed and installed 14 cm and 35 cm
respectively along the tree’s circumference to the right of the first probe on DOYs 198 and
212 respectively. The dates of probe installation and displacement were selected to generate
repeated measurements of time since installation, but with starting points at different times
during the growing season. The design permitted a more intensive sampling of the early
phase of attenuation, as well as testing for a possible phenological influence on wounding.
Sap velocity measurements were made at 30-min intervals from 4 June to 4 August 2003,
using a 1.6 s heat pulse.
33
Table II-1 - Dates of probe installation and ranges of time since installation
Probe Dates of installation Day of year Ranges of time since installation
1 June 4 155 0 to 61 days
2 June12
July 17
163
198
0 to 35 days
0 to 18 days
3 June 18
July 31
169
212
0 to 43 days
0 to 4 days
4 June 25 176 0 to 40 days
Several 5-mm diameter increment cores were taken at breast height on trees next to the
sampled trees in order to measure the percent of sapwood volume occupied by the sap,
wood and air fractions. These values are required for the conversion of heat pulse velocities
to sap velocities for the total sapwood area. Core samples were taken several times during
the season in order to account for the seasonal variations. The volume fractions were
calculated from measurements of the fresh mass, oven-dry mass and fresh volume of the
sapwood samples (Edwards and Warwick, 1984).
A previously-determined relationship between tree diameter at breast height and sapwood
width was used to install all probes to a depth that corresponded to the mid-point of the
sapwood area. Radial profiles of sap velocities had been obtained prior to the experiment in
order to account for the variation of sap velocity with radial depth (Edwards and Warwick,
1984; Green and Clothier, 1988; Hatton et al., 1990; Smith and Allen, 1996) in the
calculation of sap flows. We found a linear decrease of sap velocity with depth below
cambium (unpublished results). Because of this linear decrease, probes, installed to mid-
point of the sapwood area, measured area-weighed mean sap velocity. Total sap flow for a
given tree was obtained by multiplying the measured sap velocity by the sapwood area.
The transpiration model We used the transpiration model described in Mathieu et al. (submitted), but applied it to
probe-level measurements of sap flux instead of the stand-level transpiration analysis of
34
Mathieu et al. (submitted). Transpiration can be calculated according to Monteith and
Unsworth (1990) as:
γλρp
acc
cDgE = (2)
where Ec is the tree-level transpiration (l.h-1), gc the canopy conductance (m³.h-1), Da the
vapour pressure deficit (Pa), cp the specific heat of dry air (1010 J.kg-1.C-1), ρ the density of
the air (1.204 kg.m-3), γ the psychrometric constant (66.1 Pa.C-1) and λ the latent heat of
vaporization (2.454E6 J.kg-1). The proportionality term, gc is variable and dependent on
different environmental variables (Mathieu et al., submitted). In the current model, the non-
linearities in the proportionality term gc are captured by modifiers that represent the
influence of the external variables as:
DotDyTarRguDcxcc ffffffffgfgga θ∏ == (3)
where cg is the average canopy conductance (m³.h-1), fx are modifiers that account for the
effect of vapour pressure deficit (Da), wind speed (u, m.s-1), global radiation (Rg, W.m-2),
relative available soil water (θr), air temperature (Ta, ºC), measurement day of year (Dy),
time since HPV probe installation (t, d) and day of year of probe installation (Do). These
last two modifiers account respectively for signal attenuation following wound response
and phenological effects at the time of probe installation. The modifiers for Da, u, Rg, θr, Ta,
Dy and Do are computed as (Raulier et al., 2000):
2
,,1 ⎟⎠⎞
⎜⎝⎛ −+⎟
⎠⎞
⎜⎝⎛ −+=
xxx
xxxf xqxlx αα (4)
where fx is the modifier for the variable x, x is the mean value of this variable within the
observation data set and αl,x and αq,x are parameters to be estimated along with cg .
Modifier for t was expressed as:
2,,1 ttf tqtlt αα ++= (5)
35
All periods with relative humidity above 95% were removed from the dataset for model
adjustment in order to eliminate the effect of wet foliage. We also removed night-time
measurements (null or negative global radiation). In addition to providing a limited
contribution to total transpiration, these measurements represent low sap flow velocities in
which HPV measurements present the greatest uncertainties (Barrett et al., 1995; Becker,
1998; Burgess et al., 2001). All other records were used for the empirical fit.
Equations (2), (3), (4) and (5) were adjusted to half-hourly data using the PROC
NLMIXED function of the SAS software (SAS Institute, Cary, NC). As the effect of
repeated measures within tree was not significant (P value = 0.35), it was removed. The
parameters of the reduced model were then estimated with the PROC MODEL function.
Modifier variables were included using the forward selection method, starting with the null
model (gc = cg ) and retaining at each step the variable whose modifier explained the most
observed variation in the dependant variable. Only variables that explained more than 5%
of the variability in observed transpiration rates were retained. No other provision was
made to account for the repeated nature of the measurements.
A modifier based on time since probe installation was adjusted on Swanson data, including
a binary variable p where p = 1 for Swanson data and p = 0 for the data of the present
study. The PROC MODEL function of the SAS software (SAS Institute, Cary, NC) was
used with a likelihood ratio test.
Results and discussion The analysis reveals that only the variables Da and t contribute to explaining more than 5%
of the transpiration rate variability. Modifiers based on Rg, θr, u, Ta, Dy and Do are therefore
absent from the final equation. A more detailled analysis on the relationship between the
various environmental variables and transpiration is found in Mathieu et al. (submitted).
The application of the transpiration model to probe-level measurements was done to permit
the extraction of probe-level effect of wounding, but we recognised that the resulting
empirical fit would necessarily be more noisy at the probe level than it would have been at
the stand level. Adjustment of the resulting transpiration model to the field data shows a
good correspondence between predicted and observed transpiration for Ec < 3 l.h-1, with R²
36
value of 0.59 (Figure II-3), down from the R2 of 0.75 achieved by Mathieu et al.
(submitted) at the stand level. The model could not achieve predicted values above 3 l.h-1,
but only 3% of the observed transpiration rates were above this value. Values of the
parameters are listed in Table II-2. Given that the model is a heteroskedastic one, the least
squares estimators are unbiased but not efficient or asymptotically efficient..Their
conventionally calculated standard errors are biased and, therefore, are not presented. Note
that the above value of R² is the maximum value for the given sample since the last squares
regression line gives the best fit of any line by definition (Kmenta, 1971). Residuals
(measured minus predicted Ec) show no apparent bias with respect to Rg, θr, u, Ta, Da, t, Dy,
Do nor to the predicted Ec (data not shown). The average canopy conductance value, cg ,
explains 22.6% of the observed variability in Ec, while the vapour pressure deficit and the
time since installation modifiers explain 21.0% and 15.8% respectively.
0
1
2
3
4
5
6
0 1 2 3 4 5 6
Ec measured (l/h)
Ec p
redi
cted
(l/h
)
Figure II-3 - Comparison of measured (HPV) and predicted half-hourly transpiration. Also shown is the 1:1 line.
