Universite Pierre et Marie Curie, Ecole des Mines de Paris & EcoleNationale du Genie Rural des Eaux et des Forets

Master 2 Sciences de l’Univers, Environnement, Ecologie

Parcours Hydrologie-Hydrogeologie

Flow evolution in a large Sudano-Saheliancatchment under the constraint of climatic

scenarios for the 21st century

Lila Collet

Supervisor: Denis Ruelland (CNRS)

Co-Supervisor: Sandra Ardoin-Bardin (IRD)

Laboratoire HydroSciences Montpellier300, avenue du Professeur Emile Jeanbrau34000 MontpellierFrance

June 14, 2010

Abstract

Because of severe climatic changes over many decades, environmental and natural resourceshave evolved in West Africa. This study assesses the impact of future climate change on waterflows at the outlet of the Bani watershed (Mali). Many general climate models (GCM) andone regional climate model (RCM) have been used to provide future climate scenarios overthis area. Based on the SRES-A2 scenario, outputs from these climate models were used togenerate daily rainfall and temperature series in the short, mid and long-term: (i) accordingto the Unbias and Delta Methods application and (ii) temporal and spatial downscaling. Asimple temperature-based formula was used to calculate future daily PET. Both rainfall andPET daily series have been introduced into the HydroStrahler model (calibrated and validatedover 1952-2000) to simulate future discharge. Results show that various future trends for waterresources can be expected. Using the WRF RCM does not provide better results than GCMs.The CSIRO GCM is the most optimistic model (the simulated water volume is 10.5 timeshigher than with the others): it thus does not appear to be relevant as the simulated dischargevalues are out of the observed ranges. The ARPEGE GCM implies discharge comparable tothe wet 1950-60s. The MPI-M and HadCM3 GCMs induce in the long-term water resourcesas scarce as in the 1970-80s. This latter trend would tend to reduce water resources if demo-graphic pressure still increases, which would make the local populations more vulnerable.

Key words Climatic projections; Hydrological modelling; Hydro-climatic variability; HydroS-trahler; River Bani

Contents

Foreword 1

Acknowledgements 2

1 Introduction 3

2 Study area 52.1 Physical characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.2 Hydro-climatic analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.3 Anthropogenic pressure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

3 Material and methods 83.1 Hydrological modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3.1.1 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83.1.2 Hydro-climatic data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93.1.3 Model calibration/validation . . . . . . . . . . . . . . . . . . . . . . . . 11

3.2 Climate models and simulations over the reference period . . . . . . . . . . . . 133.2.1 GCMs & RCM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133.2.2 CM temporal evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 143.2.3 CM spatial distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.3 Climatic scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213.3.1 Computing mean rainfall and temperature values over the reference period 213.3.2 Monthly biases computation . . . . . . . . . . . . . . . . . . . . . . . . 233.3.3 Spatial and temporal downscaling . . . . . . . . . . . . . . . . . . . . . 23

4 Results 264.1 Hydrological model fitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264.2 Future climatic trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274.3 Future hydrological trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

5 Discussion 335.1 Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335.2 Criticism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355.3 Comparisons with other studies . . . . . . . . . . . . . . . . . . . . . . . . . . 36

6 Conclusion 38

References 39

List of acronyms 43

List of figures 44

List of tables 44

Appendix 45

Foreword

Foreword

In the context of global warming and the over-exploitation of land, various natural resourcesare being damaged. One of the research themes of the French HydroSciences Montpellier lab-oratory (HSM) is climatic and environmental variability and its impacts on water resources.These issues are brought together in a recent research theme called CACHEMIRE (ClimAt,CHangements Environnementaux et Modelisation de leur Impact sur les Ressources en Eau).In the Mediterranean and tropical regions, the past and present climatic variability have tobe understood in order to simulate the functioning of river systems, their future evolution andthe impact on the water supply. Important socio-economic problems are arising as a result offlooding and decreasing water resources.

To address this issue, the French Agence Nationale de la Recherche (ANR) launched a na-tional research program in 2006 to define the vulnerability of the environment caused by globalchanges and anthropogenic pressure. The HSM has been in charge of the RESSAC project(vulnerabilite des Ressources en Eau Superficielle au Sahel aux evolutions Anthropiques et Cli-matiques a moyen terme) from 2007 to 2010. This study focuses io the Sudano-Sahelian region,an area with limited resources for dealing with climatic changes. As precipitation and runoffhave decreased, the hydrologic systems have evolved while simultaneously under increasing de-mographic pressure around the region’s main cities. The Bani catchment is the main tributaryof the Niger River and is an interesting site for studying the modelling of hydrological systemsin order to examine the past and current situation. The ultimate goal is to simulate climaticand hydrological trends in this watershed between 2011 and 2100.

Concurrently, an internal HSM project underway aims to simulate the climatic and an-thropogenic changes in the Bani watershed. Since 1970 West Africa has been experiencing acontinual state of drought. Decreasing rainfall has led to lower flows in the largest West Africanrivers. With the intensification of agriculture and over-exploitation of forests there has alsobeen significant changes in the surface features of these regions. The HSM project uses theHydroStrahler (HS) conceptual model to simulate the rainfall-runoff relationship in the Baniwatershed (Ruelland et al., 2008a). From a comparative study of the HS and GR4J models(Ruelland et al., 2010a) it appears that HS more accurately simulates floods in this region. Thewater volumes estimation is really accurate too with this model and an aggregation functionproposed by Ruelland et al. (2009) manages to reduce some well-known equifinality problems.

My work falls within the framework of these studies and aims to simulate future trendsin runoff in the Bani catchment and establish the impact climate changes may have on waterresources in the region.

1

Acknowledgements

Acknowledgements

My six-month internship at HydroSciences Montpellier was my second experience in hy-drological research. It was an opportunity for me to apply what I had learnt in my Masters,engineering school and ”prepa” school classes in a practical setting. Without going so far as toname every teacher I have had over the last 6 years, I would like to make some special mentions.

First of all, a huge thank to my supervisors Denis Ruelland and Sandra Ardoin-Bardin.Thank you for letting me work the way I wanted, and for always being there when I neededyou. Thank you Denis for all your advices that allowed me learn and make improvements inmy work. You also made me sure what I wanted to do in the coming years. Thank you Sandrafor your kindness and wonderful listening skills. Both of you are so complementary, these lastfew months have been very rewarding.

I would never have been able to work in the laboratory at all if Pierre Ribstein had notaccepted me into the hydrology and hydrogeology Masters course in Paris. Thank you forgiving me the opportunity to enjoy a scientific field which is now my chosen profession. Inthe Jussieu teacher team, I would like to thank Ludovic Oudin and Vazken Andreassian foranswering all my questions and giving me such helpful advices. At HydroSciences, I wouldalso like to thank Eric Servat for welcoming me in the laboratory. Yves and Kenza have alsospent many hours helping me with my basic Matlab problems, so thank you for your patience.I have not forgotten Pascal Roucou from the CRC of Dijon who managed to provide me datain the format I required.

An internship or any other scientific study is made successful by the people who do admin-istrative jobs each day with a smile. So a big thank you to Nadine Peres for taking care ofmy academic record and helped me when I was desperate; and to Kristine Gujda for the officesupplies and... the sweets.

In the intern’s office, I would first like to thank Cecile and Camille for two reasons. Forhelping me in my work with all those softwares, and of course for the friendly atmosphere. Toall the members of the Bomba Girl Team with Amelie, I will never forget the great times wehad every day, in the cars, at the beach, eating at the IRD, doing some sports or just havingfun after a long (sometimes very long) work day. Marianne thank you for taking me horseriding, it was very relaxing. I have not forgotten my Parisian friends either: Camille and theM2HH mates. Keeping in touch with you was a kind of deep breath which helped me calmdown especially at the end, in the big rush.

Finally, the most personal note. Without getting into the details, because absolutely noone understands my family, I would like to thank all of you: my parents, sisters and brothers(a family tree is available on my desk to follow me when I talk about them). You are veryimportant to me even if we barely manage to see each other. My last few words are for you,Abraham. Just to say, you know, thank you!

2

Introduction

1 Introduction

The climate and water supply in West Africa have undergone various changes since the1970s. Rainfall has decreased by 15 to 30 % in comparison with the 1950-60s period (L’Hoteet al., 2002; Lebel et al., 2003; Andersen et al., 2005). Unfortunately since the 1980s, theclimate did not return to the wet state of the 1950-60s. The significant drought that has ef-fected this region for the last forty years has had a strong impact on the largest West Africanrivers. The latter are principally supplied by the wet Guinean and Sudanian zones. The riverflows have sometimes decreased twice as much as rainfall since the 1970s (Servat et al., 1997).Decreased water resources are a major factor limiting development in this region.

This large area of West Africa is characterized predominantly by agriculture and herdingactivities which have been developing with increasing demographic pressure. This has led toincreasing stresses on the natural resources, because food and energy are in higher demandeach year. The combination of climatic and demographic changes makes living conditions in-creasingly difficult for inhabitants. Moreover, it has sometimes led to environmental damageincluding a decline in soil fertility, decreased vegetation production and the saturation of agri-cultural areas (Ruelland et al., 2010b). In this context, the local population is more vulnerableto the effects of climate change which could lead to more drought periods. As the local popula-tion is entirely dependent on climatic conditions, these changes have to be taken very seriously.

