Flood management is an important topic worldwide. Precipitation is the most crucial factor in reducing flood-related risks and damages. However, its adequate quality and sufficient quantity are not met in many parts of the world. Currently, near real-time satellite precipitation products (NRT SPPs) have great potential to supplement the gauge rainfall. However, NRT SPPs have several biases that require corrections before application. As a result, this study investigated two statistical bias correction methods with different parameters for the NRT SPPs and evaluated the adequacy of its application in the Fuji River basin. We employed Global Satellite Mapping of Precipitation (GSMaP)-NRT and Integrated Multi-satellitE Retrievals for GPM (IMERG)-Early for NRT SPPs as well as BTOP model (Block-wise use of the TOPMODEL (Topographic-based hydrologic model)) for flood runoff simulation. The results showed that the corrected SPPs by the 10-day ratio based bias correction method are consistent with the gauge data at the watershed scale. Compared with the original SPPs, the corrected SPPs improved the flood discharge simulation considerably. GSMaP-NRT and IMERG-Early have the potential for hourly river-flow simulation on a basin or large scale after bias correction. These findings can provide references for the applications of NRT SPPs in other basins for flood monitoring and early warning applications. It is necessary to investigate the impact of number of ground observation and their distribution patterns on bias correction and hydrological simulation efficiency, which is the future direction of this study.

Climate change is increasingly sensed by nations vulnerable to water-related disasters, and governments are acting to mitigate disasters and achieve sustainable development. Uncertainties in General Circulation Models’ (GCM) rainfall projections and seamless long-term hydrological simulations incorporating warming effects are major scientific challenges in assessing climate change impacts at the basin scale. Therefore, the Data Integration and Analysis System (DIAS) of Japan and the Water Energy Budget-based Rainfall-Runoff-Inundation model (WEB-RRI) were utilized to develop an integrated approach, which was then applied to the Mahaweli River Basin (MRB) in Sri Lanka to investigate climate change impacts on its hydro-meteorological characteristics. The results for the Representative Concentration Pathway (RCP8.5) scenario from four selected GCMs showed that, with an average temperature increase of 1.1 °C over the 20 years in future (2026 to 2045), the basin will experience more extreme rainfall (increase ranging 204 to 476 mm/year) and intense flood disasters and receive sufficient water in the future climate (inflow increases will range between 11 m³/s to 57 m³/s). The socio-economic damage due to flood inundation will also increase in the future climate. However, qualitatively, the overall trend of model responses showed an increasing pattern in future meteorological droughts whereas there is uncertainty in hydrological droughts. Policymakers can utilize these results and react to implementing soft or hard countermeasures for future policymaking. The approach can be implemented for climate change impact assessment of hydro-meteorology in any other river basin worldwide.

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.