Climate Change Management

Walter Leal Filho · Gustavo J. Nagy · Marco Borga · Pastor David Chávez Muñoz · Artur Magnuszewski   Editors

Climate Change, Hazards and Adaptation OptionsHandling the Impacts of a Changing Climate

Climate Change Management

Series Editor

Walter Leal Filho, International Climate Change Information and ResearchProgramme, Hamburg University of Applied Sciences, Hamburg, Germany

The aim of this book series is to provide an authoritative source of information onclimate change management, with an emphasis on projects, case studies andpractical initiatives – all of which may help to address a problem with a global scope,but the impacts of which are mostly local. As the world actively seeks ways to copewith the effects of climate change and global warming, such as floods, droughts,rising sea levels and landscape changes, there is a vital need for reliable informationand data to support the efforts pursued by local governments, NGOs and otherorganizations to address the problems associated with climate change.

This series welcomes monographs and contributed volumes written for anacademic and professional audience, as well as peer-reviewed conference proceed-ings. Relevant topics include but are not limited to water conservation, disasterprevention and management, and agriculture, as well as regional studies anddocumentation of trends. Thanks to its interdisciplinary focus, the series aims toconcretely contribute to a better understanding of the state-of-the-art of climatechange adaptation, and of the tools with which it can be implemented on the ground.

More information about this series at http://www.springer.com/series/8740

Walter Leal Filho • Gustavo J. Nagy •

Marco Borga • Pastor David Chávez Muñoz •

Artur MagnuszewskiEditors

Climate Change, Hazardsand Adaptation OptionsHandling the Impacts of a Changing Climate

123

EditorsWalter Leal FilhoHAW HamburgHamburg, Germany

Gustavo J. NagyOceanografia y Ecologia Marina.Coastal Climate ScienceUniversidad de la República de UruguayMontevideo, UruguayMarco Borga

Department of Land, Environment,Agriculture and ForestryUniversity of PadovaLegnaro, Italy

Pastor David Chávez MuñozDepartment of EngineeringPontifical Catholic University of PeruLima, Peru

Artur MagnuszewskiDepartment of HydrologyUniversity of WarsawWarsaw, Poland

ISSN 1610-2002 ISSN 1610-2010 (electronic)Climate Change ManagementISBN 978-3-030-37424-2 ISBN 978-3-030-37425-9 (eBook)https://doi.org/10.1007/978-3-030-37425-9

© Springer Nature Switzerland AG 2020This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or partof the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmissionor information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilarmethodology now known or hereafter developed.The use of general descriptive names, registered names, trademarks, service marks, etc. in thispublication does not imply, even in the absence of a specific statement, that such names are exempt fromthe relevant protective laws and regulations and therefore free for general use.The publisher, the authors and the editors are safe to assume that the advice and information in thisbook are believed to be true and accurate at the date of publication. Neither the publisher nor theauthors or the editors give a warranty, expressed or implied, with respect to the material containedherein or for any errors or omissions that may have been made. The publisher remains neutral with regardto jurisdictional claims in published maps and institutional affiliations.

This Springer imprint is published by the registered company Springer Nature Switzerland AGThe registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

Hazards may be defined as the potential occurrence of a natural or human-inducedphysical event, trend or physical impact, which may cause loss of life, injury orother health impacts, as well as damages and/or losses to property, infrastructure,livelihoods, service provision and environmental resources.

Due to climate change, the frequency and intensity of hazard events such asextreme weather events (e.g. floods, droughts and heatwaves) on the one hand, butalso of forest fires and damages to agricultural productivity (especially crop yield)on the other, are expected to increase in the future. Hazards may change exposurepatterns, lead to substantial breakdowns of infrastructure, damages to property andultimately decrease the resilience of households and communities. Moreover, thelinks between climate hazards and health are strong, with mortality and morbidityrates rising as a result of them. Hazards pose additional pressures to both humanand natural systems. The Intergovernmental Panel on Climate Change (IPCC)outlined on its 4th (2007) and 5th (2014) Assessment Reports the need to managethe risks of extreme events and the hazards they bring about, to advance climatechange adaptation.

It is important to better understand what climate change hazards are, how thedifferent components of climate change including frequency, intensity, variabilityand uncertainty play together and relate to ecosystems, and how a better under-standing of vulnerability of people to climate change in terms of sensitivity can beachieved. Also, it is equally important to identify adaptation options based on theadaptive capacity—which does vary—between sectors, populations and ecosystems.

This book is an attempt to address the need for interdisciplinary publications,which look at the subject matter of climate change and hazards on the one hand, butwhich outline adaptation options on the other. It contains articles presented anddiscussed at a series of events organised by the International Climate ChangeInformation and Research Programme (ICCIRP), held in Lima (Peru), Padova(Italy) and Warsaw (Poland) in 2019. The events gathered a set of specialists fromdifferent backgrounds and disciplines, whose know-how is amassed and docu-mented on this publication. It is structured in two main parts:

v

Understanding Climate Change Hazards: In this part, a variety of studies andtechnical assessments are showcased, aimed at leading to a better understanding ofwhat climate hazards are and their implications.Handling the Impacts of Climate Hazards via Adaptation Methods and Options: Inthis part, a wide range of experiences and case studies is presented, which illustratethe many ways via which climate hazards are being dealt with, along with the roleof education and communication.

Thanks to its scope, the book Climate Change, Hazards and Adaptation Optionsnot only provides essential scientific information, but also describes facts, trendsand case studies from various geographical regions.

We hope this book will foster a broader understanding of the subject matter ofclimate hazards and risks and will support the search for suitable adaptation mea-sures so as to ensure that their impacts may be minimised.

Hamburg, Germany Walter Leal FilhoMontevideo, Uruguay Gustavo J. NagyLegnaro, Italy Marco BorgaLima, Peru Pastor David Chávez MuñozWarsaw, Poland Artur Magnuszewski

vi Preface

Contents

Understanding Climate Change Hazards

Hydrometeorological Analysis of an Extreme Flash-Flood:The 28 September 2012 Event in Murcia, South-Eastern Spain . . . . . . 3A. Amengual and Marco Borga

A Multiple Linear Regression-Based Approach for Storm SurgePrediction Along South Brazil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27Arthur Ohz, Antonio H. F. Klein and Davide Franco

Hydrodynamic Study of Free Standing Drilling Riser UnderHurricane Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51Hong Yaw Yong, M. S. Liew, Mark Ovinis, Kamaluddeen U. Danyaroand Eu Shawn Lim

Assessing Contribution of Climate Change on Wetlandsby Using Multi-temporal Satellite Data . . . . . . . . . . . . . . . . . . . . . . . . . 77Nebiye Musaoglu, Adalet Dervisoglu, Nur Yagmur, Baha Bilgilioglu,Aylin Tuzcu and Aysegul Tanik