Over the last decades, a global temperature increase has been observed, especially in theNorthern hemisphere (IPCC, 2007). In West Africa, the temperature increased by 0.1 to 0.3oC in the 20th century. Global changes are then an observable reality that can no longer be de-nied. These changes are associated with higher concentration rates of greenhouse gases in theatmosphere. For this reason several research units have focussed their research on modellingglobal circulation. To that purpose, General Climatic Models (GCM) have been developed totransform the information about greenhouse gases and aerosol concentration variations intofluctuations of the atmosphere and ocean conditions and circulation. Various greenhouse gasemission scenarios have been created and take into account factors like demographics, livinghabits and social activities; these are elaborated to build up future greenhouse gas trends thatcan be applied to GCMs. Climate forecasts estimate that the temperature could increase by1.1 to 6.4 oC around the world, depending on GCM and the scenario considered (IPCC, 2007).

As GCM outputs only roughly correspond with observed data, especially for rainfall, theycannot be used directly for impact modelling: adjustments have to be made (Sperna Weilandet al., 2010). Among the various methods to use GCM simulations, the most famous are theDelta Method and the Unbiasing Method (see e.g. Stone et al., 2001; Ardoin-Bardin, 2004;Deque, 2007; Sperna Weiland et al., 2010). The principle of both methods is to add the differ-ence between the mean scenario values generated in the reference and future periods at eachtime step to the observed series. The first method relies on observed data being available forthe location being studied. It also assumes that climate variability is unchanged in the scenarioprojection, i.e. that it follows the same pattern as observed variability. The second methodcorrects the mean error, but assumes the variability of the climatic model is valid, or at leastnot too detrimental to the study (Deque, 2007).

The scenarios generated by the GCM outputs do not always provide enough informationto feed impact models which require accurate series of climate variables. The output gridsare generally coarse and cannot be used directly in an impact model. For this reason, Re-gional Circulation Models (RCM) have become part of the modelling chain. They are designed

3

Introduction

for a limited region and provide high-resolution climate grids. In the West African region,the WRF model has been able to provide future daily climatic series over 2032-2041 (Vigaudet al., 2009). This appears to be an efficient tool for assessing high-resolution climates in thisregion. The results of this climate model can be used in impact models to simulate e.g. fu-ture hydrological trends. This kind of scientific research has a societal aspect, because climatechange directly effects the environment. Especially for water resources, it could disturb futurerunoff and may require a planning response or changes in the way water resources are managed.

In Mali, water resources are scarce and the local population is entirely dependent on naturalresources. For example in the inner Niger Delta region, water flows constrain the social andeconomical activities of the local populations. This makes the impact of climate change on theenvironment (especially on water flows) a major issue. Future evolution of regional climatehas then to be faced in order to forecast impacts it might have on water resource management.This problem can be dealt with by hydrological modelling of upstream rivers. Using a concep-tual model based on the rainfall-runoff relationship, recent studies have accurately simulatedthe Bani River flows over 1950-2000 (Ruelland et al., 2008a; 2009; 2010a). This river is a majorcontributor to the inner Niger Delta flood. Assessing the evolution of the Bani flows in thefuture is a key issue in this area’s water resource management.

This report tempts to insert various climatic scenarios in this hydrological model to sim-ulate their impacts on the Bani flows in the short, mid and long-term. For this, GCM andRCM outputs are processed to generate rainfall, temperature and potential evapotranspiration(PET) over 2011-2099. To that purpose, both the Delta and Unbiasing Method are combinedto correct these monthly outputs. These series are then downscaled to daily scenarios. Af-ter integrating them in the calibrated/validated hydrological model, the future trends of Baniflows are analyzed. This is followed by an examination of the problem of water resources inthis African region in the next century. As this study is based on various climatic models, arange of hydrological responses is discussed based on the results.

4

Study Area

2 Study area

2.1 Physical characteristics

Mali is the second largest country in West Africa after Niger. It is crossed by its two mainrivers: the Senegal and the Niger Rivers. The Bani River is the most important tributary ofthe Niger River. It is principally located in the southern part of Mali from latitudes 9 1’ to14 5’ N and longitudes 3 5’ to 8 5’ W (Fig 1). With a catchment area of around 100, 000 km2

at the Douna gauging station, it is one of the largest rivers in West Africa (approximatively 700km). It flows into the inner Niger Delta at Mopti and is a large contributor to the annual deltaflood. Consequently, every single flow perturbation in the Bani impacts upon the delta annualflood and the socio-economical activities related to the flood. The watershed’s topography haslow slops and elevations ranging from 265 m at the outlet to 702 m.

The vegetation becomes denser at the southern end of the catchment. It divides the wa-tershed into three main parts (see Ruelland et al., 2010b). The northeast of the basin includesmainly cultivated areas and grassland with crops and wooded vegetation in the middle and sa-vannah woodland over the south-western area (Fig 3). Soils are mainly ferralitic and lessivatedwith high sand and clay content. Sandy hillwash is often found at the surface while basal gravelis found in deeper layers of the profile. In the basement, the aquifers are found in two types offissured formation: In the southwest, they have low permeability and a base layer of Birrimianmica-schists and metamorphic rocks (60%). In the northeast, they are made of Infracambriansandstone (40%) so that the downstream area of the basin has higher permeability and mightprovide better support for groundwater flows (Brunet-Moret et al., 1986).

Figure 1: Geographical localization of the Bani catchment at Douna (104 000 km2)

5

Study Area

2.2 Hydro-climatic analysis

Located in a Sudano-Sahelian climatic regime, the Bani watershed is characterized by amonsoon climate with temperatures ranging from south to north from 25 to 30 oC. Thiscatchment has a strong north-south rainfall gradient. Three climatic zones can be identifiedin the Basin: until 800 mm/y the Sahelian zone, between 800 and 1200 mm/y the Sudano-Sahelian zone and more than 1200 mm/y the Sudano-Guinean zone. Since the mid-20th centuryrainfall has varied considerably (see Fig 2). As a result, the flow at the Douna gauging stationfell by 68% between 1952-1970 and 1971-2000 and there was a decrease in deep water rechargeand baseflow contribution to the annual flood (Ruelland et al., 2009). Some of the low-waterperiods were so severe that river flow stopped periodically at Douna during the 1980s (Maheet al., 2000). In addition, flooded areas of the inner Niger delta decreased by 60 % (see e.g.Bamba et al., 1997; Orange et al., 2002; Mariko, 2003). The climatic rupture in the 1970s alsoled to a strong decrease at the other gauging stations (67 % for Dioıla, 61 % for Pankourouand 56 % for Bougouni, see Ruelland et al., 2009). The consequences are worrying, especiallywith the current population explosion in Mali. Consequently, the region’s future water supplyis an important issue and deserves an accurate estimation in the context of climate change.

Figure 2: Hydro-climatic changes on the basin at Douna over the last 50 years: (a) Pluviometricindex over the 1950-2000 period; (b) Rainfall and discharge evolution comparing the 1952-1970and 1971-2000 periods (Ruelland et al., 2008a; 2009)

6

Study Area

2.3 Anthropogenic pressure

Human activity is generally organized around water resources, topography, pedology andthe axes of communication. In this catchment, the population is considered as agro-pastoral.Most of the agricultural areas are located in the northern area of the basin where the hydro-graphic network is highly developed and a main road runs from Bamako (the capital city ofMali) to Mopti (see Fig 1). In this area agriculture is mainly traditional, located around riversand with typical crops like millet, sorghum, cotton, manioc and peanuts. In the south, thevegetation is denser and there are very few cultivated areas (Fig 3). Here, agricultural areasare rapidly expanding due to demographic pressure. Recent remote sensing studies (Ruellandet al., 2008b; 2010b; in press) showed that Sahelian and Sudano-Sahelian areas have been suf-fering from the rapid extension of cropland and pastureland for 40 years now; this is a resultof Mali’s growing population. In fact, the population increased by nearly 2.6 % per year sincethe 1960s (CIA World Factbook, 2010). Deforestation has also been increasing; wood andcharcoal remain the prime energy sources. Other land-cover changes caused by the growingpopulation have been observed. For example soil degradation has sometimes led to increaseerosion (Ruelland et al., in press) and woodland has been replaced by pasture in dry season.

The government has regularly launched dam projects on the Niger River and its tributariesto provide water to the inhabitants. Depending on the area, these dams have a direct impact onthe inner Niger Delta flood. In this region, socio-economic activities alternate according to theseason: wet or dry; which are regulated by floods (Marie et al., 2007). Fishing, agriculture andlivestock farming are the main economic activities in the delta. They are essential, but limitedin Mali. They are also in higher and higher demand because of demographic pressure in thelast few decades. Future climatic conditions are a pressing issue as human and environmentalfactors continue to move in an unsustainable direction.

