Performance Assessment of High-Resolution Remote Sensing Rainfall Products and their utility in simulating Extreme Hydrological Events in a Peri-urban Catchment

Abstract

Abstract Flood risk management studies require reliable estimates of extreme precipitation at high spatial-temporal distribution to force hydrologic models. Recently, Remote Sensing Rainfall Products (RRPs) have gained significant importance in the field of hydrometeorology, but their applicability in urban hydrologic predictions remains uncertain. The current study evaluates the accuracy of RRPs in comparison with observed rainfall and the significance of space-time representation of rain in simulating single and bimodal flood hydrographs. The current study is conducted for the Adyar river basin, a rapidly developing urban area in Chennai experiencing frequent floods. Sub-daily rainfall retrievals from three different satellites and Doppler Weather Radar (DWR) are the Remote sensing Rainfall products (RRPs) selected in the present study. Continuous and categorical statistical indices are selected to evaluate the performance of satellite rainfall estimates. Then the hydrologic utility of RRPs is conducted using the HEC-HMS model for five extreme precipitation events. The RRPs simulated the rising and recession portion of flood hydrographs accurately with a bias in peak discharge. Then, two approaches are selected to further improve the flood hydrograph simulations in the current study; 1) Hydrologic model simulations after disaggregating the daily station data to sub-daily scale using time characteristics of RRPs, 2) Hydrologic simulations after bias adjusting the RRPs with station data. We found substantial improvements in model results in the two approaches. The disaggregation approach using satellite rainfall estimates has overcome the insufficiency of sub-daily rainfall observations. The bias adjusted radar rainfall data is found as best performing for the flood hydrograph simulations.