37
Table II-2 - Values of the parameters of the transpiration model
Parameters Estimates
cg (m³.h-1) 367.5575
αl,Da -0.41292
αq,Da 0.070586
αl,t -0.01798
αq,t 0.000082
For all parameters, Pr > |t| is less than 0.0001
The wounding reactions depend on tree species. We found no wound response in a previous
experiment with sugar maple (Bernier et al., 2002). In the current experiment with balsam
fir, thirty days after probe installation, the wounding response modifier (ft) has dropped to
0.53 as the HPV signal, and the resulting measured transpiration, was attenuated by the
wounding response (Figure II-4). Swanson (1983) checked the loss of sensor sensitivity
(development of wound reaction) with extended implantation time for Pinus contorta
Dougl. by comparing readings from sequentially implanted probes. Thirty days after
installation HPV signals averaged 0.75 of the current (control) HPV signals, and 360 days
after installation, 0.6 of control. The parameters estimates of the ft modifier adjusted on his
data (his figure 5, p. 47) for lodgepole pine are significantly different (α = 1%) from the
ones determined in the present study for balsam fir (likelihood ratio χ² = 3302.5, P value <
0.0001). Wound diameter and its expansion control wounding response. Wound diameter
has been found to be constant for 10 – 20 days in Pinus spp. (Swanson and Whitfield,
1981). For Eucalyptus spp. wound diameter has been found to reach a maximum value
within two days of probe installation and remain stable for up to 27 days (E. marginata,
Marshall, 1993 in Greenspan Technology, 2002b) and at least 7 days (E. populnea, Hatton
et al., 1995). In Eucalyptus grandis, Olbrich (1991) reported that 5 days were necessary for
tyloses formation after probes implantation.
38
0
1
2
3
0 10 20 30 40 50 60 70
Time since installation (d)
Tim
e si
nce
inst
alla
tion
mod
ifier
Figure II-4 - Relationship between time since installation and its Ec modifier (curve). The data points correspond to daily averages of observed Ec divided by Ec predicted without considering the time since installation modifier.
The modifier function for days since probe installation can be used in a corrective mode to
eliminate the progression of wounding response with time. Using the parameters presented
in Table II-2, one can obtain a corrected transpiration rate by applying the following
correction:
t
cc f
EE =' (6)
where 'cE is the corrected transpiration rate.
An additionnal analysis was performed to determine how long probes could be installed
without requiring using a time since installation modifier. For this analysis the model was
refitted for data sets in which the length of the record was gradually increased from 14 days
since installation to 40 days. Figure II-5 shows the percent of explained variability in
observed transpiration by the time since installation modifier as a function of data record
length. The analysis shows a lack of wounding response for the initial two weeks after
installation and a strong response thereafter. In balsam fir, probes can therefore be installed
for about two weeks without having to account for signal attenuation. This strategy was
used in the work carried out by Mathieu et al. (submitted) during the previous summer by
39
moving the probes on a regular basis in order to prevent wounding response from becoming
significant. Moving the probes presents the benefit of increasing the sample size of studied
trees, but greatly increases the burden of field work.
0
2
4
6
8
10
12
14
0 5 10 15 20 25 30 35 40 45
Length of data record (d)
Var
iabi
lity
expl
aine
d (%
)
Figure II-5 - Relationship between the percent variability in measured transpiration rates explained by adding the time since installation modifier in the model, and the length of data record since installation used in model adjustment.
Defence mechanisms that are suspected to be important for wound width increase and HPV
signal attenuation are resin deposition in tracheids surrounding the probes (Barrett et al.,
1995; Smith and Allen, 1996; Swanson and Whitfield, 1981), tracheids occlusion by tyloses
(Barrett et al., 1995) and embolism due to air entry when probes are emplaced (Pearce,
1996; Smith and Allen, 1996; Sperry and Tyree, 1990; Swanson, 1972). Balsam fir xylem
doesn’t normally contain resin ducts (Bakuzis and Hansen, 1965), but traumatic resin ducts
formation has been reported in the xylem of balsam fir roots (Tippett et al., 1982) and
stems (Blanchette, 1982), as well as in many conifers sapwood (Pearce, 1996; Tippett et al.,
1982; Tippett and Shigo, 1981). Embolism is another possible mechanism of wound
response in balsam fir as Sperry and Tyree (1990) qualified this species as especially
vulnerable to embolism. The absence of wounding response in non-resinuous sugar maple
in the measurements made by Bernier et al. (2002, their Fig. 3e) favours the hypothesis of
resin deposition as a main cause of signal attenuation in balsam fir.
40
The day of probe installation explaining only 3.2% of transpiration measurement variability
was not retained as a significant modifier because it failed to meet our criteria of 5%.
However, we suspect that this effect would have turned out significant if the experiment
had been planned during a longer period than two months, and if more than six installation
dates had been tested. The plant’s reaction to wounding has been reported to be related to
the date of probe installation in various species. In studies of Pinus contorta Dougl.,
Swanson (1983) reported a much stronger drop in signal level when probes were installed
in April than when probes were installed in August. Shigo indicated (personal
communication as cited in Swanson, 1983) that there are two periods, one at leaf
emergence and the other broadly in August when a tree may not react to a wound to isolate
it. Lopuhinsky (1986) also noted a time dependence of the response to wounding in Pinus
ponderosa Dougl. and Pseudotsuga menziesii with a probes sensivity loss in the range 6-
13% during the 1-year period when probes were installed in late summer. Miller et al.
(1980) working with oaks observed that the length of time the measurements remain
accurate varies with the phenological stage. In the earlier part of the season, sap exudes into
the sensor and heater holes, apparently coating the sensors and insulating them, thus
slowing their response to an applied heat pulse. In August and September they could not
detect any difference in recorded velocities between newly implanted sensors and adjacent
ones which had been in operation for several weeks. It is also reported that in general the
response to wounding depends on phenological state (Shigo and Hillis, 1973) and that resin
exudation and physiological wound reactions may be delayed or postponed by high plant
water stress (Puritch and Mullick, 1975; Vité, 1961).
The general effect of phenology was also evaluated by visually examining the plot of
residuals against the day of year of measurements (results not shown). Bernier et al. (2002)
had shown a small phenological effect in sugar maple which they attributed to a response to
soil drying. However, in this experiment on balsam fir, we could not detect such
relationship between day of year and transpiration outside the effect of seasonality in
climate variables.
41
Conclusion The Swanson and Whitfield (1981) correction is an adjustment intrinsic to the HPV method
for the wound permanent effect, whose validity is not challenged when employed
immediately following the probes implantation. On the other hand, for long-term
measurements, it is essential to verify by HPV sequential readings experiment if the studied
tree species reacts to wounding with defense mechanisms leading to signal degradation. In
order to avoid significant sap flows underestimates, a correction factor can then be easily
calibrated using a simple empirical modelling method.
Acknowledgements The authors thank Jean-Philippe Brunet and Daniel Breton for their help in the field,
Sébastien Dagnault for providing meteorological data and Marie-Claude Lambert for
statistical support in SAS analyses. We are also grateful to Mike Lavigne for helpful
comments on the initial draft. The study was supported by the Ministère des Ressources
naturelles du Québec (Fonds forestier) and the Canadian Forest Service, Laurentian
Forestry Centre (ECOLEAP project).
42
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Barrett DJ, Hatton TJ, Ash JE, and Ball MC. 1995. Evaluation of the heat pulse velocity technique for measurement of sap flow in rainforest and eucalypt forest species of south-eastern Australia. Plant, Cell and Environment 18, 463-469.
Becker P. 1998. Limitations of a compensation heat pulse velocity system at low sap flow: implications for measurements at night and in shaded trees. Tree Physiology 18, 177-184.
Bernier PY, Bréda N, Granier A, Raulier F, and Mathieu F. 2002. Validation of a canopy gas exchange model and derivation of a soil water modifier for transpiration for sugar maple (Acer saccharum Marsh.) using sap flow density measurements. Forest Ecology and Management 163, 185-196.
Bernier PY, Fournier RA, Ung CH, Robitaille G, Larocque GR, Lavigne MB, Boutin R, Raulier F, Pare D, Beaubien J, Delisle C, and Mitchell AK. 1999. Linking ecophysiology and forest productivity: an overview of the ECOLEAP project. The Forestry Chronicle 75, 417-421.
Bernier PY, Raulier F, Stenberg P, and Ung CH. 2001. Importance of needle age and shoot structure on canopy net photosynthesis of balsam fir (Abies balsamea): a spatially inexplicit modeling analysis. Tree Physiology 21, 815-830.
Blanchette RA. 1982. Decay and canker formation by Phellinus Pini in white and balsam fir. Canadian Journal of Forest Research 12, 538-544.