Figure 3: Land use map of the Bani catchment at Douna in 1986 (Ruelland, 2009)

7

Material and methods

3 Material and methods

3.1 Hydrological modelling

3.1.1 Model

To represent seasonal and inter-annual variations in runoff from the Bani catchment, arainfall-runoff model was used. The purpose was to represent the link between climate forcingsover the basin and discharge at its outlet. Due to data scarcity for this basin, a conceptualapproach was favored and the HydroStrahler (HS) model (Billen et al., 1994; Ruelland et al.,2008a; 2009) was chosen. Using daily rainfall/PET data, this model represents, in a simplifiedmanner, the flow in the basin. It makes possible to simulate runoff at the outlet with a dailytime-step. It can be used with a lumped or semi-distributed mode, depending on the issue ofthe study. HS takes into account two reservoirs in the watershed (Fig 4):

• a shallow reservoir, with short residence time, supplied by rainfall and feeding evapora-tion, surface/subsurface runoff and infiltration

• a deep reservoir, with longer residence time, fed by infiltration and generating baseflow

Figure 4: Principle of the HydroStrahler model (Ruelland et al., 2009)

The model involves four parameters as shown in Fig 4: the saturation parameter (sat)controls immediate runoff; the sub-surface runoff rate (ssrr) determines sub-surface flows; in-filtration toward the deep reservoir is controlled by infr ; and gwrr represents the groundwater

8

Material and methods

runoff rate. These parameters have to be calibrated for each catchment modelled in order toaccount for three sources of runoff: immediate, rapid and delayed.

Ruelland et al. (2009) showed that fixed parameters over time were insufficient to simulaterunoff over non-stationary periods in the catchment. Their research suggested that variableparameters were required. The subsurface runoff (ssrr) and deep infiltration (infr) rates pa-rameters appeared to be particularly sensitive. The evolution of these parameters was con-strained by the spatio-temporal rainfall variability using a smoothed pluviometric index andan empirical adjusting function (see Ruelland et al., 2009):

Paramy = Param0 + Iy ∗ βParam with Iy =

y∑y=y−2

[(Py − Pavg)/σP ] (1)

where Paramy is the annual parameter value (d−1) based on the fixed-time calibrated param-eter Param0 (d−1), Iy is the smoothed pluviometric index for the year y, βParam is the scalingparameter to be calibrated, Py the total annual rainfall for the catchment studied (mm/d),Pavg the average rainfall over 1950– 2000 (mm/d) and σP is the observed standard deviationof rainfall over 1950–2000.

3.1.2 Hydro-climatic data

The daily rainfall and PET series are required for input into the HS model and daily dis-charge series at the basin outlet is needed to calibrate and validate the model.

Rainfall was extracted from 72 rain gauges covering the area (Fig 5). During the 1950-2000 period an average of 65 gauges per day (with a minimum of 39) were used to interpolatedaily rainfall maps using the inverse distance weighted method to a 2.5 km grid. Although thegauge network was sparse in the Bani catchment, this method proved to be optimally accurateout of the classic methods for data reconstruction (Ruelland et al., 2008a). Fig 5 shows therainfall gradient for the three different climates and regions (Sudano-Sahelian, Sudanian andSudano-Guinean) in the Bani catchment.

PET data was calculated, not extracted from measured series. The Climatic Research Unit(CRU) generated monthly 2m high air temperature series using data from various climaticstations around the world, compensating data that was missing from some station using datafrom neighbouring stations that had full data series. These series were then interpolated tocreate 0.5o grids covering the global Earth surface. These grids have been regularly updated.The reference grid for this study was the CRU TS 2.1 version (Mitchell and Jones, 2005). Itwas used to calculate the reference PET forcing. Temperature was the only data available forcalculating the PET and so a simple formula was selected. Oudin et al. (2005) investigatedthe main strategies for estimating PET. A 27-formulae sample was tested using data from 308catchments in a wide range of climatic zones. It was highlighted that very simple PET formulaerelying on extraterrestrial radiation and mean daily temperature was just as efficient as morecomplex formulae such as the Penman’s method. After comparing various mean temperaturebased PET formulae, Eq (2) was proposed.

9

Material and methods

PET =

{R

28.5∗ T+5

100if T+5 >0

0 else(2)

With PET (mm/d), R the extraterrestrial radiation (MJ/m2/d) and T the temperature of theair at 2 m of altitude ( C).

As Oudin et al. (2010) suggested, this formula can be applied with a monthly temperaturetime-step, which was done in this study. However, the extraterrestrial radiation is a dailyvariable which depends on latitude and the Julian day of the year. Thus PET was finallycalculated with a daily time step through the reference period.

Figure 5: Isohyets, rainfall and discharge gauging stations over the Bani catchment

Finally, discharge was extracted from the Douna gauging station series. This station is notat the outlet of the entire basin but it appeared to have a high quality daily discharge series(less than 0.5 % of gap over 1952-2000) and covers more than 90 % of the total Bani catchment(Fig 5).

10

Material and methods

3.1.3 Model calibration/validation

The model was run in the lumped mode. This was chosen in this context for many reasons.First of all, a regional assessment of water resources does not need a particularly detailedknowledge of the internal basin functioning: the most important is modelling accurately thedischarge at the outlet. Furthermore, Ruelland et al. (2010a) have shown that the semi-distributed mode generally yields poorer results than the lumped mode. In the end, the GCMgrids have such a low resolution compared to the HS model that the results are probably notaccurate enough to use with the semi-distributed version of the hydrological model.

A procedure was developed to automatically calibrate the model and calculate runoff (Ruel-land et al., 2008a). The spatial average of daily rainfall and PET was calculated for the entireperiod being studied and for the whole basin at Douna. The model was then run with thesedata as inputs and its parameters were tested within a defined range. These systematic testruns aimed to optimise the statistical criterion between calculated and observed values ofspecific flow over the calibration period. In this study, the following criteria were considered:

• a good agreement between the average simulated and observed catchment runoff volume;

• a good overall agreement of the shape of the hydrograph;

• a good agreement of the peak flows.

In order to obtain a successful calibration using automatic optimization routines, numericalperformance measures that reflect the calibration objectives were formulated. This was doneby considering the calibration problem from a multi-objective perspective (see Madsen, 2000;Ruelland et al., 2009; 2010a). The following numerical performance statistics measured thedifferent calibration objectives stated above:

• Nash, Nash-Sutcliffe coefficient efficiency (Nash and Sutcliffe, 1970):

Nash = 1− [N∑t=1

(Qobs,t −Qsim,t)2/

N∑t=1

(Qobs,t −Qobs)2] (3)

• VE, volume error:

V E = Vobs − Vsim/Vobs = (P∑

y=1

Vobs,y −P∑

y=1

Vsim,y)/P∑

y=1

Vobs,y (4)

• V Eavg, annual average relative volume error:

V Eavg =1

P

P∑y=1

(|Vobs,y − Vsim,y|/Vobs,y) (5)

• PEavg, annual average peak error:

PEavg =1

P

P∑y=1

(|Qpeakobs,y −Q

peaksim,y|/Q

peakobs,y) (6)

11

Material and methods

where N and P are respectively the number of time steps and the number of years in theperiod, Qobs,t and Qsim,t are the observed and simulated discharge at time t, Qobs is the average

observed discharge over the period, Qpeakobs,y and Qpeak

sim,y are the observed and simulated maximumpeak discharge for the year y, Vobs and Vsim are the total volumes of the observed and simulatedhydrographs over the period, Vobs,y and Vsim,y are the volumes of the observed and simulatedhydrographs for the year y.

This multi-objective calibration problem was transformed into a single-objective optimiza-tion problem by defining a scalar objective function Fagg that aggregates the various objectivefunctions (Ruelland et al., 2009):

Fagg = (1−Nash) + V E + V Eavg + PEavg (7)

Model calibration was then performed in a 4-D parameter space by searching for the minimumvalue of Fagg. The model’s calibration/validation was done with a 10-day lumped modellingstrategy. This time step was chosen because it integrates the water time of transfer from a sub-basin to another in the semi-distributed mode. Spatially distributed forcings were aggregatedacross the entire basin so they could be used in the lumped mode. The optimal parameter setwas estimated by calibrating the lumped model to simulate streamflow at the basin outlet.

The first two years of simulations (both in calibration and validation) were used as a modelwarm-up, to eliminate the influence of initial conditions in the model reservoirs. The 1952-2000 simulation period was divided into three periods. Calibration was performed for a 30-yearperiod (1961-1990) and validation was carried out in two 10-year periods. This was done forseveral reasons. First, the 1961-1990 period contains a wide range of climatic conditions: inthe 1960s climate was wet, the 1970s were characterized by a decline in rainfall which was ex-acerbated in the 1980s (see Fig 2). Secondly, the two validation periods are distinguished bycontrasted climatic behaviour: the period 1952-1960 was wet and the 1991-2000 period quitedry, which allowed the model to be tested for its suitability in differing climatic situations.Thirdly, the IPCC (2007) recommends working with periods long enough to represent climatevariability (i.e. at least 30 years), and the 1961-1990 period is a predominant reference periodin the scientific literature. Validation consisted of running the model with the optimized pa-rameters of the calibration phase.

To obtain the best calibration, the variable parameter version of the model was used. Thescaling parameters β for ssrr and infr were calibrated for the same 1961–1990 period thatwas used for the fixed parameter version. These adjustments made it possible to take rainfallvariability and its consequences on basin behaviour into account, particularly when there is asuccession of wet and dry years (see Ruelland et al., 2009).

