The Sudan Experience in Flood Forecasting and Early Warning

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Date
2015-12-28
Authors
Abdo, Gamal M.
Nasr, Ahmed E.
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Publisher
UOFK
Abstract
In the paper, the experience of Sudan in stream flow forecasting and early warning is highlighted. Over the past two decades, various models have been applied which are either rainfall runoff models, channel routing models or a combination of both. Examples of these models are the simple linear Model (SLM), Linear Perturbation Model (LPM), Soil Moisture Accounting and Routing Model (SMAR) and the SAMFIL which is a coupling of the Sacramento rainfall runoff model and a hydraulic routing model. A major difficulty experienced in flood forecasting in Sudan is getting the real time information needed for the model, particularly when the upper catchments lie beyond the Sudan’s territories. One model that has been found to be very effective in this respect, is the USGS Geospatial Stream Flow Model (SFM). This model has been successfully applied in Sudan using remotely sensed data and the widely available global data sets such as the Digital Elevation Model DEM, Cold Cloud Duration CCD and vegetation data .Based on the results of applying some of the above models in various catchments in Sudan, the paper provides an assessment of the overall performance of these models, their efficiency and the problems facing their application. Finally, the paper draws some conclusions and recommendations for improved early warning and proper flood management in Sudan
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Keywords
Hydrologic models, flood forecasting, early warning, remote sensing, flood management, River Nile
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