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.

An assessment of climate impacts in the hydrologic system of the Blue Nile basin is useful for enhancing water management planning and basin-wide policymaking. Climate change adaptation activities predominantly require an understanding of the range of impacts on the water resource. In this study, we assessed climate change impacts on the Blue Nile River using 30-year in situ climate data (1981–2010) and five bias-corrected General Circulation Models (GCMs) for future (2026–2045) climate projections of RCP8.5. Both historical and GCM precipitation projections show inter-annual and spatial variability, with the most significant increases in the rainy season and a significant decrease in the dry season. The results suggest the probability of an increase in total precipitation. The intensity and frequency of future extreme rainfall events will also increase. Moreover, the hydrological model simulation results show a likely increase in total river flow, peak discharges, flood inundation, and evapotranspiration that will lead to a higher risk of floods and droughts in the future. These results suggest that the operation of water storage systems (e.g., the Grand Ethiopian Renaissance Dam) should be optimized for Disaster Risk Reduction (DRR) and irrigation management in addition to their intended purposes in the Nile basin.

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.

The development of data and model integration platforms has furthered scientific inquiry and helped to solve pressing social and environmental problems. While several e-infrastructure platforms have been developed, the concept of data and model integration remains obscure, and these platforms have produced few firm results. This article investigates data and model integration on the Data Integration and Analysis System (DIAS) platform, using three case projects from water-related fields. We provide concrete examples of data and model integration by analyzing the data transfer and analysis process, and demonstrate what platform functions are needed to promote the advantages of data and model integration. In addition, we introduce the Digital Object Identifier (DOI), a valuable tool for promoting data and model integration and open science. Our investigation reveals that DIAS advances data and model integration in five main ways: it is a "sophisticated and robust integration platform"; has "rich APIs, including a metadata management system, for high-quality data archive and utilization"; functions as a "core hydrological model"; and promotes a "collaborative R&D community" and "open science and data repositories". This article will appeal especially to researchers interested in new methods of analysis, and information technology experts responsible for developing e-infrastructure systems to support environmental and scientific research.

The Sangu River basin significantly contributes to national economy significantly; however, exposures to water-related hazards are frequent. As it is expected that water-related disasters will increase manifold in the future due to global warming, the Government of Bangladesh has formulated the Bangladesh Delta Plan 2100 (BDP-2100) to enhanced climate resilience. Accordingly, this study assessed the hydro-meteorological characteristics of the Sangu River basin under the changing climate. This study scientifically selected five General Circulation Models (GCMs) to include the model climate sensitivity and statistically bias-corrected their outputs. The Water and Energy Budget-based Rainfall-Runoff-Inundation (WEB-RRI) model was used to simulate the hydrological responses of the basin. The analysis of five GCMs under the Representative Concentration Pathway (RCP8.5) revealed that all selected GCMs estimate a 2–13% increase in annual rainfall and a 3–12% increase in annual discharge in the near-future (2025–2050), whereas four GCMs project an 11–52% increase in annual rainfall and a 7–59% increase in annual discharge in the far-future (2075–2100). The projected more frequent and intense increased extreme rainfall and flood occurrences in the future indicate an increase in flood disaster risk, whereas increased meteorological and hydrological drought in the future reflects a scarcity of water during dry periods. The number of projected affected people shows an increasing trend due to the increased inundation in the future. However, an increasing trend of transpiration indicates agricultural productivity will increase in the future. Policymakers can utilize this evidence-based information to implement BDP-2100 and to reduce the disaster risks in the basin.

Understanding urbanization and its impact on flooding and flood risk is crucial to better manage flood risk in the future. This study analyzed land use/land cover changes and how urbanization would impact flooding and flood risk in Kathmandu Valley of Nepal, and assessed flood risk by integrating flood hazards based on hydrologic–hydraulic modeling with the Analytic Hierarchy Process-based Multi-Criteria Decision Analysis (AHP-MCDA) approach. Land cover maps for past years were generated using Landsat satellite images, and land use/land cover maps for future years were projected based on machine learning techniques. Flood simulations were conducted using a rainfall runoff inundation model with land cover maps for different flood scales to analyze the impact of urbanization and land cover changes on flood runoff, flood inundation extent, and flood inundation volume. Then, we comprehensively assessed flood risk by integrating hazard conditions simulated under different land cover conditions using a hydrologic–hydraulic model and the AHP-MCDA approach. The results showed that the flood inundation extent and the peak inundation volume for a 200-year flood may increase in the future by 10.66% and 15.04%, respectively, as a result of urbanization. The results also highlighted that urbanization may lead to an expansion of high-risk and very-high-risk areas in the future by 3.2% and 9.4%, respectively, indicating an increase in the valley’s flood vulnerability and greater severity of flood hazards.