City-scale Modeling of Urban Heat Islands for Kolkata . . . . . . . . . . . . 89Ansar Khan, Soumendu Chatterjee, Walter Leal Filho, Rupali Khatun,Apurba Dinda and Aprajita Minhas

GIS Hazard Assessments as the First Step to Climate ChangeAdaptation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135Nelson Rangel-Buitrago, Adriana Gracia C., Giorgio Anfusoand Jarbas Bonetti

Landscape Ecology and Conservation for Building Resilienceand Adaptation to Global Change in Venezuela . . . . . . . . . . . . . . . . . . 147Eulogio Chacón-Moreno, Isabel Olivares, Georgina Navarro,Anderson J. Albarrán, Yorman Paredes, Carla I. Arangurenand Gustavo J. Nagy

vii

Influence of Vegetation in the Creation of Urban Microclimates . . . . . 161Bruna Giacomelli, Júlia Calvaitis Padilha,Paula Renata Albrecht Mantovani, Fabiane Bencheand Natalia Hauenstein Eckert

Preliminary Evaluation of Emergency Shelters for DisastersAssociated with Landslides at the Hydrographic Basin of CorregoD’Antas, Nova Friburgo, Rio de Janeiro, Brazil . . . . . . . . . . . . . . . . . . 177Tomás Coelho Netto Duek, Leonardo Esteves de Freitasand Marcos Barreto de Mendonça

Post-catastrophic Disaster Induced Laws for Climatic ChangeAdaptation: A Case Study in SE-Brazil . . . . . . . . . . . . . . . . . . . . . . . . 197Leonardo Esteves de Freitas, Raiza Fernandes da Silvaand Ana Luiza Coelho Netto

Assessment of Carbon Sequestration Potential of a Disturbed HumidTropical Ecosystem, Southeast Nigeria . . . . . . . . . . . . . . . . . . . . . . . . . 213Chris O. Nwoko, Samuel C. Anuna and Jonathan C. Anyanwu

Flash-Floods: More Often, More Severe, More Damaging?An Analysis of Hydro-geo-environmental Conditionsand Anthropogenic Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225Axel Bronstert, Irene Crisologo, Maik Heistermann, Ugur Ozturk,Kristin Vogel and Dadiyorto Wendi

Landslide Hazard Induced by Climate Changesin North-Eastern Romania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245Mihai Niculiţă

Objective Analysis of Envelope Curves for Peak Floodsof European and Mediterranean Flash Floods . . . . . . . . . . . . . . . . . . . 267William Amponsah, Francesco Marra, Lorenzo Marchi, Hélène Roux,Isabelle Braud and Marco Borga

Treatment of Natural Hazards Within Planning Documentsin Serbia in Relation to Climate Change Issues . . . . . . . . . . . . . . . . . . 277Tijana Crnčević, Jelena Živanović Miljković and Omiljena Dželebdžić

Occurrence and Characteristics of Flash Floods in Bavaria(Germany) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293Maria Kaiser, Marco Borga and Markus Disse

Ozone Layer Holes, Regional Climate Change and Possible Waysfor Their Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311Aliaksandr Krasouski, Siarhei Zenchanka, Veronika Zhuchkevich,Tsimafei Schlender and Henry Sidsaph

viii Contents

The Protective Role of Forests to Reduce Rockfall Risks and Impactsin the Alps Under a Climate Change Perspective . . . . . . . . . . . . . . . . . 333Emanuele Lingua, Francesco Bettella, Mario Pividori, Raffaella Marzano,Matteo Garbarino, Marco Piras, Milan Kobal and Frédéric Berger

Handling the Impacts of Climate Hazards via Adaptation Methodsand Options

Towards a Prioritized Climate Change Management Strategy:A Revisit to Mitigation and Adaptation Policies . . . . . . . . . . . . . . . . . . 351Cosmos Nike Nwedu

Urban Heat Island Effect, Extreme Temperatures and ClimateChange: A Case Study of Hong Kong SAR . . . . . . . . . . . . . . . . . . . . . 369Charles Galdies and Hok Sin Lau

Internal Displacement Due to Disasters in Latin Americaand the Caribbean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 389Roberto Ariel Abeldaño Zuñiga and Javiera Fanta Garrido

Climate Change and Southern Hemisphere Tropical CyclonesInternational Initiative: Twenty Years of Successful RegionalCooperation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 411Yuriy Kuleshov

Solutions Stories: An Innovative Strategy for Managing NegativePhysical and Mental Health Impacts from ExtremeWeather Events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441Emily Coren and Debra L. Safer

Transforming Gendered Lives and Livelihoods in Post-disasterSettings in the Bangladesh Sundarbans Forest . . . . . . . . . . . . . . . . . . . 463Sajal Roy

Strengthening Africa’s Adaptive Capacity to Climate Change:African Union Law and Implications of China’s Beltand Road Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 481Michael Addaney

Legal Recognition of Women’s Role in Combating Desertificationin Africa: The Case for Uganda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 505Charlotte Kabaseke

Women in a Climate Changing World. The Need of a PolicySolution for Cross-Border Displacement . . . . . . . . . . . . . . . . . . . . . . . . 523Beatriz López-Fanjul Díez del Corral

Contents ix

Re-naturing Cities: Impact of Microclimate, Human ThermalComfort and Recreational Participation . . . . . . . . . . . . . . . . . . . . . . . . 545Ruzana Sanusi and Sheena Bidin

Natural Hazards and Climate Change: Lessons and Experiencesfrom Kerala Flood Disaster . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 563Neha Goel Tripathi and Nidhin Davis

The Impact of Extreme Floods on Rural Communities:Evidence from Pakistan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 585Ali Jamshed, Joern Birkmann, Joanna M. McMillan, Irfan Ahmad Ranaand Hannes Lauer

Crop-Livestock Inter-linkages and Climate Change Implicationsfor Ethiopia’s Agriculture: A Ricardian Approach . . . . . . . . . . . . . . . . 615Zenebe Gebreegziabher, Alemu Mekonnen, Rahel Deribe Bekele,Samuel Abera Zewdie and Meseret Molla Kassahun

Post-cyclone Aila and Mobility Rights of the Shora Muslim Womenof the Bangladesh Sundarbans Forest . . . . . . . . . . . . . . . . . . . . . . . . . . 641Sajal Roy

Sanctuary in the City? Climate Change and Internally DisplacedPersons in Harare, Zimbabwe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 659Vincent Itai Tanyanyiwa

Climate Change, Extreme Events and Human Mobilityin Latin America: Exploring the Links Through National Lawsand Policies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 679Fernanda de Salles Cavedon-Capdeville, Erika Pires Ramos,Andrea Cristina Godoy Zamur, Diogo Andreola Serraglio,Ignacio Odriozola, Luiza de Moura Pallone,Fernanda Dalla Libera Damacena, Lilian Yamamotoand Giulia Manccini Pinheiro