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Material and methods

3.2 Climate models and simulations over the reference period

3.2.1 GCMs & RCM

GCM data grids are available on the Intergovernmental Panel on Climate Change (IPCC)Data Distribution Center (http://ipcc-ddc.cru.uea.ac.uk). For this study, three models wereselected from an extensive range of coupled ocean-atmosphere global models: CSIRO (Com-monwealth Scientific and Industrial Research Organization, Australia), MPI-M (Max-Planck-Institute for Meteorology, Germany), UKMO-HadCM3 (Meteorological Office, UK); see IPCC(2007). This choice was based on historical, practical and scientific reasons. The HSM labo-ratory has been working with these GCMs for a decade now (see e.g. Ardoin-Bardin, 2004;Ardoin-Bardin et al., 2009) and has acquired an extensive experience dealing with these GCMsin West Africa.

Data grids from ARPEGE (MeteoFrance, France) have been also used in this study (seeTab 1 for GCM description). With the RESSAC program, the Climatic Research Center(CRC, University of Bourgogne) has used a regional climatic model (RCM) constrained byARPEGE outputs. In addition, Vigaud et al. (2009) developed this model specifically in WestAfrica. The authors used the Weather Regional Forecast (WRF) model to downscale low-resolution data from ARPEGE in West Africa. This RCM appeared to be an efficient tool forsimulating high-resolution climates in West Africa. It was used in this study to simulate theshort-term climate and compare results with 10-year short-term simulations from ARPEGE-10(same outputs as ARPEGE in a shorter period). This links the RCM and GCMs results.

The various GCMs used in this study provide monthly time series, while WRF providesdaily time series. The CMs have different spatial resolutions (Tab 1): CSIRO and MPI-M wererecently updated to the same atmospheric resolution while HadCM3 and ARPEGE continue touse a larger grid. This is a problem that have to be considered for consistency when comparingand processing CM data.

Model Origin Atmospheric resolution ReferenceCSIRO Australia 1.9o x 1.9o Hirst et al., 1996, 2000MPI-M Germany 1.9o x 1.9o Roeckner et al., 1992HadCM3 U.K. 2.5o x 3.75o Johns et al., 2003ARPEGE France 2.5o x 2.5o Deque et al., 1994WRF France 0.5o x 0.5o Vigaud et al., 2009

Table 1: Description of the studied GCMs and RCM

Generally, GCM simulations are constrained by the IPCC SRES (Special Report for Emis-sion Scenarios) greenhouse gas emission scenario (Nakicenovic et al., 2000). Climatic propertieslike temperature, cloud cover, rainfall, and so on are calculated and interpolated all over theworld. In order to use a similar time-step series for all the GCMs, the monthly time-step waschosen for rainfall and mean temperature. Fig 6 presents the simulation periods of each CM,with the hydrological model calibration/validation period. The GCM and RCM results arepresented for various scenarios in the past and future. In this study, the reference period is1961 to 1990 for GCMs and 1981 to 1990 for WRF due to limited time availability reasons. CMsimulations in the reference period are based on the 20C3M SRES scenario where greenhousegases increase at the same rate as the 20th century (IPCC, 2007). The projected CM simula-tions are based on the A2 SRES scenario (IPCC, 2007). The hypothesis of this scenario are the

13

Material and methods

following: a very heterogeneous world with steady population growth, reaching 15 thousandmillion people by the end of the 21st century. Others themes include strengthening regionalcultural identities, an emphasis on family values and local traditions, and less concern for rapideconomic development. There are no concerted measures to mitigate the increasing concen-tration rates of greenhouse gases in the atmosphere. This would cause a global warming of2.7-4.8 oC, depending on the GCM (IPCC, 2007). Three future time periods were considered:2011-2040, 2041-2070 and 2071-2099 (respectively a short, mid and long-term period) (see Fig6). For RCMs, the future simulations occur between 2032 and 2041 which places them in theshort-term.

Figure 6: CM monthly temperature and rainfall study periods over 1950-2099

To assess wether model simulations are suitable for use with HS, a forewarning analysis oftheir spatial and temporal quality was performed.

3.2.2 CM temporal evaluation

First, the seasonal variability of each climatic model was tested. The monthly average forrainfall and temperature was calculated for each CM cell over the reference period. Then, theseries were spatially aggregated across the basin so that the series could be compared withobserved values in a thinner grid. First, each HS cell included in a CM cell was given the CMoutput value. As HS cells do not have the same surface area, the aggregation was done bycalculating the cell surface weighted average:

Meanmonth =

∑Nj=1Meanj

month ∗ s(j)S

with Meanjmonth =

∑Lyi=FyMonthji

Ny(8)

where Meanmonth is the spatially aggregated mean value for the chosen month over the basin,Meanj

month is the mean value over the reference period for the chosen month in cell j, s(j) isthe surface of cell (km2) j, S is the total surface of the catchment (km2), Monthji is the valuefor chosen month of year i in cell j, Fy is the first year of the reference period, Ly is the lastyear of the reference period and Ny is the number of years in the reference period.

When compared with observed monthly rainfall and temperature, the CMs represent sea-sonal variability fairly accurately (Figs 7 and 8). The monsoon season is correctly reproducedbut the rainfall peak does not always match observed data (see Fig 7a). Some GCMs, likeCSIRO and HadCM3, start the monsoon season well, while MPI-M and ARPEGE start the

14

Material and methods

monsoon season late. GCM rainfall peak values are always lower than the observed values.HadCM3 and MPI-M reproduce the rainfall peak on time, while CSIRO simulates it a monthearly and ARPEGE a month late. HadCM3 reproduces the end of the rainfall season accu-rately, ARPEGE and MPI-M simulate it quite late and CSIRO has it beginning a month early.Finally, HadCM3 simulates rainfall seasonality the most accurately and ARPEGE and CSIROprove unsatisfactory with values falling well under those of observations. WRF values followthe same pattern as ARPEGE-10 with values that are consistently too low (even lower thanARPEGE-10). They have the monsoon beginning at least two months late and the seasonalpeak arriving one month late (Fig 8a). However, this model simulates the end of the rainfallseason more accurately than ARPEGE.

The models are more accurate for simulating temperature seasonality. All of the GCMsreproduce the two annual peaks except for ARPEGE (see Fig 7b). On closer examination,CSIRO is slightly behind the observed temperature curve and MPI-M is slightly in advance.HadCM3 closely matches the observed temperature curve and ARPEGE shows a tendencyto peak after the observed curve and then decrease after it. WRF follows the same pattern,but with higher values (Fig 8b). Here again, HadCM3 simulations seem accurate while theARPEGE and WRF series do not reproduce temperature variability.

Figure 7: Mean seasonal dynamics (a) in rainfall and (b) in temperature over 1961-1990

15

Material and methods

Figure 8: Mean seasonal dynamics (a) in rainfall and (b) in temperature over 1981-1990

Next, the inter-annual variability of rainfall and temperature was evaluated. To do this, apluviometric index (see Eq 9) and mean annual temperature were used for each year in thereference periods. Both the pluviometric index and mean annual temperature were calculatedusing annual rainfall and temperature values based on the spatial average over the basin anda surface weighting, as mentioned previously.

Ii =Pi − Pavg

σ(9)

where Ii is the pluviometric index in year i, Pi is the annual rainfall on year i (mm/y), Pavg

is the annual mean rainfall over the reference period (mm/y) and σ is the standard deviationover the reference period.

The results are surprising (see Figs 9 and 10). None of the CMs accurately simulatesannual variability of the climate parameters. In the wettest years, only HadCM3 and CSIROpresent a positive pluviometric index three times while the others remain below zero (Fig 9a).Then, when the drought is most severe in the 1980s, the GCM simulations remain mainly onthe positive side of the pluviometric index. In addition, there is no clear tendency by the GCMsto reproduce wet and dry years in the 1970s. As a general rule, none of the RCM simulationsare correct for the 1981-1990 period (Fig 10a).

For the GCM mean annual temperature series, there are no clear pattern which can beidentified (Fig 9b). However, ARPEGE values appear as the lowest and HadCM3 turns out tobe the most accurate model. WRF follows the same pattern as ARPEGE-10 but with highertemperature values (Fig 10b). Both models generally follow the inter-annual variability trendover 1981-1990 with the exception of 1983 in which the series decrease rather than increasingas the observed values do.

16

Material and methods

These last observations give two reasons for not using CM outputs directly in hydrologicalmodelling. They show that CMs simulate rainfall and temperature seasonal dynamics fairlyaccurately in general, but that simulated data does not follow observed inter-annual trends.Next, spatial distribution was also analyzed to compare CM simulations to observed reality onaverage in the reference periods.

Figure 9: Observed and simulated inter-annual variability over 1961-1990 on the catchment:(a) pluviometric index; (b) mean annual temperature

17

Material and methods

Figure 10: Observed and simulated inter-annual variability over 1981-1990 on the catchment:(a) pluviometric index; (b) mean annual temperature

3.2.3 CM spatial distribution

As stated in the previous sections, the Bani catchment shows a very strong and character-istic rainfall gradient. However, the temperature gradient is less distinctive. Seasonal thermalvariation is generally low. In order to keep this study concise, only the spatial distribution ofrainfall was studied.

To do this, CM rainfall simulations were mapped over the reference periods. Annual rainfallwas calculated and then averaged for the reference periods (1961-1990 or 1981-1990) and foreach CM cell. To analyze the grids and compare them with observed data at the 0.5o gridscale, the same calculation as for observed rainfall was performed. CM grids were downscaledto fit the 0.5o grid using an inverse distance weighted method, as used previously to constructthe rainfall reference series. Fig 11 shows the results.

