Water resources planning and management in Africa in the context of climate change

Challenges, initiatives and management tools

___________________________________Abou AMANI, ing, Ph.D

Natural Science programme Specialist

UNESCO Accra office (Sierra Leone, Liberia, Cote d’ivoire, Ghana, Togo and Benin)

GHANA

E-mail: a.amani@unesco.org

Some realities• Around 40% of the population in WA have not

access to clean water (around 100 million inhabitants)

• 60% for sanitation• More than 80% of deseases are water related

diseases (Malaria, cholera, Guinea Worm,..;);• Many countries are still struggling to cope to food

insecurity;

• Less than 4% of renewable WR are mobilized in WA;

• Only around 10% of irriguable lands are irriguatedin WA;

• Only 16% of hydro-power potential are mobilized.Ecowas water policy (2007)

Importance of climate in WAAround 70% of the population in WA are rural

population with agriculture and livestock as main activities;

Agriculture in WA is essentially rainfed and istherefore highly vulnerable with climatefluctuations;

Livestock is greatly sensitive to the availability of Biomass and water resources;

The primary Sector contributes to around 35% of the GDP of many countries in WA and is an important factor of economic growth.

Rainy season is an important factor in the region

IPCC(2001, 2007) have qualified Africa as the most vulnerable continent to climate change

Plan of the presentation• Characteristics of WR in WA• Climate Variability and its impacts • Climate change / Global change and its

impacts• Africa Water Vision 2025• Research priorities• Institutionnal interventions• Operational and Scientific interventions• Management Tools

Water resources in Africa___________________________________________________

Surface water

Concentrated within few river basins

cooperation between riparian countries is needed

groundwater

repartition depends on the hydrogeology

few shared hydrogeological basins

cooperation is also needed

Renewable water resources in WA

UNWWDR2, AQUASAT, 2005 M3/capta/yearBenin 3820Burkina 930Cape Verde 630Cote d'Ivoire 4790Gambia 5470Ghana 2490Guinea 26220Guinea-bissau 20160Liberia 66530Mali 7460Mauritania 3830Niger 2710Nigeria 2250Senegal 3810Serra Leone 30960Togo 2930Chad 4860

In 2025, only by the effect of the population growth,

Six countries will be in scarcity or stress situation and two countries will not be far from the stress threshold

Rainfall variability in West AfricaSeasonal variability

Around 80% of rainfall during JAS in SahelGreat inter-annual variability

Break in annual cumul time series, Nord-south shifting of annual isohyetsdecrease of number of rainy days,Increasing of dry spellGreat variability of onset

Great spatial variability

Impacts on water resources (1/2)Water resources concentrated within 3-4 months

Great inter-annual variabilitydecrease of water availability(40-60%)great reduction of wetland areas

Decrease of groundwater table

Impacts on water resources (2/2)

Increase for small and localized systems

Increase of runoff coefficient and dischargedue to great modification of land use land cover

Increase of aquifer water table

due to land use land cover changes and recharge process

Future?

What is the future evolution?

From IPCC TAR and FAR.

• Temperature global increase 1.4-5,8°C • Increasing of Maxi and Mini temperature• Reinforcement of the hydrological cycle and

the extremes (droughts and floods)• Sea level rise 9-88 cm

• Global increase of rainfall, spatial variation not clear, still models are not consistent in manyparts of Africa and not capture very well the regime

IMPACTS

• Impacts on water resources• Impacts on agriculture• Aggravation of coastal erosion• Flooding of coastal low areas• Degradation of some ecosystems• Reinforcement of extremes (too much or too

little water)

Adaptation: A necessity– Climate change is a reality – Ecosystems continue to be degradated– Great vulnerability of the population vis-à-

vis climate change;– Different water uses and more competion

with the increasing of populuatin needsIntegrated Water resources Management

(IWRM) a systematic process for the sustainable development, allocation and monitoring of water resource use in the context of social, economic and environmental objectives.

Africa Water Vision 2025

AN AFRICA WHERE THERE IS AN EQUITABLE AND SUSTAINABLE USE AND MANAGEMENT OF WATER RESOURCES FOR POVERTY ALLEVIATION, SOCIOECONOMIC DEVELOPMENT, REGIONAL COOPERATION, AND THE ENVIRONMENT.