Coping with Extreme Weather in Arid Areas, a Case Studyof Uzumba Maramba Pfungwe District, Zimbabwe . . . . . . . . . . . . . . . 701Juliet Gwenzi, Emmanuel Mashonjowa and Paramu L. Mafongoya

Climate Change Induced Soil Compaction: Evaluatingthe Adaptation Measures to Enhance Maize Yields in a TropicalHumid Acidic Soil, Nigeria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 717Chukwudi Nwaogu, Teowdroes Kassahun and Patrick U. S. Eneche

Knowledge Dialogues and Climate Change: Integrating ParticipatoryApproaches in the Design of Ecosystem-Based Adaptation Measuresin the Peruvian Andes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 741Florencia Zapata and Erin Gleeson

x Contents

Effects of Climate Change on the Morphological Stabilityof the Mediterranean Coasts: Consequences for Tourism . . . . . . . . . . . 761Federica Rizzetto

Chennai City and Coastal Hazards: Addressing Community-BasedAdaptation Through the Lens of Climate Change and Sea-Level Rise(CBACCS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 777A. Saleem Khan, M. Sabuj Kumar, R. Sudhir Chella and B. Devdyuti

Selection of Five Rice Varieties (Oryza sativa) Under Salinity Stressin Climate Field Schools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 799Neni Rostini, M. Khais Prayoga, Tualar Simarmata,Mieke Rochimi Setiawati, Silke Stoeber and Kustiwa Adinata

SMART Agriculture and Rural Farmers Adaptation Measuresto Climate Change in Southeast Nigeria: Implications for SustainableFood Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 813P. C. Obasi and C. Chikezie

University’s Inclusion in Providing Climate Services to Farmers:Is It Possible Without Agricultural Agentsand Farmer Facilitators? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 835Yunita T. Winarto, Sue Walker, Rhino Ariefiansyah, Iqbal H. Lisan,Maudy Y. Bestari and Tiara Audina

The Usage of Circular Economy Strategies to Mitigate the Impactsof Climate Change in Northern Europe . . . . . . . . . . . . . . . . . . . . . . . . 853Janis Zvirgzdins, Kaspars Plotka and Ineta Geipele

Public Energy Preferences from the Perspective of ClimateChange Mitigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 875Agne Budzyte

Addressing Climate Change Through Education and Researchin Maritime Energy Management: The Case of World MaritimeUniversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 895Momoko Kitada, Alessandro Schönborn, Aykut I. Ölçer, Fabio Balliniand Dimitrios Dalaklis

Is Green Manure (Azolla pinnata and Sesbania rostrata)a Climate-Resilient Strategy for Rice Farming? . . . . . . . . . . . . . . . . . . 911M. Khais Prayoga, Neni Rostini, Tualar Simarmata,Mieke Rochimi Setiawati, Silke Stoeber and Kustiwa Adinata

An Assessment of the Impacts of Climate Change on African CatfishFingerling (Clarias gariepinus Burchell, 1822) . . . . . . . . . . . . . . . . . . . 925Jude Awoke and Johnny Ogunji

Contents xi

A Novel Transdisciplinary Methodology and Experience to GuideClimate Change Health Adaptation Plans and Measures . . . . . . . . . . . 941Marilyn Aparicio-Effen, James Aparicio, Cinthya Ramallo,Mauricio Ocampo and Gustavo J. Nagy

Potential Biophysical Climate Change Impacts at World NaturalHeritage Sites in the Brazilian Atlantic Forest . . . . . . . . . . . . . . . . . . . 961Felipe Bittencourt, Melina Amoni, Augusto Schmidt and Cecília Loureiro

Towards a Prevention-Driven Adaptation Strategy, as Appliedto Indigenous Peoples’ Internal Climate Migration: Some InputsBased on a Rights-Based Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . 979Armelle Gouritin

Carbon Inventories Implementation as Competitive Strategyin Mexican Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 993Yolanda Mendoza Cavazos, Yesenia Sánchez Tovarand Mariana Zerón Felix

How to Govern Climate Change Without Being Able to Govern:Adaptation Governance in Colombia . . . . . . . . . . . . . . . . . . . . . . . . . . 1009Kerstin Mohr

The Andean Farmers of Peru: Farm-Household SystemVulnerability to Climate-Related Hazards . . . . . . . . . . . . . . . . . . . . . . 1029Mariana Vidal Merino, Diana Sietz, Francois Jost and Uta Berger

The Importance of Climate Change Education in Urban Planning:A Review of Planning Courses at UK Universities . . . . . . . . . . . . . . . . 1045Alice Preston-Jones

When Fighting Climate Change Leads to Better Cities: A Studyof Actions Implemented by 100 Cities in Spain . . . . . . . . . . . . . . . . . . 1069Xira Ruiz-Campillo

xii Contents

Understanding Climate Change Hazards

Hydrometeorological Analysisof an Extreme Flash-Flood: The 28September 2012 Event in Murcia,South-Eastern Spain

A. Amengual and Marco Borga

Abstract Heavy precipitation following a prolonged summer drought led towidespread flash flooding across Andalucía, Murcia and Valencia in south-easternSpain on 27, 28 and 29 September 2012. On September 28, an extreme flash-flooddeveloped when 214 mm of rain fell in 8 h over the semi-arid and medium-sizedGuadalentín River basin up to Paretón (~2800 km2). Six fatalities were reported,hundreds of homes were evacuated and a bridge spanning an ephemeral channelwas undermined as the flood bore routed through normally dry river beds. Currentestimates of flood damage are ofe64 million, including extensive losses in livestockand agriculture. The last event of this magnitude over the Guadalentín occurred on19 October 1973. Availability of high-resolution rainfall estimates from dense rain-gauge networks and radar observations, together with flood response observationsderived from stream-gauge data and post-event surveys, provides the opportunity tostudy the hydrometeorological mechanisms associated with the responsible convec-tive systems and the associated flash-flood. Results show that the basin faced a veryrare rainfall event with extreme intensities and accumulations which, in combinationwith the catchment properties, led to extreme runoff. The distinct soil substrates andbasin morphology led to varied runoff responses that required a multisite calibra-tion of a hydrological model so as to successfully reproduce this flash-flood. Heavyprecipitations resulted from deep convection triggered by local orography as well asthe subsequent passage of a slow-moving mesoscale convective system (MCS). Themotion of the MCS was crucial for exacerbating peak discharges, whereas times topeaks weremodulated by the river network geometry and the temporal distribution ofthe rainfall rates. The roles of the different anthropogenic activities on the mitigationor intensification of the hazardous effects of this extreme event are also highlighted.Finally, some recommendations are proposed in order to mitigate future impacts ofsuch catastrophic floods in a changing climate era.

