Understanding riverine wetland-catchment processes using … · 4 20 34 10 30 8 Wetlands...

Understanding riverine wetlandUnderstanding riverine wetland--catchment processes using remote catchment processes using remote sensing data and modellingsensing data and modelling

Yunqing Xuan (UNESCO-IHE, NL)Didier Haguma (KIST, Rwanda)William Niyonzima (UNESCO-IHE, NL)

Ann van Griensven (UNESCO-IHE, NL)

UNESCO-IHE Institute for Water EducationDepartment of Hydroinformatics and Knowledge Management

Catchment modellingHill slope hydrologyInclusion of agricultural processes

Water quality: from river reach to river basinHill slope hydrologyInclusion of agricultural aspectsImportance of natural landscape elements

Wetland processes- Water retention- Sediment/Nutrient trapping- Anaerobic processes- Groundwater recharge- Habitats

How to represent wetland-catchment hydrology?

What is the value of remote sensing data?How to model wetlands at catchmentscale?

Kagera basin

Study area

GIS/RS DataDEM: Watershed delineationDEM: Watershed delineation

Remote sensing data

GIS/RS DataDEM: wetland delineationDEM: wetland delineation

Remote sensing data

GIS/RS DataDEM: wetland delineationDEM: wetland delineation

Remote sensing data

GIS/RS DataSoil dataSoil dataLand use dataLand use data } Usefull for catchment modelling

Not well representing wetlands

Remote sensing data

GIS/RS Data: soil water

ZAIRE

SUDAN

LIBYAALGERIA

MALI

CHAD

NIGER

EGYPT

ANGOLA

ETHIOPIANIGERIA

NAMIBIA

ZAMBIA

SOUTH AFRICA

TANZANIA

MAURITANIA

KENYA

SOMALIA

BOTSWANA

MOROCCO

CAMEROON

ZIMBABWE

GABON

GHANA

GUINEA

UGANDA

MOZAMBIQUE

MADAGASCAR

CONGO, THE

TUNISIA

SENEGAL

CENTRAL AFRICAN REPUBLIC

BENIN

BURKINA FASO

IVORY COAST, THE

WESTERN SAHARA

MALAWI

ERITREA

LIBERIA

TOGOSIERRA LEONE

LESOTHO

BURUNDIRWANDA

DJIBOUTIGUINEA-BISSAU

SWAZILAND

EQUATORIAL GUINEA

GAMBIA, THE

CAPE VERDE

COMOROS, THE

CAPE VERDECAPE VERDE

SAO TOME AND PRINCIPE

COMOROS, THE

GAZA STRIP, THE

Soil water for the entire Africa January-02-1995

0 1,700 3,400 5,100 6,800850Kilometers

Legendafrica_adm0

January-02-1995% Soil water

-20 - 0

0 - 10

10.00000001 - 16

16.00000001 - 23

23.00000001 - 29

29.00000001 - 35

35.00000001 - 43

43.00000001 - 55

55.00000001 - 71

71.00000001 - 99

.

TANZANIA

ZAIRE

UGANDA

BURUNDI

RWANDA

KENYA

Kagera Basin Area .

0 150 300 450 60075Kilomete

Legendafrica_adm0

Riv2

watsub2

January-02-1995% Soil water

-20 - 0

0 - 10

10.00000001 - 16

16.00000001 - 23

23.00000001 - 29

29.00000001 - 35

35.00000001 - 43

43.00000001 - 55

55.00000001 - 71

71.00000001 - 99

25 km x 25 km25 km x 25 kmUniversity of AmsterdamUniversity of Amsterdam

Remote sensing data

GIS/RS Data: soil waterWetland identification?Wetland identification?Spatial resolution too lowSpatial resolution too low

Remote sensing data

GIS/RS Data: soil waterWetland identification?Wetland identification?Spatial resolution too lowSpatial resolution too low

Dail soil water in Nyabarongo

0

20

40

60

80

100

120

1-Jan-05 1-Mar-05 1-May-05 1-Jul-05 1-Sep-05 1-Nov-05

%

agricultural wetland

Daily soil water for 2 Nyabarongo gridsDail soil water in Nyabarongo

0

20

40

60

80

100

120

1-Jan-05 1-Mar-05 1-May-05 1-Jul-05 1-Sep-05 1-Nov-05

%

agricultural wetland

Daily soil water for 2 Nyabarongo grids

Remote sensing data

Rainfall dataRemote sensing data

How to represent wetland-catchment hydrology?