A vision1. There is sustainable access to a safe and adequate water

supply and sanitation to meet the basic needs of all;2. Water inputs towards food and energy security are

readily available;3. Water for sustaining ecosystems and biodiversity is

adequate in quantity and quality;4. Water-resources institutions have been reformed to

create an enabling environment for effective and integrated management of water in national and trans-boundary water basins, including management at the lowest appropriate level;

5. Water basins serve as a basis for regional cooperation and development, and are treated as natural assets for all within such basins;

A vision6. There is an adequate number of motivated and highly

skilled water professionals;7. There is an effective and financially sustainable system

for data collection, assessment and dissemination for national and trans-boundary water basins;

8. There are effective and sustainable strategies for addressing natural and man-made problems affecting water resources, including climate variability and change;

9. Water is financed and priced to promote equity, efficiency, and sustainability;

10. There is political will, public awareness and commitment among all for sustainable management of water resources, including the mainstreaming of gender issues and youth concerns and the use of participatory approaches.

Research priorities• Improvement of knowledge on climate and its impacts

(Ex: Programme AMMA);• Improvement of knowledge on the water resources and

particularly on groundwater;• Tools for decision making for sustainable management of

water resources;• Promotion of IWRM and ecosystem approach for the

management of water resources and wetlands;• Identification, promotion and diffusion of technologies

and appropriate measures for adaptation to climatechange;

Institutional initiatives• AMCOW (Committee of African Ministers in

charge of Water)• African Water Facility hosted by African

Development Bank• African Commisssion on Groundwater• African water week• Creation of River basin organizations (OMVS,

OMVG, NBA, VBA, LCBC, ..), RAOB • Creation of Water Coordination Unit for ECOWAS

and SADC• UN-Water / Africa• National, sub-regional water partnerships (IWRM)

Operational and research initiatives

• WHYCOS (SADC, IGAD, CONGO, NIGER, VOLTA, LCBC, OMVS);

• FORA on seasonal forecast (PRESAO, PRESAC, South, East, North)

• FRIEND for hydrology and water resources (SADC, AOC, Nile)

• ISARM (SADC, AOC), GRAPHIC, TIGER

• RIPIESCA, ACCA• AMMA: research on African Monsoon

and its impacts

Some Tools

TDA/SAP for IWRM• IDF curves and Flood frequency Analysis Need to take into account climate change

dimension• Early Warning Systems

– Flood– Drought : Africa Drought Monitor – Seasonal forecast : PRESAO

Find more

AMMA (http://www.ird.ne/ammanet)

PRESAO (http://www.acmad.ne; http://www.agrhymet.ne) ;

WWDR (http://www.unesco.org/water) ;

VIC Land Surface ModelSVATS

Input Variables Input parameters

Elevation

Vegetation

Soil porosity

RainfallTemperatureWindspeedRadiationPressure

Output variablesSoil moisture (three layers)evapotranspirationdischarge

Spatial resolution1° by 1°

Time steps3 h, daily

PGF501948-2000, 3hr, daily, 1.0degP, T, Lw, Sw, q, p, w

CRU1901-2000, Monthly, 0.5degP, T, Tmin, Tmax, Cld

GPCP1997-, Daily, 1.0degP

UW1979-2000, Daily, 2.0degP

TRMM2002-, 3hr, 0.25degP

SRB1985-2000, 3hr, 1.0degLw, Sw

NCEP/NCAR Reanalysis1948-, 3hr, 6hr, daily, T62P, T, Lw, Sw, q, p, w

ReanalysisHigh temporal/low spatial resolution

ObservationsGenerally low temporal/high

spatial resolution

Bias-CorrectedHigh temporal/high

spatial resolution

Global Forcing Dataset

Development of Global Real-Time Drought Monitoring

1) Retrospective Simulation -- DONE

2) Calculate Soil Moisture Index -- DONE 3) Historical Drought Analysis -- DONE

Historic soil moisture

Realtime soil moisture

0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 1Soil Moisture

Cum

ulat

ive

Prob

abilit

y

Princeton University

4) Real-time Drought Analysis – NEED REAL TIME FORCINGS

0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 1Soil Moisture

Cum

ulat

ive

Prob

abilit

y

GTS observations

ECMWF analysis

Satellite observations(NASA, ESA, JAXA) (PERSIANN dataset)

Focus on African Drought

Water Cycle and Drought Monitoring over Africahttp://hydrology.princeton.edu/monitor

Terrestrial water cycle (evaporation, runoff, soil moisture, snow) simulated using the VIC land surface model, forced by observed and remotely sensed precipitation and temperature