A. Amengual (B)Grup de Meteorologia, Departament de Física, Universitat de Les Illes Balears, Palma, Mallorca,Spaine-mail: arnau.amengual@uib.es

M. BorgaDepartment of Land, Environment, Agriculture and Forestry, University of Padova, Legnaro, Italye-mail: marco.borga@unipd.it

© Springer Nature Switzerland AG 2020W. Leal Filho et al. (eds.), Climate Change, Hazards and Adaptation Options,Climate Change Management, https://doi.org/10.1007/978-3-030-37425-9_1

3

4 A. Amengual and M. Borga

Introduction

Climate change poses major challenges for current societies given that it will impactmean and extreme regimes. A warming of 0.85 °C has been observed in the averageglobal temperature over the period 1880–2012. Associated with this warming, thenumber of heavy precipitation episodes has likely increasedworldwide since themid-twentieth century. Confidence in a likely increase in either the frequency or intensityof heavy precipitation is highest over Europe (Stocker et al. 2013). In particular, theMediterranean emerges as an especially responsive region to climate change andextremes. Basin-wide, annual mean temperature has increased by 1.4 °C since thelate nineteenth century (Cramer et al. 2018).

Future warming in the Mediterranean region is projected to be associated with aprevailing increase in the annual number of precipitation extremes (Beniston et al.2007). In summer, precipitations are projected to substantially decrease, but thefrequency of extreme rainfalls is expected to increase over large regions (Giorgi andLionello 2008). Heavy rainfall episodes are likely to intensify by 10–20% in allseasons, except summer (Toreti et al. 2013; Toreti and Naveau 2015). Furthermore,high levels of exposure and vulnerability to flash-flooding are currently present acrossthis area.

The Western Mediterranean region is especially prone to heavy precipitation andflash-flooding during late summer and early autumn. The relatively high sea surfacetemperature increases the convective available potential energy of the overlyingmoistairmasses through sensible and latent heat flux exchanges.Togetherwith the intrusionof polar cold air masses aloft, the complex orography and land-sea contrasts promotethe lifting of low-level conditionally unstable air, favouring the triggering of deepmoist convection.

High precipitation rates can remain during several hours over individual catch-ments. This persistence is often associated with prominent orography that anchorsquasi-stationary mesoscale convective systems (MCSs; Doswell et al. 1996; Koliosand Feidas 2010). In the Spanish Mediterranean semi-arid area, many small- tomedium-sized catchments are steep, densely urbanized and close to the coastline.Many of these rivers are ephemeral, with short hydrological response times, anddominated by extreme events of low frequency but high magnitude. All these factorsenhance flood risk and further exacerbate unexpected and extensive flood damage(Camarasa-Belmonte and Segura-Beltrán 2001; Amengual et al. 2007, 2015, 2017).

Within this framework, the extreme flash-flood over the Guadalentín River basinin Murcia, south-eastern Spain, on 28 September 2012 is examined in detail (Fig. 1).Six fatalitieswere reported, hundreds of homeswere evacuated and a bridge spanningan ephemeral stream was undermined as the flood bore routed through normally dryriver beds. Current estimates of flood damage are ofe64million, including extensivelosses in livestock and agriculture. This catchment is a paradigmatic example of thesemi-arid and medium-sized watersheds in Mediterranean Spain that suffer suddenhydrological responses to heavy precipitation.

Hydrometeorological Analysis of an Extreme Flash-Flood … 5

Fig. 1 Top left figure: overview of theWesternMediterranean region. The grey shaded area displaystheCHSdemarcationwhere theGuadalentín basin is located. Bottom centre figure: TheGuadalentínRiver basin up to Paretón. The affected populations, available stream-gauges and reservoirs areindicated as well as tributaries mentioned in the text

The availability of high-resolution rainfall estimates from radar observations anddense automatic rain-gauge networks, togetherwith flowmeasurements from stream-gauge data and maximum discharge estimates from post-event surveys, has providedthe opportunity to highlight themost relevant hydrometeorologicalmechanisms asso-ciated with this flash-flood. The consequences on the hydrological response of thedifferent anthropogenic activities carried out inside the Guadalentín basin have beenassessed as well.

The Study Region

The Guadalentín River is the most important affluent of the Segura River, ending inthe vicinity of Murcia City. This catchment spans over a drainage area of 2848.1 km2

up to Paretón (Fig. 1). The height transition is from above 2000 m in its mountainousheadwaters to 210 m at this hydrometric section. The Guadalentín basin is locatedin one of the most arid regions of Spain. High mountainous ranges shelter this areafrom the passage of the rainfall-bearing Atlantic cold fronts. Thus, precipitationmainly comes from easterly moist flows associated with sub-synoptic scale, less

6 A. Amengual and M. Borga

frequent Mediterranean disturbances. The rainfall regime is typical of a semi-aridclimate: annual precipitations range from barely 300 to above 500 mm, dependingon altitude. Most of the annual amounts are collected as torrential rainfalls duringthe extended warm season.

The Guadalentín River is characterized by a very irregular regime, passing fromlarge periods of very low—or inexistent in many of its ephemeral tributaries—flowsto sporadic flash-floods. Being aware of the recurrent nature of these episodes, theConfederación Hidrográfica del Segura (CHS) demarcation has introduced manystructural elements for water supply, drought mitigation and flood control. In par-ticular, two reservoirs are located along the Guadalentín River up to Paretón. Thesedams are located at the outlets of the Valdeinfierno and Puentes mountainous basins.In addition, an artificial diversion connects directly the river to the MediterraneanSea at Paretón (Fig. 1). Therefore, large discharge and sediment volumes are partiallydiverted into the Mediterranean so as to avoid catastrophic flooding in Murcia City.Despite these structural measures, extreme episodes still pose significant threats tolife and property.

La Rambla de Nogalte forms part of the Guadalentín river network. The basinextension is of 124.3 km2 up to Puerto Lumbreras town, where this ephemeral streamis gauged. The height transition is fromclose to 1100–360m in 30 kmandwith ameanslope of 25% (Gil-Ocina 2016). However, livestock and agricultural activities carriedout downstream of Puerto Lumbreras have produced the stream bed disconnectionwith the Guadalentín River. That is, La Rambla de Nogalte ends in an alluvial fanover the Guadalentín valley (Fig. 1). Therefore, the Biznaga basin collects most partof the water contribution of La Rambla de Nogalte when it is active. Biznaga has anextension of 404.8 km2, spanning over the flattest portion of the Guadalentín basin,the river valley.

The Hydrometeorological Episode: A Brief Description

A strong closed low formed southwest of Portugal on 27 September 2012 to laterslowly move eastward along southern and eastern Spain (Fig. 1). Deep convectionwas promoted in its forward flank due to the lifting of low-level warm and moistconditionally unstable air and its later destabilization by the intrusion of cold airaloft. Subsequent convection organized in bands that were frequently anchored bythe complex orography of the region. Extraordinary torrential precipitations tookplace over Murcia on 28 September 2012 due to a V-shaped MCS that developedalong a low-level convergence line in front of the coastline (Ducrocq et al. 2014).