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Dunn GM, and Connor DJ. 1993. An analysis of sap flow in mountain ash (Eucalyptus regnans) forests of different age. Tree Physiology 13, 321-336.
Dye PJ, Soko S, and Poulter AG. 1996. Evaluation of the heat pulse velocity method for measuring sap flow in Pinus patula. Journal of Experimental Botany 47, 975-981.
43
Edwards WRN, Becker P, and Cermak J. 1997. A unified nomenclature for sap flow measurements. Tree Physiology 17, 65-67.
Edwards WRN, and Warwick NWM. 1984. Transpiration from a kiwifruit vine as estimated by the heat pulse technique and the Penman-Monteith equation. New Zealand Journal of Agricultural Research 27, 537-543.
Granier A. 1985. A new method of sap flow measurement in tree stems. Annals of Forest Science 42, 193-200.
Green SR, and Clothier BE. 1988. Water use of kiwifruit vines and apple trees by the heat-pulse technique. Journal of Experimental Botany 39, 115-123.
Greenspan Technology. 2002a. Sapflow sensor - User Manual, Version 2, Edition 6.4. Warwick, Australia: Greenspan Technology Pty. Ltd. 41p.
Greenspan Technology. 2002b. Sapflow Measurement with the Greenspan Sapflow Sensor: Theory and Technique. Warwick, Australia: Greenspan Technology Pty. Ltd. 34p.
Hatton TJ, Catchpole EA, and Vertessy RA. 1990. Integration of sapflow velocity to estimate plant water use. Tree Physiology 6, 201-209.
Hatton TJ, Moore SJ, and Reece PH. 1995. Estimating stand transpiration in a Eucalyptus populnea woodland with the heat pulse method: measurement errors and sampling strategies. Tree Physiology 15, 219-227.
Kmenta, J., 1971. Violation of basic assumptions. In: Elements of econometrics. New York: The Macmillan Company. P. 260.
Lopushinsky W. 1986. Seasonal and diurnal trends of heat pulse velocity in Douglas-fir and ponderosa pine. Canadian Journal of Forest Research 16, 814-821.
Martin, T.A. et al., 1997. Crown conductance and tree and stand transpiration in a second-growth Abies amabilis forest. Canadian Journal of Forest Research, 27: 797-808.
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Miller DR, Vavrina CA, and Christensen TW. 1980. Measurement of sap flow and transpiration in ring-porous oaks using a heat pulse velocity technique. Forest Science 26, 485-494.
Monteith JL, and Unsworth M. 1990. Principles of Environmental Physics. Edward Arnold, London. 291.
Olbrich BW. 1991. The verification of the heat pulse velocity technique for estimating sap flow in Eucalyptus grandis. Canadian Journal of Forest Research 21, 836-841.
Pausch RC, Grote EE, and Dawson TE. 2000. Estimating water use by sugar maple trees: considerations when using heat-pulse methods in trees with deep functional sapwood. Tree Physiology 20, 217-227.
Pearce RB. 1996. Tansley review No. 87 - Antimicrobial defences in the wood of living trees. New Phytologist 132, 203-233.
Puritch GS, and Mullick DB. 1975. Effect of water stress on the rate of non-suberized impervious tissue formation following wounding in Abies grandis. Journal of Experimental Botany 26, 903-910.
Raulier F, Bernier PY, and Ung CH. 2000. Modeling the influence of temperature on monthly gross primary productivity of sugar maple stands. Tree Physiology 20, 333-345.
Raulier, F., Bernier, P.Y., Ung, C.-H. and Boutin, R., 2002. Structural differences and functional similarities between two sugar maple (Acer saccharum) stands. Tree Physiol. 22(15-16), 1147-1156.
45
Robitaille A, and Saucier J-P. 1998. Paysages régionaux du Québec méridional, Sainte Foy, Québec. 213.
Saxton KE, Rawls WJ, Romberger JS, and Papendick RI. 1986. Estimating generalized soil-water characteristics from texture. Soil Science Society of America Journal 50, 1031-1036.
Shigo AL, and Hillis WE. 1973. Heartwood, discolored wood, and microorganisms in living trees. Annual Review of Phytopathology 11, 197-222.
Smith DM, and Allen SJ. 1996. Measurement of sap flow in plant stems. Journal of Experimental Botany 47, 1833-1844.
Sperry JS, and Tyree MT. 1990. Water-stress-induced xylem embolism in three species of conifers. Plant, Cell and Environment 13, 427-436.
Swanson RH. 1972. Water transpired by trees is indicated by heat pulse velocity. Agricultural Meteorology 10, 277-281.
Swanson RH. 1983. Numerical and experimental analyses of implanted-probe heat pulse velocity. University of Alberta, Botany, Edmonton, Alberta. 235.
Swanson RH. 1994. Significant historical developments in thermal methods for measuring sap flow in trees. Agricultural and Forest Meteorology 72, 113-132.
Swanson RH, and Whitfield DWA. 1981. A numerical analysis of heat pulse velocity theory and practice. Journal of Experimental Botany 32, 221-239.
Tippett JT, Bogle AL, and Shigo AL. 1982. Response of balsam fir and hemlock roots to injuries. European journal of forest pathology 12, 357-364.
Tippett JT, and Shigo AL. 1981. Barriers to decay in conifer roots. European Journal of Forest Pathology 11, 51-59.
Vité JP. 1961. The influence of water supply on oleoresin exudation pressure and resistance to bark beetle attack in Pinus ponderosa. Contributions from Boyce Thompson Institute for Plant Research 21, 37-66.
Wullschleger SD, Meinzer FC, and Vertessy RA. 1998. A review of whole-plant water use studies in trees. Tree Physiology 18, 499-512.
46
III. Chapitre 3: Transpiration as an index of peak flow recovery in regenerating clearcuts, Montmorency Forest, Quebec
Abstract The objective of this study was to verify if the canopy changes following harvesting in
balsam fir (Abies balsamea L.) forests have an analogous effect on transpiration and
snowmelt. Transpiration measurements were taken using heat pulse velocity and chamber
systems in four stands of balsam fir, 2, 10, 22 and 62 years old near Quebec City, Canada,
during two growing seasons. Empirical transpiration models were adjusted in order to
predict the transpiration rates for a common year. The transpiration rates from the four
stands were all significatively different. Coefficients of attenuation of the hydrological
effects of harvesting (Ca) were calculated from the transpiration rates and relations were
adjusted as a function of stands properties. The Ca were highly correlated with age, height
and basal area. The Ca function indicates that a 50% hydrologic recovery is achieved at a
canopy height of 5.3 m and a basal area of 12.6 m².ha-1, 16 years after the cut. The
estimated values of the Ca for the 2, 12 and 22 years old stands based on transpiration are
similar to those obtained from snowmelt measurements.
Résumé L’objectif de cette étude était de vérifier si les changements de couvert après la coupe dans
des forêts de sapin baumier (Abies balsamea L.) ont un effet similaire sur la transpiration et
sur la fonte de la neige.. Les mesures de transpiration ont été prises pendant deux saisons de
croissance en utilisant des systèmes heat pulse velocity et une chambre dans quatre
peuplements de sapin baumier (Abies balsamea L.) âgés de 2, 10, 22 et 62 ans, près de
Québec, Canada. Des modèles empiriques de transpiration ont été ajustés afin de prédire les
taux de transpiration pour une année commune. Les taux de transpiration des quatre
peuplements étaient tous significativement différents. Les coefficients d’atténuation des
effets hydrologiques de la coupe (Ca) ont été calculés à partir des taux de transpiration et
des relations ont été ajustées en fonction de propriétés des peuplements. Les Ca étaient
hautement corrélés avec l’âge, la hauteur et en particulier avec la surface terrière. La
fonction de Ca indique qu’une récupération hydrologique de 50% est atteinte pour une
47
hauteur de couvert de 5.3 m et une surface terrière de 12.6 m².ha-1, 16 ans après la coupe.
