Risk management has reduced vulnerability to floods and droughts globally^1,2, yet their impacts are still increasing³. An improved understanding of the causes of changing impacts is therefore needed, but has been hampered by a lack of empirical data^4,5. On the basis of a global dataset of 45 pairs of events that occurred within the same area, we show that risk management generally reduces the impacts of floods and droughts but faces difficulties in reducing the impacts of unprecedented events of a magnitude not previously experienced. If the second event was much more hazardous than the first, its impact was almost always higher. This is because management was not designed to deal with such extreme events: for example, they exceeded the design levels of levees and reservoirs. In two success stories, the impact of the second, more hazardous, event was lower, as a result of improved risk management governance and high investment in integrated management. The observed difficulty of managing unprecedented events is alarming, given that more extreme hydrological events are projected owing to climate change³.

For effective management of water quantity and quality, it is absolutely essential to estimate the pollution level of the existing surface water. This case study aims to evaluate the performance of twelve machine learning (ML) models, including five boosting-based algorithms (adaptive boosting, gradient boosting, histogram-based gradient boosting, light gradient boosting, and extreme gradient boosting), three decision tree-based algorithms (decision tree, extra trees, and random forest), and four ANN-based algorithms (multilayer perceptron, radial basis function, deep feed-forward neural network, and convolutional neural network), in estimating the surface water quality of the La Buong River in Vietnam. Water quality data at four monitoring stations alongside the La Buong River for the period 2010–2017 were utilized to calculate the water quality index (WQI). Prediction performance of the ML models was evaluated by using two efficiency statistics (i.e., R² and RMSE). The results indicated that all twelve ML models have good performance in predicting the WQI but that extreme gradient boosting (XGBoost) has the best performance with the highest accuracy (R² = 0.989 and RMSE = 0.107). The findings strengthen the argument that ML models, especially XGBoost, may be employed for WQI prediction with a high level of accuracy, which will further improve water quality management.

Climate change (CC) is likely to have a long-term influence on regional water resources, including surface water and groundwater. Therefore, quantifying the CC influence is indispensable for proper management of water resources. This study scrutinized the influence of CC on river discharge and groundwater recharge (GWR) in Ho Chi Minh City (HCMC), Vietnam, utilizing the Soil and Water Assessment Tool (SWAT). The calibrated SWAT was utilized to simulate the discharge and GWR under projected climate scenarios in reliance on an ensemble of seven General Circulation Models (GCMs) derived from Coupled Model Intercomparison Project Phase 6 (CMIP6) under three Shared Socioeconomic Pathways (SSPs), including SSP1-2.6, SSP2-4.5, and SSP5-8.5. Results pointed out that the climate of HCMC is warmer and wetter in the 21st century. Under the CC influence, the future discharge is envisaged to rise from 0.1 to 4.5% during the near-future period of 2030s (2021–2045), 8.1 to 11.6% during the mid-future period of 2055s (2046–2070), and 7.7 to 19.6% during the far-future period of 2080s (2071–2095) under the three SSP scenarios. In addition, the GWR is prognosticated to have rising trends of 0.9–4.9%, 5.3–7.9%, and 5.7– 13.5% during the near-future, mid-future, and far-future periods, respectively. Furthermore, uncertainties in the discharge and GWR projections connected with SSP scenarios and CMIP6 GCMs are considerable.

Rainfall data with an appropriate spatial resolution is a key input to hydrological models. However, networks of rain gauges are often sparsely and unevenly distributed in large catchments, especially in developing countries. High-resolution rainfall datasets, such as the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), the Climate Forecast System Reanalysis (CFSR), the Climatic Research Unit Time Series (CRU-TS), the Global Precipitation Climatology Centre (GPCC) and the Tropical Rainfall Measuring Mission (TRMM), have become available to overcome such limitations. The objective of this study was to evaluate the impacts of four land-based rainfall products (APHRODITE, CFSR, CRU-TS, and GPCC) and a satellite-based rainfall product (TRMM) on streamflow of the upper catchment of Tri An reservoir in Vietnam using the Hydrological Modeling System (HEC-HMS). In addition, the available rain gauges data were used for comparison purpose. Result indicates that the TRMM and GPCC data show their best match to rain gauges data in simulating the streamflow in the period 1999–2007. Generally, the results indicate that the TRMM and GPCC data could be alternative solutions.

The effects of climate and land-use changes have put intense pressures on water resources with regard to water quantity and quality in the La Buong River Basin, located in Southern Vietnam. Therefore, an estimate of such effects and their consequences on water resources in this area is needed. The aim of this study is to evaluate the segregated and aggregated effects of climate change and land-use change on streamflow and water quality components (sediment and nutrient loads) using the well-known Soils and Water Assessment Tool (SWAT). The SWAT model was carefully calibrated and validated against the observation data before it can be used as a simulation tool to study the impacts of climate and land-use changes on hydrological processes. As a result of this study, it shows a reduction in the wet-season and annual streamflow, and sediment and nutrient loads will be occurred in the study area due to climate change effects, while the streamflow, and sediment and nutrient loads will be increased under the effects of the land-use change. Moreover, the streamflow and water quality components are more sensitive to land-use change than climate change. The results obtained from this study can provide a basic knowledge of the effects of climate and land-use changes on the streamflow and water quality to the local and national authorities for the future development of integrated water resources management in the La Buong River Basin.