According to the rain-gauge networks, daily precipitations were up to 240 mmover the Guadalentín River basin. Most of heavy precipitation occurred between06 and 14 UTC on 28 September 2012, with recorded accumulations up to 37 and119mm in 5min and 1 h, respectively. Amaximum cumulative of 214mm in 8 h wasregistered in Puerto Lumbreras. In Lorca town, the observedmaximumdischargewasof 616.3 m3 s−1 at 13:15 UTC. At Paretón, two almost consecutive peak discharges

Hydrometeorological Analysis of an Extreme Flash-Flood … 7

of 1067.9 and 1081.2 m3 s−1 at 16:00 and 17:20 UTC were registered. Peak flows upto 939.7 m3 s−1 were diverted into the Mediterranean Sea by the artificial diversion.

This extreme flash-flood produced 6 fatalities and the evacuation of many inhabi-tants in Puerto Lumbreras andLorca towns.Material losseswere of 64Me, as severalinfrastructures were destroyed and extensive livestock and agricultural areas wereflooded. This natural hazard is known as the San Wenceslao flash-flood. In Spain,it is customary to name these extremes with the Christian Saint of the occurrenceday. It is important to note that the Valdeinfierno and Puentes reservoirs were bothclosed during the entire event. Therefore, no discharge contributed downstream forthese basins. Further details about the study region, Guadalentín River basin andhydrometeorological episode can be found in Amengual et al. (2015).

Analysis of the San Wenceslao Flash-Flood

The San Wenceslao flash-flood exemplifies the impact of torrential precipitationsover a basin with persistent dry soils. The lithology of the Guadalentín features largespatial heterogeneities: most parts of the basin are settled on karstic and dolomiticfractured bedrocks, but others areas lay over relative impermeable metamorphicsubstrates (e.g. phyllites, schists, quartzites and micachists). A clear example ofthe spatial heterogeneities is found in the neighbouring La Rambla de Nogalte andBiznaga basins. The former is highly impermeable, whereas the latter is extremelypermeable, despite being located a few kilometres away (Fig. 1).

Figure 2 shows the climatological soil moisture of the Guadalentín River basin,based on the computation of the daily water balance over the 2000–2014 period.

0

0.1

0.2

0.3

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Volu

met

ricw

ater

cont

ent(

m3 /m

3 )

Month

Fig. 2 Mean monthly climatological values of the soil moisture over the Guadalentín basin for the2000–2014 period. Vertical bars denote the standard deviation

8 A. Amengual and M. Borga

0.01

0.1

140 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340

P (Q≥q

)

pcp (mm/24h)

0.01

0.1

10 200 400 600 800 1000 1200

P (Q≥q

)

Q (m3s-1)

(b)(a)

Fig. 3 Annual observed a maximum daily precipitation amounts and b hourly peak flows rep-resented as cumulative distribution functions on a Gumbel chart. Black solid dots denote the 28September 2012 episode. Recall that the 15-year time-series span over the 2000–2014 period

Monthly mean climatic volumetric water content is less than—or close to—0.25throughout the entire hydrological year. Thus, persistent low antecedent water con-tents and high soil moisture capacities are inherent to this semi-arid catchment.Sparse vegetation, thin soils and convective precipitations—which easily exceedsthe high initial soil infiltration capacity—lead to the generation of fast Hortoniansurface flows and rapid flow velocities in the river streams.

Intense precipitations on 28 September 2012 ended a long summery droughtperiod with very warm conditions. This episode can be considered as a paradigm ofthe organized convective systems that are likely responsible for much of flash-floodsin this region. Two previous similar extreme events were recorded on 19 October1973 and 2 September 1989.

An annual observed frequency analysis of the daily rainfall amounts and hourlypeak discharges have been performed over the same 15-year period. At this aim, 28automatic pluviometric stations and two stream-gauges deployed in the GuadalentínRiver basin have been considered (Fig. 1; see next section). The frequency analysisshows the rarity of the San Wenceslao flash-flood, which dominates the upper tail ofthe precipitation and runoff frequency distributions (Fig. 3).

Observed Databases and Precipitation Analysis

The region is monitored by the radar network of the Spanish Agency of Meteorology(AEMET). This network consists of a set of Doppler dual-polarized C-band radarslocated close to themost important cities of the country. Reflectivity scans are carriedout with spatial and temporal resolutions of 1 km and 10 min, respectively. Threedifferent radars cover the Guadalentín catchment and they are located in Murcia,Almería and Valencia cities. Out of these, only the Almería and Valencia radars were

Hydrometeorological Analysis of an Extreme Flash-Flood … 9

entirely operative during the 28 September 2012 episode. The closest weather radarto the Guadalentín (the Murcia radar, 50 km away) was severely affected by heavyprecipitations, being off-line almost all the day. The Almería and Valencia radarswere operative, being approximately located 100 and 200 km south and north of thebasin, respectively.

Raw precipitation is observed with a temporal resolution of 5 min by a dense rain-gauge network of 108 automatic stations that are distributed over the CHS demarca-tion (Amengual et al. 2015). Out of these, 28 rain-gauges were selected to evaluatethe hourly radar-derived rainfalls against the pluviometric values, because being sitedwithin or very close to the Guadalentín basin. The rain-gauge spatial density is of~125 km2 (Fig. 2). 5-min runoff data are available at three different flow-gaugesalong the basin. These stream-gauges are located in Puerto Lumbreras, Lorca andParetón (Fig. 1). 5-min stage data are also available at Valdeinfierno and Puentesdams. In addition, valuable post-flood field information was gathered at differenthydrometric sections after the San Wenceslao flash-flood as well (Table 1).

Quantitative rainfall estimations have been derived from the Almería radar reflec-tivity scans from 27 to 29 September 2012 00 UTC. These observations were takenover a highly rouged terrain, which partially shielded the radar beams. Partial beamblocking was amended by using the procedure developed by Pellarin et al. (2002).Next, the WSR-88D convective relationship was used to convert the 10-min radarreflectivity to precipitation estimations (Hunter 1996). Finally, the hourly radar rain-fall estimations were compared against observations from the selected rain-gauges.Biases in the precise hourly rainfall spatial distributions and amounts where cor-rected by applying a dynamical adjustment based on the pluviometric data and themean fields (Cole and Moore 2008). Once the errors were removed, the statisticalcomparisons between the hourly rain-gauge and radar-derived precipitation fieldsdepicted a good agreement, with a squared-correlation over 0.85.