We are also monitoring globally…

TESTS AND VALIDATION

UNESCO-IHP and UNIVERSITY OF PRINCETON will work with AGRHYMET and NBA for the test and validation of the different products

- Datasets used in the region compared to the observations (AGRHYMET Network, EPSAT)

- Coherence of soil moisture pattern (comparison with some DHC ouputs)

- Comparison with discharge indexes of main rivers in the sub-region

Could be used as a complementary tool for the monitoring of therainy season by AGRHYMET

Model of black BoxSST discharge

Methodology for the seasonalforecast of discharge

Search for a direct statistical link between SST and discharge

Oceanic areas having an influence the West AfricaClimate

Atl NW

1940 1950 1960 1970 1980 1990 2000YEAR

-2

-1

0

1

2

3In

dice

s de

débi

t

MOYP_MOY

NIGER at NIAMEY:Compared discharge indexes observed and forecasted

Rank the index values from the highest to the smallestand divide the series in three categories: high, medium and low hydraulicity.

-Based on the forecast index, find the category of the forecast

Steps for the seasonal forecast

Above normalnormal

Below normal

-15 -10 -5 0 5 10 15 20 25

5

10

15

20

25

ZONE I

ZONE II

ZONE III4525

304525

30 4020

40

3545

20

PREVISION SAISONNIERE DES PLUIES EN AFRIQUE DE L'OUEST, LE TCHAD ET LE CAMEROUN

JUILLET- AOUT- SEPTEMBRE 2002

Niger à Koulikoro

05

1015202530354045

0 1 2 3 4 5 6 7 8 9 10 11 12

Mois

Volu

me

écou

lé (m

illia

rds

de

m3)

moy99/20002000/20012001/20022002/20032003/20042004/2005

HAUT BASSIN DU NIGER

Niger à Niamey

0

5

10

15

20

25

30

35

40

0 1 2 3 4 5 6 7 8 9 10 11 12

Mois

Volu

me

écou

lé (m

illia

rds

de

m3)

Moy99/20002000/20012001/20022002/20032003/20042004/2005

NIGER MOYEN

Chari à N'Djamena

0

5

10

15

20

25

30

35

0 1 2 3 4 5 6 7 8 9 10 11 12

Mois

Volu

me

écou

lé (m

illia

rds

de m

3)

Moy99/20002000/20012001/20022002/20032003/20042004/2005

BASSIN DU LAC TCHAD

Validation of the forecast for six years

Quality of the forecast by basin

Niger Moyen (5/6)Bassin du Sénégal (4/6)Bassin du lac Tchad (4/6)Haut bassin du Niger (3/6)

Quality of the forecast by year

2000 et 2001 (100%) Globally (16/24)2002 (75%) 67%1999 et 2003 (50%)2004 (25%)

0

10

20

30

40

50

60

70

80

90

100

Benin

Burkina

Faso

Cabo V

erde

Côte d'

Ivoire

Gambia

Ghana

Guinée

Guinée

-Biss

au

Liberi

a

MaliMau

ritanie

Niger

Nigeria

Sénég

alSier

ra Le

one

Togo

Afrique

de l'o

uest

% p

erso

nnes

aya

nt a

ccès

à l'

eau

199020022015

Percentage of access to clean water (2002)

0

10

20

30

40

50

60

70

80

Benin

Burkina

Faso

Cabo V

erde

Côte d'

Ivoire

Gambia

Ghana

Guinée

Guinée

-Biss

au

Liberi

a

MaliMau

ritanie

Niger

Nigeria

Sénég

alSier

ra Le

one

Togo

Afrique

de l'O

uest

% p

opul

atio

n ay

ant a

ccès

à u

n as

sain

isse

men

t am

élio

ré

199020022015

Percentage of access to sanitation(2002)

Pluies journalières 1994

0

20

40

60

80

100

120

1-Apr

15-A

pr29

-Apr

13-M

ay27

-May

10-J

un24

-Jun

8-Ju

l22

-Jul

5-Aug

19-A

ug2-

Sep

16-S

ep30

-Sep

14-O

ct28

-Oct

Cumul jo

urna

lier (

mm)

Pluie décadaire 1994 à Dori

0

20

40

60

80

100

120

140

160

180

mai1 mai2 mai3 juin1

juin2

juin3

juillet1

juillet2

juillet3

aout1

aout2

aout3

septem

bre1se

ptembre2

septembre

3oc

tobre1

octob

re2octo

bre3

Cum

ul d

écad

aire

(mm

)

