Accurate knowledge of the spatial distribution of snow masses across landscapes is important for water management in mountain catchments. We present a new tool for estimating snow water resources without ground measurements. We evaluate the output of this tool using accurate airborne measurements in the Sierra Nevada and find that it provides realistic estimates of snow mass and snow depth at the catchment scale.

Our study explores how ecosystems recover after flash droughts. Using vegetation and soil moisture data, we found that recovery takes about 37.5 d on average (longer in central and southern regions) in China. Factors like post-drought radiation and temperature affect recovery, with extreme temperatures prolonging it. Herbaceous plants recover faster than forests. Our findings aid water resource management and drought monitoring on a large scale, offering insights into ecosystem resilience.

Water budget non-closure is a widespread phenomenon among multisource datasets which undermines the robustness of hydrological inferences. This study proposes a Multisource Dataset Correction Framework grounded in Physical Hydrological Process Modelling to enhance water budget closure, termed PHPM-MDCF. We examined the efficiency and robustness of the framework using the CAMELS dataset and achieved an average reduction of 49 % in total water budget residuals across 475 CONUS basins.

This study estimated the water balance of Lake Titicaca using an integrated modeling framework that considers natural hydrological processes and net irrigation consumption. The proposed approach was implemented at a daily scale for a period of 35 years. This framework is able to simulate lake water levels with good accuracy over a wide range of hydroclimatic conditions. The findings demonstrate that a simple representation of hydrological processes is suitable for use in poorly gauged regions.

This work investigates how hydrological models are transferred to a period in which climate conditions are different to the ones of the period in which they were set up. The robustness assessment test built to detect dependencies between model error and climatic drivers was applied to three hydrological models in 352 catchments in Denmark, France and Sweden. Potential issues are seen in a significant number of catchments for the models, even though the catchments differ for each model.

Warming in montane systems is affecting the snowmelt input amount. At the global scale, this will impact subalpine forests that rely on spring snowmelt to support their water demands. We use a network of sensors across a hillslope in the Upper Colorado Basin to show that the changing spring snowpack has a more pronounced impact on dense forest stands, while open stands show a higher reliance on summer rain and are less sensitive to significant changes in snow.

The Standardized Precipitation Index (SPI) and related drought indices are used globally to measure drought severity. The index uses a predictable structure, which we leverage to determine the theoretical likelihood of a year with an extreme worse than a given threshold. We show these likelihoods differ by the length (number of months) and resolution (daily vs. monthly) of the index. This is important for drought managers when setting decision thresholds or when communicating risk to the public.

Glacial lake outburst floods (GLOFs) can cause serious damage. To assess their risks, we developed an innovative framework using remote sensing, Bayesian models, flood modelling, and open-source data. This enables us to evaluate GLOFs on a national scale, despite limited data and challenges accessing high-altitude lakes. We evaluated dangerous lakes in Nepal, identifying those most at risk. This work is crucial for understanding GLOF risks, and the framework can be transferred to other areas.

This study presents the first distributed hydrological simulation which confirms claims raised by historians that the eastward diversion project of the Tone River in Japan was conducted 4 centuries ago to increase low flows and subsequent travelling possibilities surrounding the capital, Edo (Tokyo), using inland navigation. We showed that great steps forward can be made for improving quality of life with small human engineering waterworks and small interventions in the regime of natural flows.

We create China’s first open county-level urban flood dataset (2000–2022) using news media data with the help of deep learning.  The dataset reflects both natural and societal influences and includes 7595 urban flood events across 2051 counties, covering 46 % of China’s land area. It reveals the predominance of summer floods, an upward trend since 2000, and a decline from southeast to northwest. Notably, some highly developed regions show a decrease, likely due to improved flood management.

Improving the accuracy of flood forecasts is paramount to minimising flood damage. Machine learning (ML) models are increasingly being applied for flood forecasting. Such models are typically trained on large historic hydrometeorological datasets. In this work, we evaluate methods for selecting training datasets that maximise the spatio-temporal diversity of the represented hydrological processes. Empirical results showcase the importance of hydrological diversity in training ML models.

We devised an adaptive method for calibrating remote sensing precipitation in the South Pacific. By classifying data into weather types and applying varied techniques, we achieve improved calibration. Results showed enhanced accuracy in mean and extreme precipitation indices across locations. The method offers customization options and effectively addresses intense rainfall events. Its versatility allows for application in diverse scenarios, supporting a better understanding of climate impacts.