Abstract
The performance of Satellite Rainfall Estimate (SRE) products applied to flood inundation modelling was tested for the Mundeni Aru River Basin in eastern Sri Lanka. Three SREs (PERSIANN, TRMM, and GSMaP) were tested, with the Rainfall-Runoff-Inundation (RRI) model used as the flood inundation model. All the SREs were found to be suitable for applying to the RRI model. The simulations created by applying the SREs were generally accurate, although there were some discrepancies in discharge due to differing precipitation volumes. The volumes of precipitation of the SREs tended to be smaller than those of the gauged data, but using a scale factor to correct this improved the simulations. In particular, the SRE, i.e., the GSMaP yielding the best simulation that correlated most closely with the flood inundation extent from the satellite data, was considered the most appropriate to apply to the model calculation. The application procedures and suggestions shown in this study could help authorities to make better-informed decisions when giving early flood warnings and making rapid flood forecasts, especially in areas where in-situ observations are limited.
Highlights
Experts consider that extreme weather events associated with climate change are becoming more severe worldwide
GSMaP-scale factor (SF) data were ‘more similar’ to those calculated with Gauged-R data, while the flood inundation extent calculated with PERSIANN-SF was less than that with the Gauged-R data (Figure 6; Tables 7 and 8)
Three datasets of Satellite Rainfall Estimate (SRE) (PERSIANN, Tropical Rainfall Measuring Mission (TRMM), and GSMaP) were applied to the RRI model and their ability to simulate flood inundation tested for a basin in eastern Sri Lanka
Summary
Experts consider that extreme weather events associated with climate change are becoming more severe worldwide. Water-related disasters, such as floods and droughts, are expected to increase. People in poverty in developing countries are likely to be exposed to extreme weather events. According to the International Disaster Database of the Center for Research on the Epidemiology of Disasters (EM-DAT; [1]), about 3 billion people globally were affected by floods and droughts between 1995 and 2014. There is concern that human livelihoods and food production will increasingly be affected by water-related disasters as climate change progresses. Remote sensing techniques, including satellite image analysis, have been employed to capture the extent of areas affected by water-related disasters. There are limitations to such methods; for example, satellite images are not always available due to the satellite’s orbital period or cloud cover
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