Pluie mensuelle 1994 à Dori

0

50

100

150

200

250

mai juin juillet août septembre octobre

Cum

ul m

ensu

el (m

m)

OUAGADOUGOUBISSAU

BAMAKO

NOUAKCHOTT

NIAMEY

DAKAR

NDJAMENA

BANJUL

-15 -10 -5 0 5 10 15 20 25

10

15

20

25isohyètes moyennes de la période 1968-97 en millimètres

isohyètes moyennes de la période 1950-67 en millimètres

Shift of isohyets after 1970

Fig: Déplacement des isohyète vers le sud (L’Hôte,Mahé, 1996)

-10 -5 0

5

10

15Δ Total Rainfall (%)

-10 -5 0

5

10

15Δ Number of events (%)

-10 -5 0

5

10

15Δ Mean event rainfall (%)

-18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18

10

12

14

16

18

Latit

ude

-40 -20 0 20 40 60 80 100 120km

0

20

40

60

80

100

km

400425450475500525550575600625650675700725750775800

1992

The scales of rainfall variability

0 20 40 60 80 1000

20

40

60

80

100

0 20 40 60 80 1000

20

40

60

80

100

Wet Years

1994

1998

0 20 40 60 80 1000

20

40

60

80

100 1992

0 20 40 60 80 1000

20

40

60

80

100 2000

0 20 40 60 80 1000

20

40

60

80

100

1990

0 20 40 60 80 1000

20

40

60

80

100 1997

Dry YearsModeratly dry

Gradient: 460 mm/24 km

Gradient: 270 mm/9 km

Gradient: 240 mm/10 km

Mesoscale view of Wet vs Dry years

-40.00 -20.00 0.00 20.00 40.00 60.00 80.00 100.00 120.00

1990

0.00

20.00

40.00

60.00

80.00

100.00

-40.00 -20.00 0.00 20.00 40.00 60.00 80.00 100.00 120.00

1990-1991

0.00

20.00

40.00

60.00

80.00

100.00

-40.00 -20.00 0.00 20.00 40.00 60.00 80.00 100.00 120.00

1990-1994

0.00

20.00

40.00

60.00

80.00

100.00

-40.00 -20.00 0.00 20.00 40.00 60.00 80.00 100.00 120.00

1990-1993

0.00

20.00

40.00

60.00

80.00

100.00

#

#

#

##

##

#

# ##

#

##

###

#

#

##

##

#

# #

#

#

#

#Bui

Lai

Ati

BaroSeme

BaKel

BonouBambou

Niamey

Lokoyo

Simenti

Moundou

FaranahBeterou

Kedougou

Makourdi

N'Djamena

Kouroussa

Dialakoro

GouloumbouGarbe-KouroDouna (Bani

Dioila (BaoPankourou (

Mallanville

Sarh

STATIONS HYDROMETRIQUESPRESAO II

BassinsChariComoéGambiaGorubalKomaduguNigerSénégalVolta

Limite des PaysLimite Cours d'eauCours d'eau

75% of Surface Water concentrated major basins: congo, Niger, Zambezi, Nile, Chari-logone, Volta, limpopo, Senegal, Orange)

Over 80 transboundaryriver/lake basins

0

20

40

60

80

100

120

Benin

Burkina

Cape V

erde

Cote d'

Ivoire

Gambia

Ghana

Guinea

Guinea

-biss

auLib

eria

MaliMau

ritania

Niger

Nigeria

Seneg

alSerr

a Leo

neTog

o

Water Dependance Index, UNWWDR2, AQUASAT, FAO, 2005

Around 41 transboundaryaquifers identified so far

There is a great lack of scientific knowledge on TBA in Africa

Eaux souterraines

PartagPartagéées entre plusieurs pays;es entre plusieurs pays;

De profondeurs variables De profondeurs variables

suivant les formations suivant les formations

ggééologiquesologiques

TrTrèès mal connues en quantits mal connues en quantitéé

et qualitet qualitéé, même si des , même si des

importantes quantitimportantes quantitéés ds d’’eau sont eau sont

disponibles (trdisponibles (trèès peu de s peu de

mesures pimesures piéézomzoméétriques) triques)

Principales sources dPrincipales sources d’’eau eau

potable pour lpotable pour l’’alimentation des alimentation des

populationspopulations

ProblProblèèmes de qualitmes de qualitéé

(intrusion saline surtout pour les (intrusion saline surtout pour les

nappes côtinappes côtièères)res)

