Small flood reservoirs are modelled to determine whether they can still provide water for farms and low-flows under uncertain forecasts. Though forecasts did affect how much water was available, reservoirs were still able to provide water for raising river levels and for crops. At the same time, they were able to ensure that flooding conditions were maintained. This shows that such flood reservoirs could be converted into water supply reservoirs to expand water resources in a warming world.

Hydrologic models help predict floods and droughts, but how we calibrate them changes what they get right. Across 47 model structures and 10 diverse catchments, we tested how 8 metrics shape the reproduction of 15 streamflow features, from runoff ratios to high and low flows. Metric choice often mattered more than model structure, yet no single metric is best for all flow conditions. We provide guidance on metric strengths and weaknesses to help match the calibration metric to the study purpose.

 

Satellite soil moisture is too coarse and ground measurements are too sparse to describe field conditions. Drone microwave sensing helps fill this gap, but mixed signals from vegetation and surface variability reduce accuracy. We combine drone microwave, optical, and thermal data in a Bayesian framework to improve soil moisture estimates and quantify uncertainty. Field tests in China show higher accuracy, lower bias, and highlight small-scale heterogeneity as a key source of uncertainty.

Water moves from land to air in a process called evapotranspiration, which affects weather, crops, and water supply. Using satellites and AI, we created a system that tracks this water movement every five minutes, day and night, even through clouds. This provides continuous insights that can help manage water, predict weather, and better understand the water cycle.

Extreme rainfall is expected to grow in magnitude with increasing temperature. We assess whether very rare extremes increase with temperature faster than moderate extremes, and we test methods to include this effect into a model to predict future extremes called TENAX. We find that this dependence on temperature is typically observed but including it in the model without prior information on its magnitude may lead to disproportionately large uncertainty.

Tracking land water storage helps examine extreme events and manage water resources. GRACE (Gravity Recovery and Climate Experiment) missions observe changes, but coarse resolution in space and time challenges effective management. New gravity mission called MAGIC (Mass Change And Geosciences International Constellation) can offers better accuracy. This study tested MAGIC’s potential to estimate precipitation using the SM2RAIN approach. Results show SM2RAIN works well with frequent, accurate data but deteriorates with noisy/sparse data, emphasizing the need for precise gravity missions.

The coupling between water use efficiency (WUE) and drought remains unclear. This study first incorporates the optimal drought timescale at which WUE responds to drought. The results show that optimal drought timescale has gradually lengthened, indicating a weakening WUE-drought coupling, mainly driven by CO₂ fertilization, with surface soil moisture as the most important hydrothermal factor. This study provides new insights into assessing WUE responses to drought.

We present a simple and practical method for sampling water vapor for stable isotope analysis. Tests conducted under various conditions demonstrate that using 250-mL glass bottles, a flow rate of 100 to 125 milliliters per minute, and storage for up to 24 h at a temperature of 20 to 25 °C yields the most stable results. The method is cost-effective, non-destructive, and suitable for remote locations.

Rainfall shifts in West Africa affects agricultural productivity, highlighting how much water is stored in the soil. Soil moisture from 2003 to 2019 was assessed using satellite, model, and in-situ data. The European Space Agency Climate Change Initiative Soil Moisture dataset best tracked local conditions, while the Gravity Recovery and Climate Experiment Follow-On (GRACE-/FO)-based Global Land Water Storage version 2.0 captured regional variability, improving understanding of root-zone water.

HydroBlocks model, was configured over Finland. Time series of snow water equivalent, soil temperature, soil water content and soil ice content were created. Observational data was used to evaluate the model performance. HydroBlocks can generally predict soil conditions in Finland with some uncertainty. The model performance is improved with using local soil data and local soil hydraulic parameters. In the future, the modelling outputs show potential to be used in environmental applications.

Flood forecasting systems depend on weather forecasts. However, weather forecasts always have an error when compared with historical observations. This causes flood predictions to become less accurate when switching from historical to forecast data. We tested artificial intelligence (AI) methods across 451 European river basins to address this challenge and found that using appropriate model design can turn this accuracy problem into something the system can learn to fix "on the fly".

We present a framework using observed rainfall and temperature to generate realistic storms and simulate street-scale flooding for present and future climates. It integrates temperature-based rainfall scaling, storm-frequency estimation, and urban flood modeling, demonstrated in Beijing to assess changes in regional storm and flood depth, timing, and flow velocity. The workflow is data-light, physically grounded, and transferable worldwide.

Modelling evapotranspiration is essential for understanding the water cycle. While irrigation is known to increase evapotranspiration, it is less known that it also modifies local weather, which can in turn partially reduce evapotranspiration. This latter phenomenon is overlooked in some land surface model configurations. This study investigates and quantifies the impact of this oversight, showing that land surface models overestimate evapotranspiration by about 25% for crops in irrigated areas.

Distributed simulations and geochemical data indicate that groundwater hysteresis dampens the elasticity of flowing stream networks. This effect is expected to become more important as intense rainfall events replace gradual snowmelt in a warmer climate.

Hydrological models combining physics with AI are becoming popular for predicting river flow, but are often unnecessarily complex. We tested these models across US river basins and found three key results: simpler designs perform equally well, extensive input data adds little value, and time-varying parameters do not represent actual physical processes. These results challenge assumptions that complexity improves predictions or understanding, arguing instead for simpler hybrid model development.