Enhancing transboundary flood forecasting for early warnings in the Gash River Basin

Historically, flooding is the most common environmental hazard worldwide, and also one of the most threatening to communities. Hydrological modeling of large river catchments has become a challenging task for water resources engineers due to the complexity of collecting and handling both spatial and nonspatial data, such as rainfall, gauge-discharge data, and topographic and hydraulic parameters. The Gash is a transboundary river which originates from the Eritrean Highlands and Ethiopian Plateau and ends up in Sudan. It is unique in its discharge flows with torrential rain between Jul. and Oct. while being dry for the rest of the year. Despite this characteristic, the river is the main source of water for domestic and agricultural use in Kassala City, Sudan. In this chapter we briefly present the potential application of satellite-based rainfall estimates and develop a flood forecasting model for the Gash River Basin, Sudan, through a distributed modeling approach using remote sensing data. The approach includes rainfall-runoff modeling, hydrodynamic flow routing, and calibration and validation of the model with field discharge data. The study area is divided into 25 subbasins to improve model accuracy. To generate relevant parameters for modeling, GlobCover land cover data (1000 m), Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) at 90 m, and the Food and Agriculture Organization of the United Nations (FAO) soil grid data using freely available datasets were used for the Gash River in Eastern Sudan. Based on several studies in Eastern Africa on the choice of satellite-based rainfall estimates, Tropical Rainfall Measuring Mission (TRMM) was used to represent the actual rainfall pattern and intensity of the basin. Model simulations were carried out using the HEC-HMS model. From 6 years (2007–12) of available discharge data for five stations, the period 2008–11 was considered for calibration with 2008 as the warming-up period, and data from 2007 and 2012 were used for validation. The model was tested during the 2013 floods at real-time, 3-h intervals. The accuracy of the estimated peak flood discharge and lag time was found to be good with reference to field observation data. Flood forecasting lead time is increased by 12 h compared to conventional methods of forecasting.