Table 1 Available hydrological information over the Guadalentín basin for the San Wenceslaoflash-flood

Hydrometric section Basin area (km2) Available information

Valdeinfierno 430.6 Real-time reservoir stage time-series.Stage-discharge relationship not available

Puentes 1441.6 Real-time reservoir stage time-series.Stage-discharge relationship not available

Torrealvilla 236.4 Post-event survey (Hooke 2016)

Lorca 1831.4 Real-time river stage time-series; stage-dischargerelationshipPost-event survey (Benito et al. 2012)

Paretón 2848.1 Real-time river stage time-series; stage-dischargerelationship

Nogalte 124.3 Real-time river stage time-series; stage-dischargerelationship (malfunctioning)Post-event survey (Benito et al. 2012; Hooke 2016)

10 A. Amengual and M. Borga

Fig. 4 Spatial distribution of the 48-h accumulated radar-observed precipitation from 27 to 29September 2012 00 UTC. Rain-gauges used for the bias correction of the hourly radar rainfallestimations are shown as circles. Also displayed stream-gauges as squares

The spatial track of the MCS passage along the southernmost part of the basin isclearly visible from the distribution of the 48-h accumulated radar-derived precipi-tation (Fig. 4). The MCS followed a south-west to north-east direction, affecting thebasin from 09 to 13 UTC on September 28, with an estimated speed ranging from 3.0to 3.5 ms−1. Initially, the MCS crossed transversally La Rambla de Nogalte catch-ment, to latermove almost parallel over the southernmost tributary of theGuadalentínbasin, the Biznaga (Figs. 1 and 4).

Basin Response and Hydrological Modelling

Stream-gauge andpost-event survey data have beenused to examine themain featuresof the basin response to the torrential precipitations. As shown in Table 2, initiallyvery dry soils and acute spatial heterogeneities enhance the nonlinear hydrologicalresponse of the Guadalentín basin to intense precipitations and large accumulatedrainfall amounts. Areas of high infiltration capacities coexist with zones relativelyimpermeable: while the Guadalentín sub-basins feature very low runoff coefficients,Nogalte exhibits an impressive unit peak discharge (Table 2). The former catchmentnoticeably mitigates the magnitude of the runoff discharges. These high infiltrationlosses and large soil moisture storage capacities are linked to the presence of calcare-ous and dolomitic fractured bedrocks, favouring the recharge of the deep aquifers. Onthe other hand, La Rambla de Nogalte basin is mostly formed by highly impermeableterrain (i.e., phyllites, quartzites, micadites and gypsum on schist bedrock).

Note that the contributing catchment and basin areas differ due to the fact thatValdeinfierno and Puentes did not contribute to flow discharge downstream (Tables 1and 2). Accordingly, the computation of the main hydrometeorological features over

Hydrometeorological Analysis of an Extreme Flash-Flood … 11

Table 2 Main hydrometeorological features of the 28 September 2012 flash flood for the differenthydrometric sections of the Guadalentín basin

Basin Contributingbasin area(km2)

Totalrainfall(mm)

Totalrunoff(mm)

Peakdischarge(m3 s−1)

Unit peakdischarge(m3 s−1

km−2)

Runoffratio (−)

Valdeinfierno 430.6 123.8 13.6 NA NA 0.11

Puentes 1011.0 130.7 9.3 NA NA 0.07

Torrealvilla 236.4 136.6 NA 300–450a 1.27–1.90 NA

Lorca 389.8 154.8 18.7 607.6 1.56 0.12

Paretón 1016.7 210.4 28.6 1081.2 1.06 0.14

Nogalte 124.3 211.7 NA 1050a–1500b 8.45–12.07 NA

The total rainfall amounts are expressed as area-averaged values. NA denotes not availableinformation. Data indicated with a denote field estimations by Hooke (2016). Data indicated withb stand for field estimations by Benito et al. (2012)

the Lorca and Paretón basins has just accounted for the contributing areas down-stream of Puentes. Analogously, the derivation of the hydrometeorological featuresin Puentes has not considered the Valdeinfierno contribution. Also note that Hooke(2016) estimated the peak flows close to the outlet in Torrealvilla (Table 2; Fig. 1). InLa Rambla de Nogalte, the peak estimations correspond to the middle of the basin,where overflow embankment was reported (Hooke 2016), and at Puerto Lumbreras(Benito et al. 2012).

The Flood Event–Based Spatially Distributed Rainfall–Runoff Transformation–Water Balance (FEST-WB)model has been implemented to further assess the hydro-logical response of the Guadalentín catchment to the intense precipitations. FEST-WB is a fully-distributed and physically-based hydrological model that accountsfor evapotranspiration, infiltration, surface runoff, subsurface flow and flow routing(Rabuffetti et al. 2008). The model computes soil moisture fluxes by solving thewater balance equation at each grid point. In particular, the evolution of the soilmoisture, θij, for the generic mesh point at (i, j) is given by:

∂θi j

∂t= 1

Zi j

(Pi j − Ri j − Di j − ETi j

)(1)

where P is the precipitation rate, R and D are the runoff and drainage fluxes, ET isthe evapotranspiration rate, and Z is the soil depth. Runoff is calculated accordingto a modified Soil Conservation Service–Curve Number (SCS-CN; USDA 1986)method extended for continuous simulation (Ravazzani et al. 2016). At this aim, themaximum potential retention S is updated at the beginning of a storm as a linearfunction of the degree of saturation. That is,

S = S1(1 − ε) + S3ε (2)

12 A. Amengual and M. Borga

where S1 and S3 are the values of Swhen the soil is dry andwet (i.e., antecedentmois-ture conditions I and III, respectively). The actual evapotranspiration is calculated asa fraction of the potential rate tuned by the beta function that, in turn, depends on soilmoisture content (Montaldo et al. 2003). Potential evapotranspiration is computedaccording to amodified version of the Hargreaves–Samani equation (Ravazzani et al.2012). The surface and subsurface flow routing is based on the Muskingum method(Chow et al. 1988).

Regarding the calibration of the infiltration parameters, the procedure presented byBorga et al. (2007) has been adopted so as to better encompass the strong nonlinear-ities of the runoff generation on the Guadalentín basin. Accordingly, the infiltrationstorativity (S0) and the initial abstraction ratio (λ) have been selected as calibrationparameters. S0 is a site storage index—with a default value of 254 mm—related tothe SCS-CN scheme by Ponce and Hawkins (1996):

S = S0

(100

CN− 1

)(3)

As pointed out by Borga et al. (2007), the infiltration storativity is considered asa calibration parameter so as to properly simulate the observed flood water balancewhen using the CN spatial distribution. The initial abstraction is defined to be propor-tional to the maximum potential soil retention. In the original SCS-CN formulation,the constant of proportionality (λ) is set to 0.2 as the standard value (Ponce andHawkins 1996):

Ia = λS (4)

Calibration of λ copes with the specific lithological features of the Guadalen-tín basin. Model calibration tasks have focused on the different flow informationavailable at each hydrometric section (Table 1). In Valdeinfierno and Puentes dams,the real-time observed stages have been used to calibrate the modelled dischargevolumes. In Lorca and Paretón, calibration has focussed on peak discharges, tim-ings and runoff volumes. In Torrealvilla and Puerto Lumbreras, efforts have centredin reproducing the peak discharge estimations from the available post-flood fieldmeasurements.