In the 1961-1990 period, rainfall observations range from 700 to 1500 mm/y (Fig 11a) andthe rainfall gradient is oriented northeast-southwest. This orientation is not reproduced by theGCM outputs. In terms of rainfall value, the simulations often fail to reproduce the range ofobserved rainfall. The ARPEGE grid clearly remains below observed levels, ranging from 400to 700 mm/y. Thus, it hardly reaches the lowest of the observed values. CSIRO values are toolow, but not quite as low as ARPEGE, ranging from 500 to 1000 mm/y. MPI-M and HadCM3simulations are within the observation range, from 900 to 1300 mm/y but are still too highin the Sudano-Sahelian area of the catchment. In addition, MPI-M, HadCM3 and ARPEGEhave a north-south gradient while CSIRO gradient orientation is northwest-southeast.

18

Material and methods

In the 1981-1990 period, rainfall observations range from 600 to 1300 mm/y, with thesame rainfall gradient orientation (Fig 11b) as the longer reference period. The same patternsemerge in this period: the range of both the CM values is below that of observations. TheARPEGE-10 series remains between 400 and 800 mm/y while the WRF series are the lowest,ranging from 200 to 700 mm/y. This gives the catchment a Sahelian climate which does notcorrespond with observed reality. Once again, ARPEGE-10 reproduces a north-south gradi-ent. The WRF grid does not follow the same pattern and the gradient has northwest-southeastorientation.

Consequently, none of the CM grids reproduces the wide range of observed rainfall values oraccurately reproduce the observed gradient. The difference between the lowest values and thehighest values is 800 mm/y for 1961-1990 and 700 mm/y for 1981-1990. The CSIRO and WRFgrids reproduce the best range with 500 mm/y. This spatial analysis reveals yet another limitof CM simulations. It is clear that simulated data cannot be used directly and that furtheranalysis is required before hydrological modelling in the Bani catchment. These findings arebacked up by other studies (see e.g. Koutsoyiannis et al., 2008; D’ Orgeval, 2008) and theproblem has been pointed out by the climatic research community.

19

Material and methods

Figure 11: Comparison of CM output spatial distribution with observations over 1961-1990and 1981-1990

20

Material and methods

3.3 Climatic scenarios

Further calculations were required to feed the hydrological model. Assuming that CM er-rors are stationary, constructing climatic scenarios based on CM simulations made it possibleto take climate variability changes and mean climate change into account. Projected climaticscenarios were elaborated in three steps (see Fig 12). The first step was to calculate the av-erage of the simulated and observed series over the reference periods. The second step was todetermine the monthly adjustments that will allow to construct projected scenarios. The thirdstep was to construct climatic scenarios for each hydrological model cell on a daily time-step.

3.3.1 Computing mean rainfall and temperature values over the reference period

In the present study, CM patterns tend to be promoted. Scenarios were constructed basedon inter-annual mean values of the rainfall and temperature series in the simulated series andobserved series. The first step was to create series for both simulated and observed data for thereference periods (Fig 12a). For each CM cell, a mean monthly hyetogram and thermogramwere calculated based on the 20C3M scenario over the reference periods (1961-1990 for GCMsand 1981-1990 for RCM). Each CM month value was averaged to produce a mean monthlyhyetogram and thermogram containing 12 values for the period in question: an average of allof the values in January, average of all of the values in February ... an average of all of thevalues in December (same method as second term of Eq 8). These values are noted PCM

20C3M,m

and TCM20C3M,m in the following sections. For observations, this method was applied to daily

values on each HS cell over the reference periods. The resulting hyetogram and thermogramcontain 365 values (one for each day of the year). These are PHS

obs,d and THSobs,d in calculations.

The monthly hyetogram and thermogram calculated for CMs were then used as input datain step 2. They represent averaged values of the simulated data in the reference periods. Simi-larly, the observed daily hyetogram and thermogram represent a base from which to constructscenarios and are the averages of observed data in the reference periods.

21

Material and methods

Figure 12: Construction of future climatic scenarios based on GCM/RCM outputs and obser-vations

22

Material and methods

3.3.2 Monthly biases computation

The second step was to calculate adjustments to use with daily observed hyetogram andthermogram in step 3 (Fig 12b).

For rainfall, an Unbiasing Method was used to calculate the correction for constructingfuture rainfall scenarios:

∆PCMm,y =

PCMA2,m,y − PCM

20C3M,m

PCM20C3M,m

(10)

where, for each CM cell, ∆PCMm,y is the bias value in month m of year y, PCM

A2,m,y is the monthly

rainfall in A2 scenario in month m of year y (mm/month) and PCM20C3M,m is the value of the

mean monthly hyetogram for month m (mm/month).

For temperature, the Delta Method was used to calculate the adjustment required to con-struct future temperature scenarios:

∆TCMm,y = TCM

A2,m,y − TCM20C3M,m (11)

where, for each CM cell, ∆TCMm,y is the delta value in month m of year y (oC), TCM

A2,m,y is the

monthly temperature in A2 scenario in month m of year y (oC) and TCM20C3M,m is the value of

the mean monthly thermogram for month m (oC).

Using these methods, the required adjustments were calculated based on average valuesof the 20C3M scenario by PCM

20C3M,m and TCM20C3M,m and each monthly value of the A2 scenario

(2011-2099 for GCMs and 2032-2041 for RCM). This meant that CM simulation patterns re-main in the future and observations were adjusted only with these corrections. As a result,the corrected CM series for rainfall and temperature have a monthly time-step in the futureand a low-resolution grid. These latter points were adjusted in step 3 to the hydrological cellgrid and at a daily time-step.

3.3.3 Spatial and temporal downscaling

The third step to construct scenarios was the spatial and temporal downscaling of CM celladjustments (Fig 12c).

For spatial downscaling, note that the hydrological model grid resolution was based onthe intersection between a 0.5o grid and the sub-basin limits (see Fig 13). Consequently, theresolution of the hydrological model was much higher than the GCM grids. To deal with thedifferent spatial resolutions, a simple method consisted of superimposing the coarse GCM gridson the finer HS grid. Then adjustments were applied on HS thinner cells.

For the temporal downscaling, the high-resolution time series were generated applying thesame adjustment to all of the HS cells contained in each CM cell to observed series, thus givingto these series the HS resolution. The adjustments calculated previously (∆PCM

m,y and ∆TCMm,y )

were then applied to the daily mean hyetogram and thermogram calculated with the observedseries over the reference periods (1961-1990 or 1981-1990; see Eqs 12 and 13). This last step

23

Material and methods

represented temporal downscaling.

PHSfuture,d = (∆PCM

m,y + 1) ∗ PHSobs,d (12)

where PHSfuture,d is the daily future (2011-2099 or 2032-2041) rainfall value on a HS cell (mm/d)

and PHSobs,d is the mean daily annual hyetogram value in day d of month m of year y (mm/d).

THSfuture,d = ∆TCM

m,y + THSobs,d (13)

where THSfuture,d is the daily future (2011-2099 or 2032-2041) temperature value on a HS cell

(oC) and THSobs,d is the mean daily annual thermogram value in day d of month m of year y (oC).

The series generated represented simulated daily rainfall and temperature over the futureperiod. Note that the reference temperature series had monthly values and that the simulatedseries had also a monthly time-step. This was not a problem in this case because the HS modelinput was in fact daily PET. This variable was calculated using a monthly future temperatureseries and Eq 2 which led to daily PET.

The future daily rainfall and PET series were used to feed HS. The hydrological model wasthen run with the calibrated parameters to simulate future discharge based on those input datain the future simulation periods. This 3-step method made it possible to construct climaticscenarios over future periods using a high-resolution grid and a daily time-step.

24

Material and methods

Figure 13: Comparison of HS and GCM grids on the Bani watershed (with CSIRO as anexample here)

25

Results

4 Results

4.1 Hydrological model fitting

The first results concern calibration/validation of the hydrological model. This is a cru-cial part because the final results are based on this model. If the hydrological model doesnot accurately match observed data, it will create significant errors that will bias hydrologicalsimulations.

The optimal parameters for the hydrological model are shown in Appendix 2, Tab 4. Thecorresponding results are shown in Tab 2. They demonstrate that HS has a very high levelof efficiency: Nash values over 89 % both in the calibration and validation periods and Fagg

close to null. Taking a closer look at the results, the validation period 1952–1960 providesbetter results than the calibration period, with the exception of VE. All of the parametersare quite well constrained. Note that V Eavg and PEavg do not exceed 0.2. This means thatinter-annual variability of water volume and the discharge peak simulated are fairly reproduced.

Period Whole period Validation Calibration Validation1952–2000 1952–1960 1961–1990 1991–2000

Nash 93.5 % 94.5 % 92.4 % 89.5 %VE 0.02 0.04 0.00 0.08V Eavg 0.13 0.09 0.14 0.16PEavg 0.16 0.10 0.17 0.18Fagg 0.38 0.28 0.38 0.53

Table 2: Goodness of fit scores of the hydrological model over calibration and validation periods

Fig 14 clearly shows that simulated discharge corresponds precisely with observed values.However, note that simulated discharge tends to end the hydrological season abruptly. Overall,the simulations correspond with the range of the observations (except in year 1999 where thereis no observed data). In addition, the main issue of this study is variation in water availabil-ity. It can be noted that simulated cumulated discharge matches observed cumulated volumeparticularly well. This is an important result because it shows that HS is capable of providingsimulations that are useful for water resource management issues.