What is the value of remote sensing data?How to model wetlands at catchmentscale?

Soil and Water Assessment ToolSimulation of processes at land and water phaseSpatially distributed (different scales)Semi physically based / empirical approachesSimulation of changes (climate, land use, management etc.)Water quantities, incl. different runoff componentsWater quality: Nutrients, Sediments, Pesticides, Bacteria, (algae and oxygen), etc.

…. all that on a daily time step and at different spatial scales and (more or less) readily available data sets!!

Wetland-catchment Modelling

Wetland representation

As reservoirs

As irrigated potholes

Ponds or wetlands


New land use mapSlope (DEM)Slope (DEM)Google earthGoogle earth


SWAT modelWetlands located in the south part of Rwanda, and north of Burundi, eastern part of Rwanda and West of TanzaniaTotal number of Subbasinis 35, and HRU number is 323

######

########

##

##

####

####

##

##

####

#

#

#

#

#

#

!(

!(

!(

!(

!(

!(

21

3

27

7

15

13

17

9

2 126

5

24

32

1

16

22

14

19

11

18

2623

28

2933

25

35

31

4

20

34

10

30

8

Wetlands delineation, including Lakes and Reservoirs

{

LegendLakes and reservoirs locations

!( Reservoirs location

# Linking stream added Outlet

# Manually added Point Source

Kagera river Network

Subbasins number

Wetelands areas0 25 50 75 10012.5

Kilometers


Preliminary results Surface Q Assessment for wetland modeling

comparison

70,26 70 70,26 70,26

122

0

20

40

60

80

100

120

140

Different scenarios for modelingw etlands

(Scenarios)

Valu

e (m

m)

Surface Q (NoWetlands)

Surface Q (As ponds)

Surface Q (AsWetlands)

Surface Q (AsReservoirs)

Surface Q (AsPotholes)

Water Yield (YLD) Assessment for wetland modeling comparison.

169,34

94

169,34170,82

376

050

100150200250300350400

Differents Scenarios for modelingw etlands

(Scenarios)

Valu

es (m

m) Total w ater Yield (YLD):

No Wetlands

Total w ater Yield (YLD):As Ponds

Total w ater Yield (YLD):As Wetlands

Total w ater Yield (YLD):As Reservoirs

Total w ater Yield (YLD):As potholes

Average flow Assessment for wetland modeling comparison

352,9

191,2

66,1994,41

176

050

100150200250300350400

Different scenarios for modelingwetlands

(Scenarios)

flow

(cm

s)

Average flow : NoWetlands

Average flow : AsPonds

Average flow : AsWetlandsAverage flow : AsReservoirs

Average flow : AsPotholes

Groundwater (GW) Assessment for wetland modeling

69,89 73 69,8971,37

219

0

50

100

150

200

250

(Scenarios GW)

Different scenarios for modeling wetlands

Valu

e (m

m)

Groundwater (Gw): NoWetlands

Groundwater (Gw): AsPonds

Groundwater (Gw):AsWetlands

Groundwater (Gw): AsReservoirs

Groundwater (Gw): AsPotholes


Conclusion (1): RS Data

-YES/NOSatellite rainfall data

NOYES/NO?Soil water (25 km x 25 km)

NOYESSoil map

NOYESLand use map

YES (slopes)YES DEM

WetlandCatchmentData

Conclusions

Conclusion (2): modelling

Many different types of wetlandsDifferent modeling conceptsNeed to link concept-type

Conclusion

Understanding riverine wetland-catchment processes using … · 4 20 34 10 30 8 Wetlands...

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