Les valeurs estimées de Ca basés sur la transpiration étaient similaires à ceux obtenus à
partir des mesures de fonte de la neige. pour les peuplements de 2, 12 et 22 ans.
Introduction Forest harvesting produces a reduction of evapotranspiration rates (transpiration and
interception) and usually, a stormflow increase following snowmelt or rainfall. Clearcutting
more than 50-60% of a basin area can increase rainfall bankfull peak flows by 50% or more
(Guillemette et al., 2005; Plamondon, 2004) which is used by the Ministry of Forest of
Québec as a temporary threshold to maintain streambed morphology. Hence, forest
harvesting is being limited to 50% of the area of any subwatershed of 100 km² or more
within the salmon river (Salmo salar) basins in Quebec, Canada. The area of the salmon
rivers basins represents about 16% of the area of commercial forests in Quebec (Robert
Langevin and Guy Parent, personal communication).
The effect of forest harvesting on peak flow decreases with revegetation of the site and
enables new forest cuttings without enhancing peak flow increases beyond the aimed 50%
(Guillemette et al., 2005). Hence, forest managers need to actualize the effect of older cuts
by considering the rate of hydrologic recovery. The actualized effect being equivalent to the
effect of an actual clearcut, is called equivalent clearcut area (Langevin and Plamondon,
2004). The actualization in this study is calculated by multiplying the area harvested by an
attenuation coefficient (Ca) where Ca = 1 – hydrologic recovery. The Ca is best estimated
by peak flow measurements over a few decades after logging. However, this expensive and
lengthly approach can be complemented or replaced by the use of indices of hydrological
recovery of peak flow. The snowmelt rate within forest stands at different states of
development is considered a good index of snowmelt peak flow recovery and this approach
was used by Hudson (2000) and Langevin and Plamondon (2004). The change in the rates
of transpiration with stand growth was assumed to be a good index of the recovery of
rainfall peak flow (Plamondon, 2004). The objective of this study was to verify if the
canopy changes following harvesting in balsam fir forests have an analogous effect on
transpiration and snowmelt.
48
Materials and methods
Experimental area The experiment was carried out at four sites in the Montmorency experimental Forest
(Figure III-1) during the 2002 and 2003 growing seasons. The Montmorency Forest
(71°06'00'' W; 47°19'00'' N) is located 80 km north of Québec City in the Laurentian
Uplands of the Canadian Shield, within the regional landscape unit of the Bastican and
Martens Lakes (Robitaille and Saucier, 1998). The four experimental sites used in this
study were a recent clearcut (S2) and sapling (S10), pole (S22) and mature (S62) stands (Table
III-1). The indices on the site names represent the years since harvest at the time of the
measurements. Sites S2, S22 and S62 are situated within 700 m of each other while site S10 is
located at 5 km from S62. Site S62 was chosen as it had been selected for the project
Ecoleap for its homogeneity (Bernier et al., 1999) and was instrumented for meteorology.
The other sites were selected in order to represent an age range and for their accessibility.
Sites S2 and S22 were chosen in order to be in the same area as site S62. Finally a 10 years
old site was not available near the S62 stand. It was located within the experimental basin
7A so the information will be also useful for the calibration of a streamflow model.
49
S2
S62S10
S22
S2
S62S10S2
S62S10
S22
Figure III-1 - Montmorency Experimental Forest and stands locations.
Table III-1 - Properties of the four experimental stands.
S62 S22 S10 S2 Clearcut year 1940 1980* 1993 2001 Balsam fir mean diameter (cm) 16.91 8.11 4.22 - Balsam fir density (stems.ha-1) 1860 3300 2625 79000Balsam fir mean height (m) 16.7 7.7 3.5 0.4 Total basal area (G, m².ha-1) 44.61 20.21 5.22 - Balsam fir leaf area index 7.7 7.9 4.5 - % balsam fir (% of G ; diameter > 3 cm) 921 931 752 - Tree cover (diameter > 3 cm) (%) 100 100 50 9 Cover below 1.5 m (%) 0 0 50 91 % ground vegetation cover within the chamber frames3 - - 76 18 Slope (%) 30-45 30-45 12 12 Elevation (m) 803 774 827 861 Exposition West West West West * precommercial thinning in 1992 ; 1 diameter at breast height ; 2 diameter at stump height ; 3 visually evaluated The projected leaf areas are derived from the sapwood-leaf area relationship of Coyea and Margolis (1992) for balsam fir.
50
The percentages of balsam fir trees and herbaceous plants cover were determined for sites
S2 and S10. In each site, 88 circular 0.2-m² plots were established at 50 cm intervals, along
two perpendicular diameters of a circular 400-m² plot. Vegetation was classified for each
station as “balsam fir trees” (stump diameters greater than or equal to 3 cm), “herbaceous
plants, shrubs, mosses and tree seedlings” (lower than 1.5 m) or “other” (vegetation above
1.5 m for which the chamber could not be used and with the exception of balsam fir above
3 cm diameter). The total of “balsam fir trees” and “herbaceous plants and seedlings” types
was set at 100%, thus distributing the type “other” in the other classes, assuming that its
transpiration rate is at the pro rata of the measured covers.
The site S2 is a recent clearcut where the vegetation was 0.3 m high except for few dead or
moribund standing trees covering about 9% of the area. In chamber frames, 6% of the
ground was covered by mosses (Pleurozium schreberi, Dicranum sp., Hylocomium
splendens) and sphagnum, 10% by seedlings (Abies balsamea, Corylus cornuta, Picea
mariana), 2% by herbaceous plants (Oxalis montana, Dryopteris spinulosa, Trientalis
borealis, grass) and 82% by bare soil and debris. The site S10 is a sapling stand where the
balsam fir trees 3 m high are bunched together, covering about 25% of the area. Abies
balsamea, Betula papyrifera, Prunus pensylvanica and Salix sp. represent 75%, 21%, 3%
and 1% respectively of the total basal area at stump height. The vegetation inventoried
within the chamber frames was dominated by Rubus idaeus (54% of the ground cover),
accompanied by Epilobium angustifolium (7%), Abies balsamea (3%), sphagnum (3%),
grass (3%), mosses (2%), Betula papyrifera (2%), Ribes triste (1%), Linnaea borealis
(1%), Corylus cornuta (1%) and debris (24%).
Environmental variables have been monitored since 1997 in the S62 stand as part of the
ECOLEAP project (Bernier et al., 1999). Wind speed (Met One 014A, Campbell
Scientific), incoming solar radiation (LICOR LI200SZ), rainfall, relative humidity (Vaisala
HMP45C, Campbell Scientific) and air temperature (CSI 107, Campbell Scientific) were
measured from a suite of instruments positioned along a 22-m tower. Precipitation was
obtained from an automatic tipping bucket rain gauge located above the canopy. Automatic
TDR sensors (CS615, Campbell Scientific) were inserted vertically in the soil around the
meteorological tower to integrate soil water content down to 30 cm. All instruments were
51
scanned automatically every five minutes and the results were compiled for every 30
minutes period on Campbell CR10 data loggers. Incoming solar radiation was also
measured in sites S2 and S10 during the chamber measurements, using a pyranometer
(LI200, Licor) installed on the portable chamber system top.