26

Results

Figure 14: Comparison of observed vs. simulated hydrographs at the Douna gauging stationover the period 1952-2000

4.2 Future climatic trends

The second results are the climatic scenarios obtained using the unbias and delta methodson rainfall and temperature respectively. These results are determining for the last issue ofthe study as climatic scenarios constrain the hydrological model. Tab 3 presents the averagedbias and delta values for each time period. The monthly series are presented in Appendix 1,Figs 23 and 24. These values were used with the observed series to generate climate trendsover 2011-2099 and 2032-2041.

For rainfall, there are three different trends. The first is fluctuating rainfall patterns overtime: the bias of CSIRO and MPI-M tends to decrease in the short to mid-term and then in-crease in the long-term. The second trend is the opposite: the bias of HadCM3 first increasesin the mid-term and decreases to long-term. The third trend is to remain a trend constantover time: the ARPEGE bias increases right up into the long-term. As far as these valuesare concerned, the CSIRO bias is substantial in comparison to the others (+555 %) whichmeans that this model generates a higher proportion of rainfall than the others. MPI-M ratesare between CSIRO and the other models (around +33 %). ARPEGE and HadCM3 presentnegative values that tend to generate lower rainfall than observed reality in the long-term forHadCM3 (-4 %), and also in the short and mid-term for ARPEGE (both -5 %). For WRF,the bias values (+104 %) are between that of CSIRO and the other GCMs. Over a 10-yearperiod, ARPEGE-10 bias is higher than a 30-year short-term period and tends to simulatehigher rather than lower levels of rainfall (+32 %). These biases are averaged values of eachterm period. It means that for the 2032-2041 period, ARPEGE-10 simulates higher rainfallthan the average value of the entire short-term and that in other decades this GCM generatesmuch lower rainfall.

27

Results

For temperature, all of the GCMs generate increasing trends. MPI-M and ARPEGE simu-late the highest values while CSIRO generates the lowest temperatures. Using the delta methodand depending on the model, temperature is estimated to rise by 1 to 1.5 oC in the short-term,2 to 2.7 oC in the mid-term and 3.6 to 5.4 oC in the long-term. Once again, ARPEGE-10values are higher than in the 30-year short-term period: reaching 1.6 oC instead of 1.4 oC. TheWRF delta remains in the range of GCM short-term values with a temperature increase of 1.3oC.

Bias (%) Delta (oC)Short-term Mid-term Long-term Short-term Mid-term Long-term2011-2040 2041-2070 2071-2099 2011-2040 2041-2070 2071-2099

CSIRO 482 431 555 1.2 2.0 3.7MPI-M 31 5 29 1.1 2.6 5.4HadCM3 2 6 -4 1.2 2.7 4.8ARPEGE -5 -5 10 1.4 2.5 5.2

2032-2041 – – 2032-2041 – –ARPEGE-10 32 – – 1.6 – –WRF 104 – – 1.3 – –

Table 3: Bias and delta averaged over the catchment in the short, mid and long-term

These values can be seen in Figs 15, 16, 17 and 18. The most noticeable trends are seenin CSIRO; they are twice as high as the other GCMs (Fig 15). They also seem to increasestrongly in the short-term and then reduce over time to arrive at values comparable with thoseof the long-term observed data. The other CM trends do not differ from observations as much.When comparing rainfall peaks, a slight decreasing-with-time trend can be noted for MPI-Mand also for HadCM3 in the long-term; however ARPEGE seems to alternate trends and even-tually increases in the long-term. Fig 16 illustrates the high level that ARPEGE-10 simulatedfor rainfall in the 2032-2041 period. This must be due to an anomaly in the simulated climaticseries. The detailed series in Appendix 1, Fig 24a show that the January 2041 rainfall bias risesso high (121) that it is no longer contained in the graph (so as not to hide other monthly values).

In Figs 17 and 18, PET scenarios are plotted. They are based on a simple temperature-based formula (Eq 2) and PET follows the same pattern as temperature. The increasing-with-time pattern is clearly demonstrated by all of the CMs. It can be noted that there arehigher values for ARPEGE-10 and WRF than other CMs in this period. The difference seenin ARPEGE-10, including the longer short-period can be explicated as above, and there arehigher values in the 2030 decade than there are in the two previous decades.

Finally, the hydrological model HS is constrained by changing patterns of rainfall depend-ing on the CM and with PET increasing over time. As rainfall and PET work in oppositionfor the discharge simulation, the HS results cannot be predicted intuitively.

28

Results

Figure 15: Simulated rainfall based on GCM simulations at short, mid and long term

Figure 16: Simulated rainfall based on ARPEGE-10 and WRF simulations at short-term

29

Results

Figure 17: Simulated PET based on GCM simulations at short, mid and long term

Figure 18: Simulated PET based on ARPEGE-10 and WRF simulations at short-term

30

Results

4.3 Future hydrological trends

Finally, the most important results of this study concern hydrological trends. For the rea-sons mentioned earlier, Figs 19 and 20 show simulated discharge in the future for MPI-M,HadCM3, ARPEGE, ARPEGE-10 and WRF. CSIRO simulations are not shown because theirvalues are much higher than the other CMs, so high that the graphs cannot accurately repre-sent the results. CSIRO simulations can still be seen in Appendix 3, Fig 25.

Figure 19: Simulated discharge according to GCM simulations at short, mid and long-term

As seen in Fig 19, the GCMs all produce simulations that decrease over time, but notalways in the same way. In MPI-M discharge appears to gradually decline over time (twiceas high as observed discharge in the short-term and seven times as high in the long-term),simulations by HadCM3 and ARPEGE decrease in the short-term and then discharge risesto reach a level comparable with observed values in the mid-term. Discharge simulated byHadCM3 reduces sharply in the long-term while discharge is steady in the short and mid-termwith ARPEGE. Overall, the flood season remains at the same time of the year. The discharge

31

Results

peak occurs at the same time as observations, sometimes a month later for HadCM3 or earlierfor ARPEGE. For the highest simulated discharges, the flood season does not change, but forthe lowest simulated discharge its time-period decreases from 7 to 3 months using HadCM3 inthe long-term; this is the most extreme case.

Figure 20: Simulated discharge according to ARPEGE-10 and WRF simulations at short-term

Discharge simulation results for ARPEGE-10 and WRF are presented in Fig 20. Due to theJanuary 2041 rainfall anomaly in ARPEGE-10, future discharge for this CM is much greaterthan past observations. As for WRF, HS simulates lower discharge with a peak at 40 m3/scompared to 115 m3/s for observed data. The flood season is slightly longer (a few weeks) andthe discharge peak is reached some days after the observed peak.

This section shows that the dynamics of simulated discharge does not change. Flood peakstend to occur at the same time as observed peaks. Only the discharge values reduce withtime as a general rule. This can be linked to PET constantly increasing in the future for allCMs and shows no clear trend for simulated rainfall. These lower values tend to decrease theseasonal flood period. This will probably impact the discharge at the outlet of the catchmentand should generate a significant decline in the annual flood of the inner Niger Delta.

32

Discussion

5 Discussion

5.1 Synthesis

This study aimed to assess the possible evolution of water resources in the Bani catchment.By comparing various CM outputs using rainfall and temperature observations from past peri-ods, it was deduced that future climate scenarios had to be constructed and not used directlyfrom CM simulations. These scenarios were then used to feed an efficient hydrological modelfor the area and simulate future discharge at the basin outlet.

The results indicate that the HydroStrahler model simulates discharge efficiently enough tobe considered as an appropriate hydrological model for the area, and does not induce substan-tial errors that need to be taken into account. Rainfall CM trends are different. CSIRO seemsto simulate much higher trends while the others stay in a similar range. PET simulationsare more uniform with an increasing pattern for the 21st century. Future simulated dischargereflects both rainfall and PET trends. Hydrological simulations with CSIRO are very opti-mistic and create a sharp increase in discharge in the long-term. On the contrary, simulationsof climatic scenarios based on the other CMs show significant decreases in discharge in thelong-term with intermediate variations in the short and mid-term depending on the scenarios.

The hydrological trends are summarized in Figs 21 and 22. These plots present the evolu-tion of simulated cumulated volume over the hydrological model simulation period and in theshort, mid and long-term. Once again CSIRO results are not presented because of its excessivepatterns. CSIRO trends are plotted in Appendix 4, Fig 26.

Figure 21: Cumulated simulated volume based on MPI-M, HadCM3 and ARPEGE simulationsover 1952-2099

33

Discussion

Figure 22: Cumulated simulated volume based on WRF and ARPEGE simulations over 1952-2041

Water cumulated volume trends illustrate discharge evolutions. Fig 21 shows that watervolume trends range in proportion from 1 to 2.5 in the long-term. The lower values matchthe MPI-M steadily decreasing simulated discharge over time. It can be noted that overall,HadCM3 and MPI-M gradients are lower than observations in the wet period and are similar tothe dry observed years. This is quite pessimistic and shows a trend away from the wet periodobserved in the 1960s. With increasing demographic pressure, it lets think that the availabilityof water resources will be a key issue for the survival of the population in this region. In themeantime, ARPEGE shows discharge simulations at the same level as the observed data andsimulates the highest cumulated water volume in the long-term. This GCM is more optimisticbecause it follows a similar pattern to the wet years before the decline in rainfall in the 1970s.This shows that in this region, inhabitants might enjoy their former living conditions.