Heterogeneities in the hydraulics of the basin response to flash-floodsmust be alsotaken into account. These heterogeneities emerge as result of the gradual decrease ofthe catchment response with increasing precipitation amounts. The main factor regu-lating these heterogeneities is the expansion of the stream network to not previouslychannelled topographic elements during flash-flooding (Borga et al. 2007). Conse-quently, the hillslope and channel velocities have been calibrated by means of theStrickler coefficients. FEST-WB has been driven by the radar-derived precipitationfields from 27 to 29 September 2012 00 UTC. Note that the FEST-WB simulationstarted on 1 August 2012 00 UTC. This warm-up period permits a good initializationof the soil moisture content for the 28 September 2012 flash-flood.

Hydrometeorological Analysis of an Extreme Flash-Flood … 13

Analogously to the procedure presented by Amengual el at. (2007), a multi-site calibration of the FEST-WB parameters has been carried out to account forthe high nonlinearities in the infiltration and rainfall-runoff transformation mecha-nisms (Table 3). The infiltration storativity and initial abstraction ratio have beenaccordingly adjusted to the subjacent substrates in the Guadalentín catchment. Nofurther calibration of the infiltration parameters has been performed in La Rambla deNogalte, although the model parameters associated with the dynamical formulationhave been adjusted in all the sub-basins.

After calibration, FEST-WB succeeds in simulating the highly nonlinear runoffproduction, the Hortonian infiltration excess mechanism, and the fast times to peaksand flood wave celerities in the river channels (Table 4 and Fig. 5). As extensiveoverflowing was reported in the Biznaga, Lorca and Nogalte basins (Benito et al.

Table 3 Calibrated parameters according to FEST-WB

Basin Contributingbasin area(km2)

Totalrainfall(mm)

CN (II) So(mm)

λ Vh(ms−1)

Vc(ms−1)

Valdeinfierno 430.6 123.8 70.1 (11.3) 304.8 0.30 0.28 2.9

Puentes 1011.0 130.7 68.1 (12.1) 508.0 0.40 0.32 3.3

Torrealvilla 236.4 136.6 67.7 (11.3) 558.8 0.35 0.34 4.0

Lorca 389.8 154.8 70.2 (11.0) 508.0 0.30 0.31 2.2

Biznaga 404.8 255.8 72.6 (11.1) 355.6 0.30 0.06 1.1

Paretón 1016.7 210.4 70.8 (11.1) 304.8 0.30 0.24 2.1

Nogalte 124.3 211.7 81.0 (9.8) 254.0 0.20 0.38 5.0

Curve numbers are expressed as area-averaged values, while their standard deviations are shownbetween brackets. Note that curve numbers correspond to normal antecedent conditions

Table 4 Observed and radar-driven simulated flow volumes and peak discharges for the 28September flash-flood at the different hydrometric sections of the Guadalentín basin

Basin Flow volumes Flow peaks

OBS(mm)

FEST(mm)

Rel.Error

OBS (m3

s−1)FEST(m3s−1)

Rel.error

NSE

Valdeinfierno 13.6 13.0 −0.04 NA 595.8 – –

Puentes 9.3 9.4 0.01 NA 1172.0 – –

Torrealvilla NA 11.9 – 300–450a 369.8 – –

Lorca 18.7 18.8 0.01 607.6 646.2 0.06 0.89

Biznaga NA 33.3 – NA 495.6 – –

Paretón 28.6 31.2 0.09 1081.2 1182.6 0.09 0.84

Nogalte NA 124.8 – 1050a–1500b 1544.3 – –

Data indicated with a and b denote field estimations by Hooke (2016) and Benito et al. (2012),respectively

14 A. Amengual and M. Borga

0

2

4

6

8

10

12

14

16

18

200

100

200

300

400

500

600

700

28/09/2012 03:00 28/09/2012 09:00 28/09/2012 15:00 28/09/2012 21:00

precipitation (mm

)Q

(m3 s

- 1)

date

Lorca (389.8 km2)

pcp

Q obs

Q sim

0

2

4

6

8

10

12

14

16

18

200

200

400

600

800

1000

1200

28/09/2012 03:00 28/09/2012 09:00 28/09/2012 15:00 28/09/2012 21:00 29/09/2012 03:00 29/09/2012 09:00

precipitation (mm

)Q

(m3 s

-1)

date

ParetÛn (1016.7 km2)

pcp

Q obs

Q sim

(a)

(b)

Fig. 5 Observed and radar-driven discharges for the 28 Sep 2012 episode at a Lorca and b Paretón

2012; Hooke 2016), the radar-driven runoff experiment has intended to accountfor these water losses by simulating peak discharges higher than observed at theLorca and Paretón hydrometric sections (Table 4). Even after applying this proxy,uncertainties in estimating the actual peak discharges and runoff volumes still remain.The spilled water volumes were not estimated during the different post-flood fieldcampaigns.

The times to peak are satisfactorily reproduced at Paretón, denoting an effec-tive calibration of the model parameters for the dynamical routing (Fig. 5). AtLorca, although these are accurate enough, the experiment fails in simulating the

Hydrometeorological Analysis of an Extreme Flash-Flood … 15

magnitude of the first peak discharge. Note that the relative errors have been com-puted where observations were available in Table 4. Negative errors denote modelunderestimation.

Anthropogenic Impacts on the Basin Response

The effects of the human activities on the hydrological response have been quantifiedbymodelling theGuadalentínRiver basin in its natural regime. That is, this catchmenthas been modelled without reservoirs. Table 5 and Fig. 6 show the intercomparisonbetween the actual and natural basins. In Lorca, increases in runoff volume and peakdischarge would have been of more than 200% of the actual basin simulations. InParetón, increments would have been of 130% in peak discharge and 50% in totalrunoff volume. The total water amount carried by the Guadalentín River during thisepisode would have been of 22.5 and 46.8 Hm3 at Lorca and Paretón, respectively.In addition, the maximum discharges would have been above 2200 and 2700 m3 s−1,respectively.

Considering that the river bed was very close to its maximum water stage level inLorca, the outcome of a river overflowing of this magnitude would have been catas-trophic for the city. Therefore, the reservoirs acted as suitable structural measuresso as to avoid catastrophic flood impacts in Lorca. In addition, the river diversionat Paretón would have been overwhelmed by the peak discharges and flow volumesunder a natural regime, as its maximum evacuation capacity is of approximately1000 m3 s−1. Even with the diversion, a considerable bore would have propagateddownstream towards Murcia City.