Chamber system Transpiration rates of herbaceous plants and seedlings were measured in sites S2 and S10
throughout the 2003 growing season by determining the rate at which air humidity
increased inside a chamber system. The portable chamber was made of a 1m x 1m x 1.5m
frame of polyvinyl chloride tubes covered with polyethylene terephthalate sheet. In order to
reduce the boundary layer, air was circulated within the chamber by an electric fan installed
on the tent top. To support the chamber, ten fixed wood frames (1m x 1m) surrounded with
polyethylene terephthalate sheet were installed in two clusters of five frames at each site. In
site S2, the first frame location of each cluster was selected randomly while the four other
ones were installed 1m away from each other along a transect whose direction was
randomly selected. On the other hand, because of the merging of forest (trees) and
herbaceous zones, site S10 was subjected to a pre-sampling stratification. The two five-
frames clusters were installed in a herbaceous zone, the frames being located 1m away
from each other. To enclose the vegetation to be measured, the portable chamber was
sealed at the base on each fixed frame. Air temperature and relative humidity inside the
chamber were measured every 5 seconds during one minute. The equation used to calculate
transpiration rate includes the correction proposed by LI-COR (1987) for the change in
humidity inside the chamber:
( ) 31036001
273 −××−
∂∂
+= M
Pe
te
TRKV
E a
adst
(1)
where E is the transpiration rate (mm.h-1), Vt the total chamber volume (m³), Kads the
adsorption coefficient, R the gas constant for air (8.314 m³.Pa.K-1.mol-1), Ta the chamber air
temperature (C), e the vapor pressure of air (Pa), te
∂∂ the rate of change of e obtained by
52
regression on the one-minute dataset (Pa.s-1), P the atmospheric pressure taken as a
constant (100 000 Pa) and M the water molar mass (18 g.mol-1).
A test was performed to determine water adsorption by the system. In the laboratory, up to
99.6% of the water evaporated in the chamber was accounted for by the measurements, so
no adsorption coefficient was included in equation (1).
On days during which the canopy was dry, 79 transpiration measurements were performed
in site S10 between 10h30 and 16h Eastern Standard Time (EST) from July 7th to July 31st
whereas 56 measurements were taken between 13h30 and 16h30 EST from July 31st to
August 13th in site S2.
Sap velocity measurement Transpiration of balsam fir trees, with diameters greater than or equal to 3 cm, was
monitored using heat pulse velocity (HPV) systems (Model SF300, Greenspan Technology,
Warwick, Queensland, Australia) (Greenspan Technology, 2002a; 2002b). Each system
consists of a dedicated logger to which are attached four probes. Each probe has a heating
element and two temperature sensing elements that are positioned on either side of the
heater in an asymmetrical fashion. Details of the system and its operation can be found in
Becker (1998), Pausch et al. (2000) and Smith and Allen (1996). . HPV probes were
installed in eight trees per site, with one probe per tree. The system configuration and cable
lengths required to select the trees in four-tree clusters centred on one dedicated logger.
Trees were selected randomly in a two-pass choice. The first tree of each four-tree cluster
was selected randomly. The three other trees within that cluster were then selected
randomly among the trees within a 4 m radius of the first. Trees were randomly selected in
each plot, with a weight applied to the random factor equal to the tree basal area, in order to
bias the sampling towards the size classes that contribute the most to plot-level
transpiration (Martin et al., 1997; Raulier et al., 2002) and to maintain the conceptual
continuity with the scaling up procedure. The net result of this sampling technique is to
reduce the variability of plot-level estimations, when scaling up from the sample trees to
the plot, by improving sap flux estimation in the largest diameter classes. The sampling
excluded the trees previously sampled or presenting a wound or a deformation.
53
Sap velocity measurements were made at 30-minute intervals using a 1.6s heat pulse from
July 9 to September 3, 2002 in sites S22 and S62, and from June 4 to August 15, 2003 in site
S10. The probes were moved every two weeks in order to avoid wounding response. A total
of 32 and 40 trees per site were therefore sampled during the 2002 and 2003 measurement
seasons respectively.
Several 5-mm diameter increment cores were taken at breast height on trees next to the
sampled trees in order to measure the percent of sapwood volume occupied by the sap,
wood and air fractions. These values are required for the conversion of heat pulse velocities
to sap velocities for the total sapwood area. Core samples were taken several times during
the season in order to account for the seasonal variations. The volume fractions were
calculated from measurements of the fresh mass, oven-dry mass and fresh volume of the
sapwood samples (Edwards and Warwick, 1984).
Radial profiles of sap velocities had been obtained in S22 and S62 sites prior to the
experiment in order to account for the variation of sap velocity with radial depth (Edwards
and Warwick, 1984; Green and Clothier, 1988; Hatton et al., 1990; Smith and Allen, 1996)
in the calculation of sap flows. We found a linear decrease of sap velocity with depth below
cambium (unpublished results). This relationship was used to estimate total sap flow in S22
and S62 trees measured during the season.
A previously-determined relationship between tree diameter and sapwood width for S10 site
trees was used to install all probes to a depth that corresponded to the mid-point of the
sapwood area. Because of the linear decrease of sap velocity with depth from cambium,
probes measured area-weighted mean sap velocity. Total sap flow for a given tree was
obtained by multiplying the measured sap velocity by the sapwood area.
Scaling up For HPV measurements, the scaling up from single-tree transpiration in l.h-1 to stand-level
transpiration in mm.h-1 was performed by multiplying the sap flow calculated in the
sampled tree by the ratio of stand-level sapwood area in m2.ha-1 to that in this tree. Scalars
of flux based on sapwood area have been shown to be excellent scalars of flux at the stand
level (Hatton et al., 1995). The canopy transpiration of the trees was calculated as the
54
average of these individual-tree estimates. Transpiration rates on sites S62 and S22 were
assumed to be equal to HPV-measured canopy transpiration. Measured over a sufficiently
long period, the quantity of sap flow upward through the stem must equal transpiration at
the leaves (Swanson 1994). Transpiration rates on site S2 were assumed to be equal to the
chamber-measured transpiration rates of the herbaceous and seedling layer, neglecting the
few moribund standing trees. Transpiration rates of site S10 was obtained by combining
modeled rates from trees to modeled rates from the herbaceous-seedling cover, pro-rated by
their respective percent cover of the site surface, that is 33% and 67% for balsam fir trees
and for herbaceous plants and seedlings respectively. The model adjusted to the various
datasets is described below.
Transpiration model The transpiration model based on the Monteith and Unsworth (1990) relation is described
in Mathieu et al. (submitted) as:
γλρp
acc
cDgE = (2)
where Ec is the canopy transpiration (mm.h-1), gc the canopy conductance (m.h-1), Da the
vapor pressure deficit (Pa), cp the specific heat of dry air (1010 J.kg-1.C-1), ρ the density of
the air (1.204 kg.m-3), γ the psychrometric constant (66.1 Pa.C-1) and λ the latent heat of
vaporization (2.454E6 J.kg-1). The proportionality term, gc is dependant on different
environmental variables. In an earlier paper, Bernier et al. (2002) proposed a form of
modifiers that accounts for the effects of radiation and vapour pressure deficit on gc. In the
current model, we used modifiers in a form that permits the use of a wider variety of
external variables for estimating the time course of gc:
cDyTaRguDcxcc fffffffgfgga θ∏ == (3)
where cg is the average canopy conductance (m.h-1), fx are modifiers that account for the
effect of vapour pressure deficit (Da), wind speed (u, m.s-1), global radiation (Rg, W.m-2),
soil water content (θ, m³.m-³), air temperature (Ta, ºC), day of year (Dy) and vegetation
55
cover percentage inside the chamber frame (c) when required. The modifiers are computed
as (Raulier et al., 2000):
2
,,1 ⎟⎠⎞
⎜⎝⎛ −+⎟
⎠⎞
⎜⎝⎛ −+=
xxx
xxxf xqxlx αα (4)
where fx is the modifier for the variable x, x is the mean value of this variable within the
observation data set and αl,x and αq,x are parameters to be estimated along with cg .
Adjustment for S22 and S62 sites was performed on the combined data of both sites by
introducing p, a dummy variable, into Equations 3 and 4:
( )( )γλ
ρβ p
axcc
cDfpgE ∏+= (5)
and
( ) ( )2
,,,,1 ⎟⎠⎞
⎜⎝⎛ −++⎟
⎠⎞
⎜⎝⎛ −++=
xxxp
xxxpf xqxqxlxlx βαβα (6)
where p takes on the value of 0 for the S62 site and of 1 for the S22 site. The difference
between the two sites shows up in the significance of the β, βl,x and βq,x terms. The model
was adjusted separately for sites S2 and S10.
