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Developing a Flood Monitoring System From Remotely Sensed Data for the Limpopo Basin

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5 Author(s)
Kwabena O. Asante ; Sci. Applications Int. Corp., U.S. Geol. Survey Center for Earth Resources Obs. & Sci., Sioux Falls, SD ; Rodrigues D. Macuacua ; Guleid A. Artan ; Ronald W. Lietzow
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This paper describes the application of remotely sensed precipitation to the monitoring of floods in a region that regularly experiences extreme precipitation and flood events, often associated with cyclonic systems. Precipitation data, which are derived from spaceborne radar aboard the National Aeronautics and Space Administration's Tropical Rainfall Measuring Mission and from National Oceanic and Atmospheric Administration's infrared-based products, are used to monitor areas experiencing extreme precipitation events that are defined as exceedance of a daily mean areal average value of 50 mm over a catchment. The remotely sensed precipitation data are also ingested into a hydrologic model that is parameterized using spatially distributed elevation, soil, and land cover data sets that are available globally from remote sensing and in situ sources. The resulting streamflow is classified as an extreme flood event when flow anomalies exceed 1.5 standard deviations above the short-term mean. In an application in the Limpopo basin, it is demonstrated that the use of satellite-derived precipitation allows for the identification of extreme precipitation and flood events, both in terms of relative intensity and spatial extent. The system is used by water authorities in Mozambique to proactively initiate independent flood hazard verification before generating flood warnings. The system also serves as a supplementary information source when in situ gauging systems are disrupted. This paper concludes that remotely sensed precipitation and derived products greatly enhance the ability of water managers in the Limpopo basin to monitor extreme flood events and provide at-risk communities with early warning information

Published in:

IEEE Transactions on Geoscience and Remote Sensing  (Volume:45 ,  Issue: 6 )