Hydrological models are useful for assessing the impact of landscape organization for effective mitigation strategies. However, using these models requires reducing uncertainties in their results, which can be achieved through model–data fusion. We integrate satellite surface moisture images into a water and pesticide transfer model. We compare three methods, studying their performance and exploring various scenarios. This study helps improve decision support in water quality management.

The summer of 2022 was extremely warm and dry in Europe, severely impacting water availability. We calculated water balance anomalies for 88 glacierized catchments in Switzerland, showing that glaciers played a crucial role in alleviating the drought situation by melting at record rates, partially compensating for the lack of rain and snowmelt. By comparing 2022 with past extreme years, we show that while glacier meltwater remains essential during droughts, its contribution is declining.

The availability of high-resolution rainfall data is limited. This study presents a method to generate hourly and 1 km gridded rainfall data for detailed hydrodynamic flood modelling purposes, using point measurements and thin-plate spline interpolation. The analysis shows that the proposed dataset outperforms other gridded datasets in representing spatial distributions and daily and hourly variations of rainfall. The data is suitable for any study where high-resolution rainfall data is needed.

Global reservoir construction has improved water supply, but its impact on evaporation losses remains uncertain. Our study examines how reservoir development affects water evaporation in the Loess Plateau of China. We find that evaporation loss has increased due to expanding surface water, particularly from thousands of small- and medium-sized reservoirs. This new finding underscores the need to consider previously overlooked evaporation in the hydrological cycle and future water management.

This study assesses how spatial patterns, temporal trends, and record length in hourly precipitation data affect annual maximum intensities estimated with stationary and non-stationary models across a climatically and topographically diverse region. Comparing five gridded datasets, we find consistent spatial patterns but notable differences in intensities, with non-stationary estimates generally slightly lower.

This study provides the first assessment of an European meteorological dataset (E-OBS) for hydrological applications. We compared the dataset to meteorological datasets developed at a country level, and tested how the different data influenced the simulation of streamflow with hydrological model. Our findings show that, despite some limitations, the European dataset offers a reasonable basis for hydrological modelling in most river catchments across Europe.

In recent years, global and regional weather datasets have emerged, but validation with real-world data is crucial, especially in diverse regions like Brazil. This study compares seven key weather variables from five datasets with measurements from 11 sites across Brazil’s main biomes. Results show varying performance across variables and timescales, with one reanalysis product outperforming others overall. Findings suggest it may be a strong choice for multi-variable studies in Brazil.

Our study investigates global dryland dynamics and aridification under future climate scenarios. By employing the Food and Agriculture Organisation Aridity Index and an ensemble of 13 models from the 6th Coupled Model Intercomparison Project, we provide projections for dryland distribution and aridity index across three shared socio-economic pathways (2-4.5, 3-7.0, and 5-8.5) for the near-term (2030–2059) and for the long-term (2070–2099) future.

River depth is crucial in flood modelling, yet often unavailable. Estimation methods can fill this gap but have errors that can affect flood modelling outputs. Our study quantified flood-prediction uncertainty due to these errors. Between Uniform Flow and Conceptual Multivariate Regression formulas, river depths from the former have higher uncertainty than the latter. Among parameters used in these equations, river width corresponds to the largest uncertainty, followed by the flow and slope.

We propose a multi-machine learning ensemble—integrating Gradient Boosting Machine, K-Nearest Neighbors, and Extremely Randomized Trees (GBM-KNN-ERT)—to improve Topography-Based Subsurface Storm Flow (Top-SSF) parameter regionalization for flood prediction in ungauged catchments. Validated across 80 Chinese catchments, the ensemble achieved a Nash-Sutcliffe Efficiency (NSE) greater than 0.9 for 90 % of catchments, showing superior robustness to climate and donor variability.

Hydrological simulation requires parameter calibration. This study quantifies uncertainties in future runoff and drought using the Soil and Water Assessment Tool, twenty general circulation models, and shared socioeconomic pathway scenarios. Results show calibration conditions significantly impact low-flow projections. While climate models dominate uncertainty, calibration choices contribute notably, highlighting the need for robust strategies in water resource planning.