Prior to adjustment to automatic sap velocity measurements, all periods with relative
humidity above 95% were rejected in order to eliminate the possible influence of wet
foliage on transpiration. We also rejected periods with a null or negative global radiation in
order to remove the null or very low nocturnal transpiration rates since the heat pulse
method is of limited value for measuring low rates of sap flow in woody plants (Becker,
1998; Burgess et al., 2001). All other records were used for the empirical fit of the model to
S62 and S22 data. For S2, the model was adjusted to all 56 manual transpiration
measurements using the meteorological variables measured at the permanent station of the
S62 site. For site S10, two models were adjusted, to chamber-measurements and to HPV-
measurements respectively.
56
Eqs. (3) and (4) or (5) and (6) were adjusted to data using the PROC MODEL function of
the SAS software (SAS Institute, Cary, NC). Modifier variables were included using the
forward selection method, starting with the null model (gc = cg ) and retaining at each step
the variable whose modifier explained the highest variation of the dependant variable. Only
variables that explained more than 5% of the variability in observed transpiration rates were
retained. No other provision was made to account for the repeated nature of the
measurements.
Comparing the sites The resulting four transpiration models were applied to the 2001 growing season
(152 < day of year < 242) using climatic variables monitored at the S62 site. The period
over which the four sites were compared required values of environmental variables that
fell within the domain of all four datasets used in adjusting the transpiration models to the
four sites. The most limiting range was that of sites S2 and S10 for the manual chamber
measurements. For example, the initial adjustment to S10 HPV data included, among others,
solar radiation and day of year as significant variables. However, this inclusion greatly
limited the number of half-hourly periods over which all four sites were compared. We
therefore chose to omit selected variables from some of the models in a trade-off between
model adjustment and the capacity to compare the four sites. The environmental conditions
met for the remaining 496 half-hourly observations are presented in Table III-2. In
herbaceous and seedlings transpiration models for S2 and S10 sites, the vegetation cover
modifier (fc) was taken as equal to 1 in order to represent the mean vegetation cover (Eq. 4).
Table III-2 - Environmental conditions during the 2001 comparison season.
Variable Da (Pa) u (m.s-1) Rg (W.m-2) θ Ta (C) Dy Minimum 804.70 0.45 229.00 0.17 11.72 152.39 Maximum 1585.60 9.15 1181.00 0.25 26.88 237.71 Mean 1182.62 2.87 623.97 0.20 19.84 195.83 Da: vapor pressure deficit, u: wind speed, Rg: global radiation, θ: soil water content, Ta: air temperature, Dy: day of year An analysis of variance was performed on the predicted transpiration rates for 2001 through
PROC GLM (SAS Institute, Cary, NC). The analysis was weighted by the inverse of the
variances of the predicted transpiration rates in order to account for model errors (Draper
57
and Smith, 1981). For site S10, the variance was equal to the sum of the models variances
weighted by the squares of the corresponding percentages of the tree and herbaceous
covers. The least significant difference (LSD) procedure was then used to characterize the
differences between the predicted mean transpiration rates by pairwise tests.
Attenuation coefficient The Ca (attenuation coefficients of the hydrological effects of harvesting, %) were
calculated as the differences between the transpiration rate estimated for 2001 at each site
to the one at the mature site. Hence, the differences were fixed at 0 and 100% respectively
for the S62 and S2 sites:
dsmaturecdsopenc
dsmaturecsitecsitea EE
EEC
tan,tan,
tan,,, 100
−−
= (7)
Functions were adjusted by regression to Ca data using the PROC MODEL function of the
SAS software (SAS Institute, Cary, NC):
2,,,0 xxC xqxlxa ααα ++= (8)
where the variable x is a stand property (total basal area (b), balsam fir mean height (h) or
stand age (a)) and α0,x, αl,x and αq,x are parameters to be estimated.
Comparison of Ca based on transpiration rates versus Ca based on snowmelt rates Prior to this study, values of Ca had been calculated by Talbot and Plamondon (2002) from
snowmelt rates measurements in various stands of Montmorency Forest. We therefore
compared values of Ca estimated from transpiration rates to values of Ca based on
snowmelt rates. For the comparison a square root transformation was performed on basal
area in the data set based on transpiration as it was done by Talbot and Plamondon (2002)
for their snowmelt data set. We combined our values of Ca and those of Talbot and
Plamondon (2002) and adjusted a modified version of Eq. 8 in which we had introduced a
dummy variable (z) that took on a value of 0 for our data and a value of 1 for the data of
Talbot and Plamondon:
58
( ) ( ) ( )( ) ( ) bzzC
xzxzzC
blblbba
xqxqxlxlxxa
,,,0,0
2,,,,,0,0
γαγα
γαγαγα
+++=
+++++= (9)
We used the MODEL procedure of SAS (SAS Institute, Cary, NC) to perform the
adjustment.
Results and discussion
The transpiration models The forward analysis of the models for both the S22 and S62 sites reveals that only the
modifier based on Da explains more than 5% of the variability in half-hourly transpiration
rates. Adjustment of the resulting transpiration model to the field data shows a very good
correspondence between predicted and observed stand-level transpiration, with adjusted R²
(R²adj) value of 0.75 (Mathieu et al., submitted). Residuals (measured minus predicted Ec)
show no apparent bias with respect to predicted Ec (Figure III-2 a) nor to Da, θ, u, Rg, Ta, Dy
(Mathieu et al., submitted).
The adjustment of the model to the HPV measurements obtained on the trees of S10 reveals
that the variables Da, Dy and Rg each explain more than 5% of the transpiration variability.
For the present study, however, we chose to exclude Rg and Dy in order to preserve a larger
prediction domain. Exclusion of these two variables reduces the R²adj from 0.72 to 0.45, and
increases the RMSE (Root Mean Square Error) from 0.0172 to 0.0240. The residuals of the
resulting model show no apparent bias with respect to predicted Ec (Figure III-2 b) nor to
Da, θ, u, Ta (data not shown). The Dy effect is quadratic with a maximum on July 14th. This
effect may be phenological but is not present in the older stands. The Rg modifier was also
quadratic with a maximum at 700 W.m-2 then nearly stagnated along an asymptote up to
1080 W.m-2.
Adjustment of the herbaceous and seedlings model for site S10 reveals that the variables Da,
Rg, c and θ each explain more than 5% of the transpiration variability. We also chose to
exclude θ in order to preserve a larger prediction domain. Values of transpiration simulated
by the resulting model are in good agreement with field data with R²adj values of 0.47
and 0.38 before and after simplification respectively. Residuals show no bias with respect
when x = a or h
when x = b
59
to predicted Ec (Figure III-2 c) nor to Da, u, Rg, Ta, Dy (data not shown). The θ modifier was
linear with a slope of 2.414 within the small range of 0.154-0.189 m³.m-³.
The mean transpiration rates of the balsam fir and ground vegetation in site S10 for the 2001
data set were of 0.0917 and 0.1511 mm.h-1 respectively. The difference in transpiration
rates is likely to be the consequence of leaf area difference.
Adjustment of the herbaceous and seedlings model for site S2 reveals that the modifiers
based on Rg, c, Da and θ each explain more than 5% of the transpiration variability. We
chose to exclude Da and θ from the final model, making the R²adj to drop from 0.61 to 0.40.
The comparison between modeled and measured transpiration showed no bias in the
predictions (Figure III-2 d). The Da modifier was linear with a slope of –1.208 in the Da
range of 753 to 1614 Pa. The θ modifier was linear with a slope of 35.869 but the θ range
of 0.186-0.191 m³.m-³ was to small to be meaningfull.
Parameters estimates, mean values of variables used in equations (4) and (6) and statistics
are listed in Table III-3.