1. Bassin de Nubie – 2. Sahara Septentrional – 3. Bassin Sénégal-Mauritanien –4. Bassin de Taoudéni – 5. Bassin de Mourzouk-Djado – 6. Bassin Irhazer-Iullemeden – 7. Bassin du Tchad – 8. Bassin d’Errachidia

Source : Unesco, 1995

0

20

40

60

80

100

120

Benin

Burkina

Cape V

erde

Cote d'

Ivoire

Gambia

Ghana

Guinea

Guinea

-biss

auLib

eria

MaliMau

ritania

Niger

Nigeria

Seneg

alSerr

a Leo

neTog

o

Indices de dépendance, AQUASAT, FAO, 2005

Renewal Water Resources (m3/yr/h)

0

10000

20000

30000

40000

50000

60000

70000

Benin

Burkina

Cape V

erde

Cote d'

Ivoire

Gambia

Ghana

Guinea

Guinea

-biss

auLib

eria

MaliMau

ritania

Niger

Nigeria

Seneg

alSerr

a Leo

neTo

go

Water Resources 2005 (FAO, AQUASAT, 2005)

0

2

46

8

1012

14

16

Benin

Burkina

Cape V

erde

Cote d'

Ivoire

Gambia

Ghana

Guinea

Guinea

-biss

auLib

eria

MaliMau

ritania

Niger

Nigeria

Seneg

alSerr

a Leo

neTo

goWater mobilisation index (FAO, AQUASAT, 2005)

Niger à Koulikoro

0500

10001500200025003000350040004500500055006000

05-janv 24-févr 15-avr 04-juin 24-juil 12-sept 01-nov 21-déc

Date

Déb

it (m

3/s)

moy2002200320042005

UPPER NIGER BASIN

Varition du module standardisé sur le Chari à N 'D jaména

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

1955 1965 1975 1985 1995

Année

Mod

ule

stan

dard

isé

Modules standardisés à Bakel

-2

-1,5

-1

-0,5

0

0,5

1

1,5

2

2,5

Année

Mean annual discharge index at Bakel beforeManantali Dam

Modules standardisés de la Gambie à Goloumbo

-2-1.5

-1-0.5

00.5

11.5

22.5

1954

1957

1960

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

Année

Mod

ules

sta

ndar

disé

sMean annual discharge indexes at Golombo on

Gambia river

-2,5-2

-1,5-1

-0,50

0,51

1,52

2,5

1941 1951 1961 1971 1981 1991

Year

Inde

xStandardarized mean annual discharge at

Niamey on Niger River

Streamflows of main rivers in WA

0

500

1000

1500

2000

2500

29-juin 18-août 07-oct 26-nov 15-janv 05-mars 24-avr 13-juin

Date

Disc

harg

e (m

3 s-1)

avant 1969

après 1969

Evolution of mean daily discharge before and after 1969 at Niamey River station

Niger at Malanville: 1.106 km²

Changes compared

Lake Chad decrease

Decrease of aquifer water table

Two Different regimes

Increase of discharge

Increase of aquifer water tableNiamey region

Rendement moyen mil / sorgho au Niger

0100200300400500600700

1953

1957

1961

1965

1969

1973

1977

1981

1985

1989

1993

1997

année

Ren

dem

ent m

il / s

orgh

o (k

g'ha

)

Prélèvements actuels de ressources en eau en Afrique de l'Ouest

AgricultureCollectivitésIndustrie76%

17%

L’érosion côtière

Yenne sur mer

Rufisque

Photo NIANG I.

Photo NIANG I.

Nom

bre

Evolution of disasters between 1991 and 2000, WMO 2002

(OMM-N° 936, Genève 2002)

Mozambique (Février et mars 2000) : Inondations provoquées par le passage des tempêtes tropicales Elyne et Gloria : 1 milliard de $ US de pertes

Niamey, août 2000

Wrong Seasonal Cycle• Onset• First Rainy Season on the coast

Seasonal cycle : Sahelian mesh

0

10

20

30

40

50

60

0 4 8 12 16 20 24 28 32 36

10 days periods

Ave

rage

rain

fall

GCMObs

13° NSeasonal cycle : Coastal mesh

0

50

100

150

200

0 4 8 12 16 20 24 28 32 36

10 days

Rai

nfal

l

GCMObs

7° N

Average: 1960-1990

Seasonal cycle in a GCM

Is this due to a wrong forcing from the oceans or to a bad water budget over land (too much recycling) ?

