Understanding riverine wetland-catchment processes using remote sensing data and modelling
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1 Understanding riverine wetland-catchment processes using remote sensing 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 Education Department of Hydroinformatics and Knowledge Management
2 Catchment modelling Hill slope hydrology Inclusion of agricultural processes
3 Water quality: from river reach to river basin Hill slope hydrology Inclusion of agricultural aspects Importance of natural landscape elements Wetland processes - Water retention - Sediment/Nutrient trapping - Anaerobic processes - Groundwater recharge - Habitats
4 How to represent wetlandcatchment hydrology? What is the value of remote sensing data? How to model wetlands at catchment scale?
5 How to represent wetlandcatchment hydrology? What is the value of remote sensing data? How to model wetlands at catchment scale?
6 Kagera basin Study area
7 Remote sensing data GIS/RS Data DEM: Watershed delineation
8 Remote sensing data GIS/RS Data DEM: wetland delineation
9 GIS/RS Data DEM: wetland delineation
10 Remote sensing data
11 Remote sensing data GIS/RS Data Soil data } Usefull Land use data for catchment modelling Not well representing wetlands
12 Remote sensing data GIS/RS Data: soil water. Soil water for the entire Africa January Kagera Basin Area. TUNISIA UGANDA MOROCCO ALGERIA LIBYA EGYPT GAZA STRIP, THE KENYA WESTERN SAHARA MAURITANIA CAPE VERDE CAPE VERDE CAPE VERDE MALI SENEGAL GAMBIA, THE GUINEA-BISSAU BURKINA FASO GUINEA BENIN SIERRA LEONE TOGO IVORY COAST, THEGHANA LIBERIA NIGER CHAD SUDAN NIGERIA CENTRAL AFRICAN REPUBLIC CAMEROON ERITREA DJIBOUTI SOMALIA ETHIOPIA RWANDA EQUATORIAL GUINEA SAO TOME AND PRINCIPE GABONCONGO, THE ZAIRE UGANDA RWANDA BURUNDI KENYA ZAIRE TANZANIA BURUNDI ANGOLA MALAWI COMOROS, THE COMOROS, THE ZAMBIA MOZAMBIQUE ZIMBABWE MADAGASCAR Legend africa_adm0 TANZANIA Legend africa_adm0 January % Soil water NAMIBIA BOTSWANA SOUTH AFRICA LESOTHO SWAZILAND Kilometers ,700 3,400 5,100 6,800 Riv2 watsub2 January % Soil water km x 25 km University of Amsterdam Kilomete
13 Remote sensing data GIS/RS Data: soil water Wetland identification? Spatial resolution too low
14 Remote sensing data GIS/RS Data: soil water Wetland identification? Spatial resolution too low 120 Daily soil water Dail soil for 2 water Nyabarongo in Nyabarongo grids agricultural wetland % Jan-05 1-Mar-05 1-May-05 1-Jul-05 1-Sep-05 1-Nov-05
15 Remote sensing data Rainfall data
16 How to represent wetlandcatchment hydrology? What is the value of remote sensing data? How to model wetlands at catchment scale?
17 Wetland-catchment Modelling Soil and Water Assessment Tool Simulation of processes at land and water phase Spatially distributed (different scales) Semi physically based / empirical approaches Simulation of changes (climate, land use, management etc.) Water quantities, incl. different runoff components Water 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!!
18 Wetland-catchment Modelling Wetland representation Ponds or wetlands As reservoirs As irrigated potholes
19 Wetland-catchment Modelling New land use map Slope (DEM) Google earth
20 Wetland-catchment Modelling SWAT model Wetlands located in the south part of Rwanda, and north of Burundi, eastern part of Rwanda and West of Tanzania Total number of Subbasin is 35, and HRU number is Wetlands delineation, including Lakes and Reservoirs!( # # 30 #!( # 20 #!( 21 # # 34 # # #!( # Kilometers !( !( # # # 15 # # 12 Legend!( 3 # # # 23 # 10 # ## 8 11 { Lakes and reservoirs locations Reservoirs location # Linking stream added Outlet # Manually added Point Source Kagera river Network Subbasins number Wetelands areas 1 9
21 Wetland-catchment Modelling Preliminary results Surface Q Assessment for wetland modeling comparison Groundwater (GW) Assessment for wetland modeling Value (mm) , ,26 70, Different scenarios for modeling w etlands (Scenarios) Surface Q (No Wetlands) Surface Q (As ponds) Surface Q (As Wetlands) Surface Q (As Reservoirs) Surface Q (As Potholes) Value (mm) , ,8971, (Scenarios GW) Different scenarios for modeling wetlands Groundwater (Gw): No Wetlands Groundwater (Gw): As Ponds Groundwater (Gw):As Wetlands Groundwater (Gw): As Reservoirs Groundwater (Gw): As Potholes Water Yield (YLD) Assessment for wetland modeling comparison. Average flow Assessment for wetland modeling comparison Values (mm) ,34 169,34170, Differents Scenarios for modeling w etlands (Scenarios) 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 flow (cms) ,9 191,2 66,19 94, Different scenarios for modeling wetlands (Scenarios) Average flow : No Wetlands Average flow : As Ponds Average flow : As Wetlands Average flow : As Reservoirs Average flow : As Potholes
22 Conclusions Conclusion (1): RS Data Data DEM Land use map Soil map Soil water (25 km x 25 km) Satellite rainfall data Catchment YES YES YES YES/NO? YES/NO Wetland YES (slopes) NO NO NO -
23 Conclusion Conclusion (2): modelling Many different types of wetlands Different modeling concepts Need to link concept-type
24
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