60
-0.25-0.20-0.15-0.10-0.050.000.050.100.150.200.25
0 0.1 0.2 0.3 0.4 0.5
Ec predicted (mm.h-1)
Res
idua
ls (m
m.h
-1)
-0.20-0.15-0.10-0.050.000.050.100.150.20
0 0.1 0.2 0.3
Ec predicted (mm.h-1)R
esid
uals
(mm
.h-1
)
a b
c d
-0.08-0.06-0.04-0.020.000.020.040.060.080.100.12
0 0.05 0.1 0.15 0.2
Ec predicted (mm.h-1)
Res
idua
ls (m
m.h
-1)
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.00 0.05 0.10 0.15 0.20 0.25 0.30
Ec predicted (mm.h-1)
Res
idua
ls (m
m.h
-1)
-0.25-0.20-0.15-0.10-0.050.000.050.100.150.200.25
0 0.1 0.2 0.3 0.4 0.5
Ec predicted (mm.h-1)
Res
idua
ls (m
m.h
-1)
-0.20-0.15-0.10-0.050.000.050.100.150.20
0 0.1 0.2 0.3
Ec predicted (mm.h-1)R
esid
uals
(mm
.h-1
)
a b
c d
-0.08-0.06-0.04-0.020.000.020.040.060.080.100.12
0 0.05 0.1 0.15 0.2
Ec predicted (mm.h-1)
Res
idua
ls (m
m.h
-1)
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.00 0.05 0.10 0.15 0.20 0.25 0.30
Ec predicted (mm.h-1)
Res
idua
ls (m
m.h
-1)
Figure III-2 - Residuals (measured - predicted canopy transpiration Ec) as a function of predicted Ec for (a) transpiration model for sites S22 and S62 ; (b) tree transpiration model for site S10 ; (c) herbaceous plants and seedlings transpiration model for site S10 and (d) transpiration model for site S2.
61
Table III-3 - Transpiration rates models statistics.
Parameter Estimated value
Standard error
t value
Pr > |t|
cg (m.h-1) 37.50569 0.3778 99.28 <0.0001β -8.41346 0.5549 -
15.16 <0.0001
αl,Da -0.25418 0.0102 -24.97
<0.0001
S62 and S22 model =aD 635.9 Pa
βl,Da 0.092273 0.0179 5.16 <0.0001cg (m.h-1) 13.88856 0.1434 96.85 <0.0001
αl,Da -0.50418 0.0146 -34.43
<0.0001S10 model for balsam firs at sapling stage
=aD 769.2 Pa αq,Da 0.100745 0.00558 18.06 <0.0001
cg (m.h-1) 27.51467 2.4056 11.44 <0.0001αl,Da -1.35064 0.1860 -7.26 <0.0001αl,Rg 0.536369 0.1009 5.31 <0.0001αl,c 0.918275 0.2378 3.86 0.0002
S10 model for herbaceous plants and seedlings
=aD 1040.5 Pa =gR 945.0 W.m-2
=c 76.5 % αq,c 3.428969 1.2053 2.84 0.0058
cg (m.h-1) 12.70679 0.8357 15.20 <0.0001αl,Rg 0.609544 0.1635 3.73 0.0005
S2 model =gR 683.0 W.m-2
=c 61.2 % αl,c 0.442447 0.1311 3.37 0.0014
Comparing the stands The mean transpiration rates predicted for 2001 and their variances for the four sites are
presented in Table III-4. The LSD procedure on the mean transpiration rates shows that all
sites are significantly different from each other (α = 5%).
Table III-4 - Mean predicted transpiration rates and their variances for the four sites in 2001.
Site Mean Ec Standard error Pr > |t| S62 0.25401732 0.00094917 <0.0001 S22 0.21906291 0.00094942 <0.0001 S10 0.13163421 0.00150400 <0.0001 S2 0.10707881 0.00149889 <0.0001
Coefficients of attenuation of the effects of harvesting The Ca coefficients calculated in this study from the transpiration rates were correlated with
stand properties, decreasing with an increase of age (A), canopy height (H) or basal area
62
(G) (Table III-5). The quadratic relations show a good adjustment with R²adj values of 0.84,
0.85 and 0.87 for age, height and basal area respectively. A Ca value of 50% was reached at
a canopy height of 5.3 m, a basal area of 12.6 m².ha-1 or a stand age of 16 years (Figure III-
3). Talbot and Plamondon (2002) report that the effect of clearcutting on mean snowmelt
rates in Montmorency forest was reduced by 50% when height and basal area reach 4 m
and 16 m².ha-1 respectively, 15 years after the clearcut.
Table III-5 - Statistics of the models of coefficient of attenuation of hydrological effects after harvesting.
Stand characteristic variable Parameter Estimated value Standard error t value Pr > |t| α0,A 116.6816 0.8571 136.13 <0.0001 αl,A -4.99527 0.0853 -58.53 <0.0001 αq,A 0.050077 0.00124 40.44 <0.0001 γ0,A 14.85691 7.9908 1.86 0.0631 γl,A -1.53614 0.7849 -1.96 0.0505
Age
γq,A 0.031596 0.0130 2.43 0.0154 α0,H 111.2355 0.7777 143.03 <0.0001 αl,H -13.7659 0.2600 -52.95 <0.0001 αq,H 0.422074 0.0143 29.57 <0.0001 γ0,H 16.96237 7.8678 2.16 0.0312 γl,H -10.6087 3.1071 -3.41 0.0007
Height
γq,H 0.771688 0.2088 3.70 0.0002 α0,G 103.8615 0.6238 166.50 <0.0001 αl,G -5.05575 0.0851 -59.39 <0.0001
Basal area Equation (8)
αq,G 0.06093 0.00181 33.68 <0.0001 α0,G 106.7808 0.6956 153.51 <0.0001 Basal area
Equation (9) αl,G -16.1773 0.1662 -97.34 <0.0001
63
-40
-20
0
20
40
60
80
100
120
140
0 5 10 15 20
Canopy height (m)
Att
enua
tion
coef
ficie
nt (%
)
-40
-20
0
20
40
60
80
100
120
140
0 10 20 30 40 50 60 70
Age (years)
Att
enua
tion
coef
ficie
nt (%
)
-40
-20
0
20
40
60
80
100
120
140
0 10 20 30 40 50 60
Basal area (m²/ha)
Att
enua
tion
coef
ficie
nt (%
)
a
b
c
-40
-20
0
20
40
60
80
100
120
140
0 5 10 15 20
Canopy height (m)
Att
enua
tion
coef
ficie
nt (%
)
-40
-20
0
20
40
60
80
100
120
140
0 10 20 30 40 50 60 70
Age (years)
Att
enua
tion
coef
ficie
nt (%
)
-40
-20
0
20
40
60
80
100
120
140
0 10 20 30 40 50 60
Basal area (m²/ha)
Att
enua
tion
coef
ficie
nt (%
)
-40
-20
0
20
40
60
80
100
120
140
0 5 10 15 20
Canopy height (m)
Att
enua
tion
coef
ficie
nt (%
)
-40
-20
0
20
40
60
80
100
120
140
0 10 20 30 40 50 60 70
Age (years)
Att
enua
tion
coef
ficie
nt (%
)
-40
-20
0
20
40
60
80
100
120
140
0 10 20 30 40 50 60
Basal area (m²/ha)
Att
enua
tion
coef
ficie
nt (%
)
a
b
c
Figure III-3 – Coefficients of attenuation of hydrological effects after harvesting, based on canopy transpiration rates (full circles) and based on snowmelt rates (empty circles and curve) as a function of (a) canopy height; (b) basal area and (c) stand age.
64
Comparison of Ca based on transpiration rates versus Ca based on snowmelt rates using the
dummy variable approach of Eq. 9 shows a significant difference between the two
responses with age (likelihood ratio χ² = 13.99, P value = 0.0029) in the quadratic term γq,A
(α = 5%) (Table III-5) and with tree height (likelihood ratio χ² = 15.46, P value = 0.0015).
