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Flood Mapping Services and Development of Flood Forecasting Tool (Gash Catchment, Sudan)

Giriraj Amarnath1, Niranga Alahacoon1, Bharat Sharma2, Gijs Simons3, Younis Gismalla4, Yasir Mohammed4, Vladimir Smakhtin1

1International Water Management Institute (IWMI), Sri Lanka;2IWMI, India; 3eLEAF Competence Center, The Netherlands4Hydraulic Research Station (HRS) Sudan

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WORK PACKAGE: FLOOD COMPONENT

• To provide flood inundation mapping services for the 2 flood seasons (i.e. 2012 & 2013)

• To develop operational flood forecasting tool using hydrological modeling system for the upstream Gash catchment

• To provide detail training on the use of satellite data and modeling system on studies related to flood irrigation.

FLOOD INUNDATION MAPPING ALGORITHM

• MODIS surface reflectance• Temporal resolution : Daily & 8 days• Spatial resolution – 250-500 m • Period : 2012 and 2013• Indices : EVI, NDWI, LSWI, NDSI• DVEL (EVI-LSWI) was used to

discriminate between Water pixels and Non–water pixels. If the smoothed DVEL is less than 0.05 pixel is assumed to be a Water pixel;

• Several procedure further differentiate between permanent water bodies and temporary Flood pixels

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Flood Inundation Products

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IDENTIFYING FARM-LEVEL FLOOD EXTENT USING GIS

• Analysis shows some farmers received excess water and this resulted in decrease in crop productivity (field site evaluated)

• Vice-versa some farms received 1 or 2 weeks, resulted in low biomass

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OPERATIONAL FLOOD INUNDATION MAPPING(MODIS + Landsat Images)

• Weekly inundation mapping services• High. Res. Flood maps from Landsat• Fieldlook Dissemination

SEASONAL FLOOD EXTENT IN GASH IRRIGATION SCHEME

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Development of Flood Forecasting System using HEC-HMS + Satellite Rainfall Estimates

Basin Characteristics25 sub-basinWatershed ~20,000km2

12 river segments

HMS ParametersLoss (SCS Curve Number)Transform (SCS Unit Hydrograph)Baseflow (Constant Monthly)Routing (Muskingum)

Model Inputs5 raingauges (Ethiopia)El Gera flow data (GRTU)TRMM, RFE, CMORPH SRE DataDEM, LULC, FAO Soil Data

SRTM DEM

HEC-GeoHMSSlope, watershed and

flow direction developed

Hydrological modeling

HEC-HMSRainfall:• Meteo. Stations• Satellite estimates• GCM CCAFS Data

Interaction between HEC-RAS and HEC-HMS to get

outflow relationship

Peak Flows

Land UseLand Classification

Data

Hydraulic structures inputted into

Drainage System Geometry

HEC-RAS

HEC-GeoRASDrainage network

characterized

SRTM DEM

TIN

Finalized Geometry

HEC-RASHydraulicModeling

HEC-GeoRAS

Flood Inundation Extent

Stream CenterlineBanksFlowpathsCross sections

Hydraulic Structure Data

Current | Future

Development of Flood Forecasting System using HEC Tools

*HEC-RAS support from HRC (Wad Medani) & M.Sc Students internship

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BIAS-CORRECTION OF SATELLITE RAINFALL ESTIMATES

• Satellite-based rainfall estimates (SREs) provide an alternative source of rainfall data for hydrological modeling

• Real-time SREs are highly suitable for water resource modeling. however an over or underestimation in the actual rainfall amount could lead to uncertainties in volume error

• In the case of Gash catchment, significant proportion of the watershed is in Ethiopia and Eretria has very few, if any, weather recording stations.

• This is a prime example of where SREs may be of benefit for filling in gaps in a ground based data acquisition network.

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BIAS-CORRECTION OF SATELLITE RAINFALL ESTIMATES

• Incorporated distribution transformation model– Correction factor for mean:

Where µf is the correction factor for the mean, µobs is the average value of OBS for all reporting stations and µSRE is average SRE for all reporting stations.

– Secondly, correction factor for the variation is determined by the quotient of the standard

deviations.

Once the correction factors are known they are used to correct all pixels in SRE image using the following equation.

– Where SREc is the corrected SRE and SRE0 is the uncorrected SRE.

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BIAS-CORRECTION OF SATELLITE RAINFALL ESTIMATES

Daily precipitation sum for 2009 for the uncorrected and corrected SRE (distribution method)

Improved SRE with R2 value of 0.68 was achieved;

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Model Calibration and Validation : Kassala Bridge Station

NSE RE% YCalibration 2011 0.79 -0.05 0.94Validation 2007 0.72 -0.17 0.87

2012 0.71 -0.06 0.65

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Simulated Discharge Observed Discharge

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Simulated Discharge Observed Discharge

Calibration : 2011 Validation : 2007

Validation : 2012

Station: Kassala Bridge (Gash Catchment)

Observed vs. Simulated Discharge

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Development of Flood Forecasting System using HEC-HMS

• Model calibration from 2011• 2007, 2010 and 2012 for validation• Model performance in estimating peak flood discharge and

lag time were reasonably good

• Catchment having no gauge stations• Used bias corrected satellite rainfall estimates• Abstraction + Ground water extraction• El Gera flow data measurement (float method + calculation)• Parameters can be improved further with local measurements

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Flood Extent and Flood Hydrograph – Gash Catchment

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Near-Real Time Flood Forecasting 2013 Flood Seasons

• Tested 2013 flood year using previous models• 3-hourly TRMM Precipitation data

1-D Hydrodynamic model for Gash Catchment

Modeling of water levels for different discharges

Modeling of flood extent using HEC-RAS with flow rate of 2000m3/Sec

133 cross section along the Gash River (field survey 2013)

USAGE OF SATELLITE ALTIMETRY IN FLOOD FORECAST RATING CURVE

- Today there are several altimetry satellite data e.g., ENVISAT, JASON-2, ICESAT, Sentinel etc. to provide elevation information

- - Example of altimeter derived

water level for Kassala bridge (Virtual station Track 528)

- Using altimeter and Manning equation and available cross section we can provide downstream water level as early warning tool

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MAJOR OUTPUTS

• Successfully implemented the potential of satellite data in operational flood inundation extent for 2012 and 2013 flood seasons

• Successfully tested operational flood forecasting tool for the 2013 flood seasons using hydrological modeling tools

• 2 Research publications in progress• Dissemination in various events

– World Irrigation Forum (Turkey, 2013)– Nile Blue Perspective Conference (Sudan, 2013)– India Geospatial Forum and ICT4D (India, Kenya - 2014)

• Capacity building programme:– Remote Sensing and GIS for flood inundation mapping (March 2013) in Wad

Medani (35 participants)– Development of flood forecasting tools for the Gash catchment (August 2013)

in Kassala (20 participants)

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http://www.guardian.co.uk/technology/2011/jul/24/mobile-phones-africa-microfinance-farming

Contact: Giriraj Amarnath, IWMI, Colomboa.giriraj@cgiar.org