We introduce a machine-learning framework to study spatial characteristics and drivers of regional floods in eastern China, using 38 years of flood peak data from a vast gauging network. Our analyses provide better understanding of contrasting flood behaviors by explicitly characterizing their spatial extents. This knowledge can help improve flood risk management.

This work aims to improve the understanding of precipitation patterns in High-mountain Asia, a crucial water source for around 1.9 billion people. Through a novel machine learning method, we generate high-resolution precipitation predictions, including the likelihoods of floods and droughts. Compared to state-of-the-art methods, our method is simpler to implement and more suitable for small datasets. The method also shows accuracy comparable to or better than existing benchmark datasets.

In this study, we identify shifts in groundwater fauna due to natural or human impacts over 2 decades. We find no overall temporal or large-scale trends in fauna or abiotic parameters. However, at a local level, six monitoring wells show shifting or fluctuating faunal parameters. Our findings indicate that changes in surface conditions should be assessed in line with hydrochemical parameters to better understand changes in groundwater fauna and to obtain reliable biomonitoring results.

The most mysterious part of the hydrological cycle is the infiltration of water into porous soil. In this process, water enters the soil, some of it is retained in the soil or evaporates, and the remaining water continues to move below and through the rock environment. The physical description of infiltration, specifically the dependence of the infiltration rate on the flow, shows very unusual features that are beyond the normal human experience. Our paper is devoted to their elucidation.

The goal is to remove the impact of meteorological drivers in order to uncover the unique landscape fingerprints of a catchment from streamflow data. Our results reveal an optimal two-feature summary for most catchments, with a third feature associated with aridity and intermittent flow that is needed for challenging cases. Baseflow index, aridity, and soil or vegetation attributes strongly correlate with learnt features, indicating their importance for streamflow prediction.

Our research explores how increased CO₂ levels affect water use efficiency in the Yellow River basin. Using updated climate models, we found that future climate change significantly impacts water use efficiency, leading to improved plant resilience against moderate droughts. These findings help predict how ecosystems might adapt to environmental changes, providing essential insights into ways of managing water resources under varying climate conditions.

For users of hydrological models, model suitability often hinges on how well simulated outputs match observed discharge. This study highlights the importance of including discharge observation uncertainty in hydrological model performance assessment. We highlight the need to account for this uncertainty in model comparisons and introduce a practical method suitable for any observational time series with available uncertainty estimates.

The proposed flood frequency model accounts for uncertainty in the perception threshold S and the starting date of the historical period. Using a 500-year-long case study, inclusion of historical floods reduces the uncertainty in flood quantiles, even when only the number of exceedances of S is known. Ignoring threshold uncertainty leads to underestimated flood quantile uncertainty. This underlines the value of using a comprehensive framework for uncertainty estimation.

Statistical models that explain global irrigation rely on location-referenced data. Traditionally, a system based on longitude and latitude lines is chosen. However, this introduces bias to the analysis due to the Earth's curvature. We propose using a system based on hexagonal grid cells that allows for distortion-free representation of the data. We show that this increases the model's accuracy by 28 % and identify biophysical and socioeconomic drivers of historical global irrigation expansion.

Estimating extreme river discharges at single stations is relatively simple. However, flooding is a spatial phenomenon as rivers are connected. We develop a statistical method to estimate extreme flows with global coverage, accounting for spatial dependence. Using our model, synthetic flood events are simulated with more information than the limited historical events. This event catalog can be used to produce spatially coherent flood depth maps for flood risk assessment.

This study examines how soil moisture impacts land–atmosphere interactions, crucial for understanding Earth's water and energy cycles. The study used two different soil moisture datasets from the SMAP satellite to measure how strongly soil moisture influences the atmosphere's ability to retain moisture (called coupling strength). Leveraging SMAP soil moisture data and integrating multiple atmospheric datasets, the study offers new insights into the dynamics of land–atmosphere coupling strength. 

In this paper, we investigate the impact of climatic and anthropogenic factors on future groundwater availability. The changes are simulated using hydrological and groundwater flow models. We find that future groundwater status is influenced more by anthropogenic factors than climatic factors. The results are beneficial for informing responsible parties in operational water management about achieving future (ground)water governance.