However, the Ca obtained from both approaches are near the same between 0 and 40 years.
Beyond this age or an height of 10 m, the higher canopy and longer live crowns were
considered to have enhanced snowmelt by increasing downward long waves radiations
(Talbot and Plamondon, 2002). This explains the significant difference between the Ca
relations calculated from transpiration and snowmelt. The relation between basal area and
Ca obtained from transpiration was similar to the one calculated from snowmelt (likelihood
ratio χ² = 2.69, P value = 0.2602).
The hydrological recovery using transpiration measurements is in agreement with the one
obtained from snowmelt which strengthens the overall result of this study. The estimate of
a 50% recovery at a height of 4 to 5.3 m or at a basal area of 12.6 to 16 m².ha-1 is likely
quite robust for balsam fir stands of the type found in the Montmorency Forest. The age-
based estimate may be less desirable since it is not related to any underlying process while
the relationships with height or basal area are dependent on growth rates. We would further
argue that basal area is a superior predictive variable as it is better related to the crown
closure, leaf area and evapotranspiration than height does. Stand height can have equal
values in stands of vastly different structures and evapotranspiration potentials. We
therefore strongly recommend using basal area as the independent variable for predicting
the attenuation of the harvesting effects. As a result, because no differences could be found
between snowmelt-based and transpiration-based estimates of Ca related to basal area, we
recommend using the function adjusted to the combined dataset (R²adj = 0.82), whose
parameter values are shown in Table III-5.
Conclusion The estimated values of the Ca coefficient for the 2, 12 and 22 years old stands based on
transpiration are similar to those obtained from snowmelt measurements. The similarities
between both approaches, particularly when the Ca coefficients are correlated with basal
area, reveal that the canopy changes have an analogous effect on transpiration and
65
snowmelt. This tends to support the hypothesis that the Ca coefficient obtained from
snowmelt is a good index for the recovery of snowmelt and rainfall peak flows. However,
the Ca coefficient based on transpiration rates including the 62 years old stand appears to
indicate that the hydrological recovery of the rainfall peak flow may be slightly longer than
the one from the snowmelt peak flow. This could be better determined by transpiration
measurements in other 20 to 60 years old stands but we suggest that it is not warranted. In
practice, hydrologic recovery was considered by Talbot and Plamondon (2002) to be
complete around 35 years after the cutting or as the stand reached 10 m in height. The Ca
obtained from transpiration does not contradict these threshold values for forest
management. The snowmelt measurements are less complex and expensive than the
transpiration ones. On the other hand, the access to forested areas may be limited during
winter and the Ca coefficient based on snowmelt is adequate only for areas with a
significant snow cover. Finally, the relations obtained between the Ca coefficient based on
transpiration and the stand properties are satisfying for guiding the forest manager. Further
to this analysis, we recommend extending the work to other boreal species using either one
of the two approaches (transpiration or snowmelt). However, as an interim measure, the
attenuation function presented in Table III-5 using basal area could be used for other boreal
conifers, with the proviso that it be re-adjusted by pro-rating the basal areas using the ratio
of the maximum basal area for the species or conditions being addressed to the basal area of
stand S62. This adjustment would locally adapt the attenuation curve.
Acknowledgements The authors would like to thank Marie-Ève Roy, Jean-Philippe Brunet, Daniel Breton and
Claudia Roberge for help in the field, Sébastien Dagnault for provision of meteorological
data and Marie-Claude Lambert for statistical support in SAS analyses. The study was
supported by the Ministère des Ressources naturelles du Québec (Fonds forestier), the
Canadian Forest Service – Laurentian Forestry Centre (Ecoleap project) and the National
Science and Engineering Research Council of Canada.
66
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Plamondon AP. 2004. La récolte forestière et les débits de pointe - État des connaissances sur la prévision des augmentations des pointes, le concept de l'aire équivalente de coupe acceptable et les taux régressifs des effets de la coupe sur les débits de pointe. Québec: Direction de l'environnement forestier, Ministère des Ressources naturelles du Québec. 236 p.
Robitaille A, and Saucier J-P. 1998. Paysages régionaux du Québec méridional, Sainte Foy, Québec. 213.
Smith DM, and Allen SJ. 1996. Measurement of sap flow in plant stems. Journal of Experimental Botany 47, 1833-1844.
Swanson RH. 1994. Significant historical developments in thermal methods for measuring sap flow in trees. Agricultural and Forest Meteorology 72, 113-132.
Talbot J, and Plamondon AP. 2002. The diminution of snowmelt rate with forest regrowth as an index of peak flow hydrologic recovery, Montmorency Forest, Quebec. 59th Eastern Snow Conference. Stowe, Vermont USA, 22, 85-92.
68
Conclusion générale Dans le premier chapitre, nous avons démontré que la transpiration et la conductance de la
canopée de deux couverts forestiers fermés pouvaient être modélisées en utilisant une
approche simple, et cependant puissante, qui permet l’inclusion de multiples variables
externes (rayonnement solaire, déficit de pression de vapeur, température de l’air, vitesse
du vent, teneur en eau du sol) dans l’équation prédictive, en plus de la prise en compte des
interactions entre les variables. Le modèle est basé sur le concept de valeurs moyennes et
d’écarts par rapport à la moyenne des variables. La plupart des modèles écophysiologiques
qui dépendent d’une conductance du couvert, utilisent des valeurs maximum qui sont
ensuite réduites par des facteurs limitants. Néanmoins, les deux approches ne sont pas
irréconciliables, puisqu’il est possible d’utiliser les équations ajustées dans cette étude pour
calculer les valeurs réelles de conductance du couvert pour de longues séries
météorologiques, et définir une valeur maximale basée sur la fréquence des valeurs
estimées.
Le second chapitre a permis de démontrer qu’il est essentiel de vérifier par une expérience
de lectures séquentielles si l’espèce d’arbre étudiée réagit à la blessure par des mécanismes
de défense conduisant à une dégradation du signal HPV. Afin d’éviter des sous-estimations
significatives des flux de sève, un facteur de correction peut alors être facilement estimé en
utilisant une méthode simple de modélisation empirique.
Nos valeurs estimées du coefficient d’atténuation des effets hydrologiques de la coupe (Ca)
pour les peuplements de 2, 12 et 22 ans sont similaires à celles obtenues par une étude des
taux de fonte de la neige (Talbot and Plamondon, 2002). Les similarités entre les Ca
obtenus par les deux approches, transpiration et fonte de la neige, particulièrement quand
ils sont corrélés avec la surface terrière, révèlent que les changements du couvert ont un
effet semblable sur la transpiration et la fonte de la neige. Cela montre aussi que le Ca
obtenu à partir de la fonte de la neige est un bon indice pour la récupération du débit de
pointe de fonte et de pluie. Toutefois, le Ca basé sur les taux de transpiration incluant le
peuplement de 62 ans indique que la récupération hydrologique du débit de pointe de pluie
pourrait être légèrement plus longue que celle du débit de pointe de fonte. Les mesures de
fonte de la neige sont moins complexes et coûteuses que les mesures de transpiration.
69
Néanmoins, l’accès aux régions forestières peut être limité pendant l’hiver et le Ca basé sur
la fonte de la neige n’est adéquat que pour des régions avec une couverture neigeuse
significative. Finalement, les relations obtenues entre le coefficient Ca basé sur la
transpiration et les propriétés du peuplement sont satisfaisantes pour guider les
gestionnaires de la forêt mais peuvent être améliorées en augmentant le nombre de
peuplements échantillonnés.
Référence
Talbot J, and Plamondon AP. 2002. The diminution of snowmelt rate with forest regrowth as an index of peak flow hydrologic recovery, Montmorency Forest, Quebec. 59th Eastern Snow Conference. Stowe, Vermont USA, 22, 85-92.
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