Average: 1960-1990

Daily rainfall in a GCM (1.65°*3.75°)

Mesh 8.8

1

10

100

1000

10000

0 5 10 15 20 25Daily rainfall (mm)

Num

ber o

f day

s a

bove

thre

shol

d

Nbj Obs >

Nbj GCM>

Model

Obs.

Daily rainfall in climate models

Signal Pluviométrique Annuel

0

20

40

60

80

100

120

140

160

180

200

Janvie

r

Févrie

r

Mars

Avril

Mai Juin

Juille

t

Août

Septe

mbre

Octobre

Novem

bre

Décembre

Observations HadCM3 ECHAM4 NCAR CCSR CSIRO CGCM2

COMPARAISON OFF DIFFERENTS GCMs

SAHEL SUB-REGION

PROBLEMATIC OF CLIMATE IMPACT ASSESSMENT

ClimateModels Biophysical

Models

ResourceswaterYield

Biomass..etc

Need for downscaling

300k

m

50km

10km

1m

Poin

t

AMMA General Scheme

DECISION MAKERS

Hydrology, Energy & Agriculture (National & Regional)

• Health and foodsécurity

• Agencies and NGOGovernments

Prediction: weather, climate and impactsIntraseasonal / Season - Interannual / Decadal /Climate Change

IMPACTS

Water Ressources

FoodSecurity

HealthEpidemiology

EconomyDémography

MonsoonDynamics

MultidisciplinaryResearch

103

Enhanced Obs. Period

Long term Observation Period

2001

Supra-regional(WA + Ocean)

Meso

Regional(WA)

Sub-Regional(Transects)

Local

SPACE (km)

2004 2005 2006 2007

103

101

102

104

1

IOPs

2

IOPs

3

IOPs

S O P

2009TIME (Years)

IDAF AERONET

Time Diagram

Cotonou

Sal

Dakar

Niamey

Abidjan

OuagaBamako

LagosLamto

Niamey

Weather Radar

Aircraft operations

Radio-sounding

Photometer

LOP Window

-2 0 2 44

6

8

10

12

14

16

18

Hombori

Bani

Djougou

Bani : super-sites

: mesoscale AMMA sites

1GeoSFMGeoSFM (FEWS) : Stream Flow Model(FEWS) : Stream Flow Model

Input Input datadata-- dailydaily rainfallrainfall (estimation, (estimation, forecastforecast))-- dailydaily ETPETP-- SoilSoil (FAO (FAO MapMap))-- Land Use / Land Land Use / Land CoverCover (NOAA)(NOAA)-- DEM(1Km, NOAA)DEM(1Km, NOAA)

Output:Output:-- dailydaily dischargedischarge-- water water levellevel-- inundationinundation areasareas

Preprocessing

MAP

MAE

Basin

Linkage

Routing Paramet

ersSoil Paramet

ers

Flood Inundation Mapping

Landsat 7 SPOT

Output / Decision Support SystemData

RFE

PET

Soil

LU/LC

DEM

QPF

Stream Flow Model

Water BalanceWater Balance

Lumped Lumped RoutingRouting

Dist. RoutingDist. Routing

Updating

1 Stream Flow Model DiagramStream Flow Model Stream Flow Model DiagramDiagram

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1 185 369 553 737 921 1105 1289 1473 1657 1841 2025 2209 2393 2577 2761 2945 3129 3313 3497 3681 3865 4049 4233 4417 4601 4785 4969

Q_model

Q_obs

Comparaison avant optimisation des débits observés et modélisés à Garbe Kourou sur la SIRBA de 1964 à 1978

ApplicatrionApplicatrion of of GeoSFMGeoSFM to the basin of SIRBA to the basin of SIRBA

(Burkina et Niger)(Burkina et Niger)

1

MODELISATION HYDROLOGIQUE

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IMPORTANCE DE LA PLUIE COMME VARIABLE D’ENTREE SUR LA REPONSE HYDROLOGIQUE

Pluie variable déterminante

Pluie moyenne sur le bassin ou par sous-bassin (global et sémi-distribué)

Pluie sur forme de grille (cas des modèles distribués)

Toute erreur sur la pluie aura un impact important sur le débit en sortie du modèle

Pluie variable déterminante

Réseau de pluviomètres

sont généralement de faible densiténécessité de spatialiser la pluie avec quelle méthode?

Estimation par télédétection

Avec quelle erreur?