Fig 22 shows the evolution of simulated cumulated volume of WRF and ARPEGE-10.WRF trend increases more rapidly than ARPEGE-10. This can be compared to the results ofTab 3. WRF rainfall simulations are greater than ARPEGE-10. This trend combined withthe lower temperature seen in WRF explains a lower simulated PET for the RCM, so thatthere is less water evaporation and finally more water volume. This means that ARPEGE-10simulation in Fig 20 is biased by the high value witnessed in 2041. This also explains whyCSIRO discharge and cumulated volume simulations reach such high values. For this GCM,simulated rainfall is much greater than the others but simulated temperature values are thelowest in the mid and long-term. This analysis is true for all of the GCMs and highlights theimportance of PET’s influence in hydrological modelling.

34

Discussion

When comparing the water availability simulated by various CMs in this watershed, it variesfrom one CM to another. Ranging from quite pessimistic forecasts in which water resourcesare as scarce as in dry observed period, and more optimistic forecasts in which the climatereturns to the former wetness of the 1960s, or even exceeding this.

5.2 Criticism

This study have some limitations that need to be pointed out. First, using CM outputs iscontestable. All GCMs are constructed and constrained differently so they assess trends thatvary depending on the climate model. Making forecasts based on such analysis do not provide aspecific pattern but a range of values. The conclusions are then inaccurate. In addition, GCMdata series are climatic model runs on coarse grids over the planet. Predictions usually coverlong periods (50 to 100 years and more) and then are used in impact studies such as freshwaterresource issues. GCM outputs were used here on the assumption that climate is predictable inthe long-term and that GCMs provide credible predictions for this kind of horizon. But thoseassumptions were not tested in this study. Koutsoyiannis et al. (2008) raised this issue in aresearch paper and concluded that GCM predictions were not reliable. They suggested usinga stochastic method to model the relationship between temperature and the concentration ofgreenhouse gases. This kind of work was not done for the area studied here. Nevertheless,until better simulations are available, the simulations proposed by the IPCC (2007) are thebest series to be used for a hydrological studies; they are considered as acceptable data by theclimatology community.

Second, using correction methods with GCM projections for greater realism and usabil-ity is debatable. This is done working on the hypothesis that although CM representationsof reality are not perfectly accurate, the evolution of the climatic variables in various futurehorizons is realistic intrinsically, according to the SRES scenario studied. Moreover, delta andunbiasing methods were used to determine rainfall and temperature scenarios based on HSMprevious studies of climate scenario (see e.g. Ardoin-Bardin, 2004; Ardoin-Bardin et al., 2009).Other methods like those proposed by Deque (2007) could also have been used. This authorsuggested three other methods: (i) the confident method if CM simulations were more reliable;(ii) the variable correction method if statistical techniques appeared to be necessary; and (iii)the regime method if CM simulations poorly represented the model variables. Deciding on themost appropriate method in this study would have implied a previous work comparing thosemethods. It would have get the study too dense and complicated.

Third, some limits are certainly provided by the hydrological model. As the aggregationfunction is not perfectly null, it might induces slight errors in simulation results. Moreover, itwas validated for certain ranges of observed rain and temperature values. It appears that forCSIRO, rainfall values are largely out of range. In addition, temperature (so PET) increases,not so much in proportion, but more than the values used to calibrate the model. So it isnot sure that HS is well calibrated for such ranges and the long-term results, simulated by thehighest rainfall and PET values, are quite questionable.

35

Discussion

5.3 Comparisons with other studies

This study differs in its methods and results with other analogue works. First of all, adaily hydrological model is rarely used to simulate future discharge in the context of climatechanges. In most research, monthly or annual time-step models are used (see e.g. Serrar-Capdevila et al., 2007; Buytaert et al., 2009; Sperna Weiland et al., 2010). Other authorslike Ducharne et al. (2003), Chiew et al. (2010) used a daily time-step to model the impactof climate change on hydrological systems. This is however quite unusual on such large scalecatchment because most GCM outputs are available with a monthly time-step. Moreover, thismodelling method proved to be efficient and appropriate for the West African climate.

Secondly, the temporal and spatial downscaling methods used in this study are a combi-nation of the various methods that are usually used. Salathe (2003) has already pointed outthat precipitation spatial downscaling on a large-scale basin does not necessarily have to bedone using a complex method. A simple method can capture the essential information andaccurately simulate discharge. Thus, the simple spatial downscaling method used in this studyappears to be a simple and efficient tool to use for GCM outputs. The temporal downscalingused for the monthly CM series by the calculation of adjustments is also a simple methodwhich results in a satisfactory daily series. Generally, authors like cited previously simulatedischarge on the time-step that is available by the GCMs. In this work, using the PET simpleformula is a useful tool to generate daily PET series based on monthly temperature. Thekey issue of the methods was obtaining daily rainfall at a high spatial resolution across thebasin. The hydrological model is mainly constrained by rainfall which varies a lot over timeand principally determines the evolution of water resources in the Sudano-Sahelian context.So working with this variable at a high spatial and temporal resolution was one of the mainchallenges of this study.

Thirdly, comparing various GCMs with one RCM results based on a SRES scenario is un-usual. There are examples of comparisons between a RCM and the GCM it is based on (seee.g. Vigaud et al., 2009) or between various GCMs (e.g. Chiew et al., 2009). However, usingWRF to compare results with other GCMs proved unsuccessful in this study. This RCM failedto meet expectations. As far as simulations in the reference period are concerned, seasonalvariability was not better than for other GCMs, not was the inter-annual variation or rainfallspatial distribution. In addition, these simulations were for a period of 10 years, which does notcorrespond with the climatic period (i.e. 30 years) that is recommended by the IPCC (2007).It made the task of comparing GCMs complicated and tedious. In the future, WRF simula-tions are expected to improve, as RCMs are supposed to accurately model regional climate. Inthe Sudano-Sahelian context, in which climate data is rare and modelling is complicated, thiskind of climate model is not available. This makes WRF a useful and possible candidate forfurther studies.

Finally, there have already been several studies on the impact of climate changes on waterresources in West Africa. Especially in West Africa, over-exploitation of the environment andnatural resources in past decades has changed the water balance and sediment budget (Descroixet al., 2009). Furthermore, in West Africa the combination of decreasing rainfall and increasingtemperature get plausible a stronger aridity scenario in the region (Nicholson, 2001). Thesekind of changes need to be identified and water availability studied so data can be provided foradvanced studies such as food security and strategic water planning and management (Schuolet al., 2008). Senegal has a similar climate to Mali. Rasmussen (2001) assessed the effects ofclimate change on agriculture and environment in this country. He showed that if rainfall does

36

Discussion

not increase greatly and is combined with higher temperatures, crop and rangeland productivitywill reduce in the long-term. This would lead to an intensification of competition for waterresources, especially when they are shared by several countries. So as the GCMs in this studyall simulated increasing temperature and a generalized decrease in rainfall, such a conclusionfollows for this catchment too. This is quite a pessimistic forecast, above all when you add inincreasing demographic pressure.

37

Conclusion

6 Conclusion

Located in West African, Mali has suffered from a severe drought since the 1970-80s andfuture water availability is becoming a pressing issue. This study assessed the impact of cli-mate change on water resources in the Bani catchment in Mali. It used several climatic modeloutputs to construct future rainfall and temperature scenarios for the study area. Using asimple temperature-based formula, PET scenarios were also calculated and used with rainfallscenarios to feed a conceptual hydrological model and simulate future discharge in variousfuture horizons.

The results indicate that most CMs can reproduce the observed mean seasonal variabilityfor rainfall and temperature. However, inter-annual and spatial representations of these vari-ables are highly inaccurate. Relative trends have been calculated based on the CM outputsand then applied to the daily observations in order to generate future series, called climaticscenarios. These scenarios follow various patterns. While one GCM tends to simulate veryoptimistic climate series, others remain within observations ranges or slightly below it. Thisinduces discharge trends that over increase in one case but generally decrease in the long-termin other cases. These results are better illustrated with simulated cumulated volume evolu-tions; most GCMs show decreasing water resource. The RCM studied here highlighted theimportance of PET in simulations for discharge and final cumulated volume. Even though itwas constrained by a GCM, it does not produce the same results; it is more optimistic for theevolution of water resources in the short-term.

This study is subject to various limitations that are mainly due to the climate modellingproblems of GCMs. However, it is innovative in the way climate variables are processed andcompared, and how daily discharge is simulated in a large-scale catchment. It is particularlyinteresting by the methods used to estimate water resources in future horizons. As climatemodelling is improving and is regularly a subject for the IPCC working groups, such methodscan be accepted and re-used when necessary.