On the other hand, La Rambla de Nogalte carried a total water volume of 15.5Hm3, according to the radar-derived rainfall driven runoff simulation. Most of thisamount was poured into the Biznaga basin (Fig. 1 and Table 4). To this incomingrunoff volume, the intense precipitations added additional 13.9 Hm3 over the lattercatchment and the subsequent flood bore destroyed a bridge. The simulated peakdischarge has been of 495.6 m3 s−1 at the junction of the Biznaga stream withthe Guadalentín River. These water volumes converted the Guadalentín valley in

Table 5 Comparison of the peak discharges and flow volumes between the actual and natural basinsaccording to the radar-derived rainfall driven runoff simulation for the 28 September flash-flood

Sub-basin Runoff volumes Peak discharges

Actual(Hm3)

Natural(Hm3)

Rel.Diff.

Actual (m3

s−1)Natural(m3 s−1)

Rel.Diff.

Valdeinfierno 5.6 5.6 0.0 595.8 595.8 0.0

Puentes 9.5 15.1 0.6 1172.0 1708.7 0.5

Lorca 2.8 22.5 2.1 646.2 2280.8 2.5

Paretón 31.7 46.8 0.5 1182.6 2708.3 1.3

16 A. Amengual and M. Borga

0

2

4

6

8

10

12

14

16

18

200

500

1000

1500

2000

2500

28/09/2012 03:00 28/09/2012 09:00 28/09/2012 15:00 28/09/2012 21:00

precipitation (mm

)Q

(m3 s

-1)

date

Lorca (1831.4 km2)

pcpQ obsQ actual basinQ natural basin

0

2

4

6

8

10

12

14

16

18

200

500

1000

1500

2000

2500

3000

28/09/2012 03:00 28/09/2012 09:00 28/09/2012 15:00 28/09/2012 21:00 29/09/2012 03:00 29/09/2012 09:00

precipitation (mm

)Q

(m3 s

-1)

date

ParetÛn (2848.1 km2)

pcpQ obsQ actual basinQ natural basin

(a)

(b)

Fig. 6 Observed (black solid line) and radar-driven discharges for the 28 September 2012 episodeand the actual (grey solid line) and natural (grey dashed line) basins at a Lorca and b Paretón

an extensive flood-plain. Note that the slow recession limbs of the observed andsimulated hydrographs at Paretón indicate that substantial amounts ofwater remainedfor a long time in the Biznaga hillslopes (Fig. 6b).

Hydrometeorological Analysis of an Extreme Flash-Flood … 17

Kinematics of the San Wenceslao Flash-Flood

The kinematic features of this extreme flash-flood have been examined through thespatial moments of the catchment rainfall and the catchment-scale storm velocity.The underlying idea is to relate the rainfall spatial organization to the flow distance,which is a basic descriptor of the structure of the drainage network. Zoccatelli et al.(2011) showed that n-order rainfall spatial moments (pn(t)) can be defined based onthe precipitation at a certain point of the basin and at a specific time (r(x, y, t)) andflow distance through the flow path (d(x, y)) between this point and the catchmentoutlet as:

pn(t) = |A|−1 ∫Ar(x, y, t) · d(x, y)nd A (5)

The zeroth-order spatial moment of the rainfall field, p0(t), corresponds to thebasin-averaged precipitation rate at time t, where A denotes the basin total area.Analogously, n-order moments of the flow distance (gn) can be introduced as:

gn = |A|−1 ∫Ad(x, y)nd A (6)

The first-order moment of the flow distance, g1, is equal to the basin-averagedflow distance. Next, the spatial moments of the catchment rainfall (δn(t)) are definedas the ratio between different orders of the rainfall spatial and flow distancemoments.Specifically, the first two moments are given by:

δ1(t) = 1

g1

[p1(t)

p0(t)

](7)

δ2(t) = 1

g2 − g21

[p2(t)

p0(t)−

(p1(t)

p0(t)

)2]

(8)

δ1(t) describes the flow distance between the centroids of the catchment rainfalland the basin. δ2(t) is related to the dispersions of the rainfall fields—with respectto its mean position—and the flow distances. Values of δ1(t) equal to one mean thatthe rainfall distribution is either concentrated on the basin centroid position or itis uniform over the catchment. Values of δ1(t) less (greater) than one indicate thatrainfall is distributed close to the outlet (headwaters). Values of δ2(t) close to 1 reflecta uniform-like rainfall distribution, while values less (greater) than the unity indicatethat precipitation is characterized by a unimodal (multimodal) distribution along theflow distance.

From Eq. (7), it is possible to derive an expression for the effective storm velocity(veff) as it is filtered by the catchment drainage properties (Zocattelli et al. 2011).Veff is obtained as the temporal derivative of the first-order spatial moment of thecatchment rainfall:

18 A. Amengual and M. Borga

veff = g1d

dtδ1(t) (9)

Also note that pn and δn can be used to describe the organization of the precipitationover a certain time period (i.e., typically the total storm duration, Ts) by defining:

Pn = 1

Ts∫Tspn(t)dt (10)

�1 = 1

g1

[P1P0

](11)

�2 = 1

g2 − g21

[P2P0

−(P1P0

)2]

(12)

�1 and �2 are the first and second time-integrated scaled moments, respectively.These concepts describe the overall rainfall organization at catchment scale, control-ling the shape of the flood hydrograph. That is, �1 has an influence on the runofftiming, while �2 affects the hydrograph shape and the flood peak value. The firsttime-integrated scaled moment represents the ratio between the routing time cor-responding to the rainfall centre of mass with respect to the basin response time.Values of �1 less (larger) than one indicate that rainfall is located towards the basinoutlet (headwaters). The second time integrated scaled moment expresses the ratiobetween the differential variance in runoff timing generated by the rainfall spatialdistribution and the variance of the basin response time. Values of �2 smaller thanunity suggest that the rainfall field is spatially concentrated across the basin. On thecontrary, values of �2 larger than 1 point out that the rainfall field has a bimodalspatial distribution: concentrations are found both at catchment’s headwaters andoutlet.

Finally, a catchment-scale storm velocity (vs) is defined from the first and secondtime-integrated scaled moments as:

vs = g1

[covt[T, δ1(t) · w(t)]

var[T ]− covt[T, w(t)]

var[T ]�1

](13)

whereT is the time, var[ ] is the variance, and covt[ ] denotes the temporal covariance.For a time duration equal to the total storm extent, Ts, var[T] = 1

12T2s (Zoccatelli

et al. 2011). In order to consider the variation over time of the first-order momentand the mean areal rainfall, a rainfall weight (w(t)) must be introduced in Eq. (13).This weight is prescribed as:

w(t) = p0(t)

P0(14)

Briefly, this method quantifies the dependency among the spatial and temporalorganization of precipitation, catchment morphology and basin response in terms of