Investigations of Multi-Platform Data for Developing an Integrated Flood Information System in the Kalu River Basin, Sri Lanka

Abstract

Flood early warning systems (FEWS) are crucial for flood risk management; however, several catchments in the developing world are still far behind in all aspects of FEWS and thus, they encounter devastating damage recurrently due to limitations in data, knowledge, and technologies. This paper presents a catchment-scale integrated flood information system by incorporating present-day multi-platform data and technologies (e.g., ground and satellite rainfall observation, ensemble rainfall forecasts, and flood simulation) and evaluates their performance in a poorly gauged prototype basin (i.e., the Kalu River basin). Satellite rainfall products obtained in real time (GSMaP-NOW) and near-real time (GSMaP-NRT) can detect heavy rainfall events well and bias-corrected products can further improve rainfall estimations and flood simulations. Particularly, GSMaP-NRT, which outperformed GSMaP-NOW in both rainfall and discharge estimations, is suitable for near-real-time flood-related applications. Ensemble rainfall forecasts showed good performance in predicting alarming signals of heavy rainfall and peak flow with uncertainties in the amounts and timings of the events. Information derived from both satellite and ensemble forecasts on heavy rainfall, simulated flood signals, and their possible range of probabilities is promising and can help minimize the data gaps and improve the knowledge and technology of experts and policy-makers in poorly gauged basins.