Finally, long-term simulated water resources in the Bani catchment tend to decrease orare similar to current water availability conditions. However, this does not take into accountincreasing demographic pressure in future decades (this is one of the main hypothesis of theSRES A2 scenario). This study ignores other processes that may influence hydrological dy-namics (e.g. water use, irrigation, soil and land-use changes). Such processes are mainlyconstrained by the population rate. Furthermore, a feedback exists between the surface andatmosphere meaning that anthropogenic modifications of land surface may influence regionalclimate (Nicholson, 2001). This makes climate evolution in the long-term difficult to predictand raises social questions for the local populations. Socio-economic activities in the region aretotally dependent of the environment. Decreasing flows simulated by GCMs in the Bani Riverwould induce lower water supply in the inner Niger Delta. At present, more than 1 millionpeople live and work there, living off the Niger floods. So, if the flood surface decreases andthe population increases, it would cause adaptation and vulnerability problems to the localinhabitants. These people generally have little chance of making changes to their lifestyle.This is an major issue that needs to be closely examined by the region’s decision-makers.

38

References

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42

List of acronyms

List of acronyms

ANR Agence Nationale de la RechercheCACHEMIRE ClimAt, CHangements Environnementaux et Modelisation de leur Impact

sur les Ressources en EauCM Climatic ModelCNRS Centre National de la Recherche ScientifiqueCRC Centre de Recherche de Climatologie (Universite de Dijon)CRU Climate Research Unit (University of East Anglia, Norwhich)CSIRO Commonwealth Scientific and Industrial Research OrganizationFagg Aggregation FunctionGCM General Circulation ModelHS HydroStrahlerHSM HydroSciences MontpellierIPCC Intergovernmental Panel on Climate ChangeIRD Institut de Recherche pour le DeveloppementMPI-M Max-Planck-Institute for MeteorologyPEavg annual average Peak ErrorPET Potential EvapoTranspirationRCM Regional Circulation ModelRESSAC vulnerabilite des Ressources en Eau Superficielle au Sahel aux evolutions

Anthropiques et Climatiques a moyen termeSRES Special Report for Emission ScenariosUKMO-HadCM3 United Kingdom Meteorological Office - Hadley Centre Coupled Model

version 3VE Volume ErrorV Eavg annual average relative Volume ErrorWRF Weather Regional Forecast

43

List of figures and tables

List of Figures

1 Geographical localization of the Bani catchment at Douna (104 000 km2) . . . 52 Hydro-climatic changes on the basin at Douna over the last 50 years: (a) Plu-

viometric index over the 1950-2000 period; (b) Rainfall and discharge evolutioncomparing the 1952-1970 and 1971-2000 periods (Ruelland et al., 2008a; 2009) 6

3 Land use map of the Bani catchment at Douna in 1986 (Ruelland, 2009) . . . 74 Principle of the HydroStrahler model (Ruelland et al., 2009) . . . . . . . . . . 85 Isohyets, rainfall and discharge gauging stations over the Bani catchment . . . 106 CM monthly temperature and rainfall study periods over 1950-2099 . . . . . . 147 Mean seasonal dynamics (a) in rainfall and (b) in temperature over 1961-1990 158 Mean seasonal dynamics (a) in rainfall and (b) in temperature over 1981-1990 169 Observed and simulated inter-annual variability over 1961-1990 on the catch-

ment: (a) pluviometric index; (b) mean annual temperature . . . . . . . . . . 1710 Observed and simulated inter-annual variability over 1981-1990 on the catch-

ment: (a) pluviometric index; (b) mean annual temperature . . . . . . . . . . 1811 Comparison of CM output spatial distribution with observations over 1961-1990

and 1981-1990 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2012 Construction of future climatic scenarios based on GCM/RCM outputs and

observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2213 Comparison of HS and GCM grids on the Bani watershed (with CSIRO as an

example here) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2514 Comparison of observed vs. simulated hydrographs at the Douna gauging station

over the period 1952-2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2715 Simulated rainfall based on GCM simulations at short, mid and long term . . 2916 Simulated rainfall based on ARPEGE-10 and WRF simulations at short-term 2917 Simulated PET based on GCM simulations at short, mid and long term . . . . 3018 Simulated PET based on ARPEGE-10 and WRF simulations at short-term . . 3019 Simulated discharge according to GCM simulations at short, mid and long-term 3120 Simulated discharge according to ARPEGE-10 and WRF simulations at short-

term . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3221 Cumulated simulated volume based on MPI-M, HadCM3 and ARPEGE simu-

lations over 1952-2099 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3322 Cumulated simulated volume based on WRF and ARPEGE simulations over

1952-2041 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3423 GCM delta and unbiasing results over the catchment from 2011 to 2099: (a)

bias for rainfall; (b) delta for temperature (oC) . . . . . . . . . . . . . . . . . . 4624 ARPEGE and WRF delta and unbiasing results over the catchment from 2032

to 2041: (a) bias for rainfall; (b) delta for temperature (oC) . . . . . . . . . . . 4625 Simulated discharge based on CSIRO simulations at short, mid and long-term 4726 Cumulated simulated volume based on GCM simulations over 1952-2099 . . . 48

List of Tables

1 Description of the studied GCMs and RCM . . . . . . . . . . . . . . . . . . . 132 Goodness of fit scores of the hydrological model over calibration and validation

periods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263 Bias and delta averaged over the catchment in the short, mid and long-term . 284 Optimized parameters obtained for the hydrological model . . . . . . . . . . . 47

44

Appendix

Appendix

45

Appendix

Appendix 1: CM delta and unbiasing method results

Figure 23: GCM delta and unbiasing results over the catchment from 2011 to 2099: (a) biasfor rainfall; (b) delta for temperature (oC)

Figure 24: ARPEGE and WRF delta and unbiasing results over the catchment from 2032 to2041: (a) bias for rainfall; (b) delta for temperature (oC)

46

Appendix

Appendix 2: Parameters obtained by the multi-objective calibration routinesfor HS

sat ssrr infr gwrr βssrr βinfr782 0.0034 0.0015 0.059 0.0005 0.0026

Table 4: Optimized parameters obtained for the hydrological model

Appendix 3: CSIRO simulated hydrographs in the future

Figure 25: Simulated discharge based on CSIRO simulations at short, mid and long-term

47

Appendix

Appendix 4: CSIRO and other GCMs cumulated simulated volumes

Figure 26: Cumulated simulated volume based on GCM simulations over 1952-2099

48

Abstract Because of severe climatic changes over many decades, environmental and naturalresources have evolved in West Africa. This study assesses the impact of future climate changeon water flows at the outlet of the Bani watershed (Mali). Many general climate models (GCM)and one regional climate model (RCM) have been used to provide future climate scenarios overthis area. Based on the SRES-A2 scenario, outputs from these climate models were used togenerate daily rainfall and temperature series in the short, mid and long-term: (i) accordingto the Unbias and Delta Methods application and (ii) temporal and spatial downscaling. Asimple temperature-based formula was used to calculate future daily PET. Both rainfall andPET daily series have been introduced into the HydroStrahler model (calibrated and validatedover 1952-2000) to simulate future discharge. Results show that various future trends for waterresources can be expected. Using the WRF RCM does not provide better results than GCMs.The CSIRO GCM is the most optimistic model (the simulated water volume is 10.5 timeshigher than with the others): it thus does not appear to be relevant as the simulated dischargevalues are out of the observed ranges. The ARPEGE GCM implies discharge comparable tothe wet 1950-60s. The MPI-M and HadCM3 GCMs induce in the long-term water resourcesas scarce as in the 1970-80s. This latter trend would tend to reduce water resources if demo-graphic pressure still increases, which would make the local populations more vulnerable.

Key words Climatic projections; Hydrological modelling; Hydro-climatic variability; HydroS-trahler; River Bani

Resume Les changements climatiques qui marquent l’Afrique de l’Ouest depuis quelquesdecennies ont fait evoluer les ressources naturelles. Cette etude presente l’impact des pos-sibles changements climatiques sur les debits a l’exutoire du bassin du Bani (Mali). Plusieursmodeles climatiques globaux (MCG) et un modele regional ont ete exploites afin de fournir desprojections climatiques dans ce secteur. Les sorties des modeles climatiques sous l’influencedu scenario SRES A2 ont ete utilisees pour generer des scenarios journaliers de precipitationset de temperatures a court, moyen et long-terme: (i) application de la methode du biais et dudelta; (ii) changement d’echelle spatial et temporel. L’ETP a ete estimee a partir d’une formulesimple. Les series journalieres de precipitation et d’ETP ont ete introduites dans le modeleHydroStrahler, prealablement cale et valide sur 1952-2000, pour simuler les debits futurs. Lesresultats montrent que plusieurs tendances peuvent etre attendues dans le futur. L’utilisationdu modele regional WRF ne presente pas d’amelioration par rapport aux MCGs. Le modeleCSIRO est le plus optimiste (10,5 fois plus de volume d’eau que les autres MCGs) mais neparaıt pas pertinent car les valeurs simulees n’ont jamais ete observees sur le bassin versant.Le modele ARPEGE induit des debits futurs comparables aux annees humides passees. Lessimulations des modeles MPI-M et HadCM3 conduisent a long terme a des annees seches com-parables a celles observees dans les annees 1970-80. Ces dernieres tendances entraıneraientune reduction importante des ressources en eau a la fin du siecle si la pression demographiquecontinue d’augmenter, ce qui pourrait fragiliser la vie des populations locales.

Mots clef Projections climatiques; Modelisation hydrologique; Variabilite hydro-climatique;HydroStrahler; Riviere du Bani