How drought impacts communities is complex and not yet fully understood. We examined a disaster dataset and compared various drought measures to pinpoint affected regions. Our new combined drought indicator (CDI) was found to be the most effective in identifying drought events compared to other traditional drought indices. This underscores the CDI's importance in evaluating drought risks and directing attention to the most impacted areas.

This extended review asks whether hydrological (river flow) droughts have become more severe over time in the UK based on literature review and original analyses. The UK is a good international exemplar, given the richness of available data. We find that there is little compelling evidence for a trend towards worsening river flow droughts, at odds with future climate change projections. We outline reasons for this discrepancy and make recommendations to guide researchers and policymakers.

Understanding the changes in water movement in earth is crucial for everyone. To quantify this water movement there are several techniques. We examined how different methods of estimating evaporation impact predictions of various types of water movement across Europe. We found that, while these methods generally agree on whether changes are increasing or decreasing, they differ in magnitude. This means selecting the right evaporation method is crucial for accurate predictions of water movement.

We studied how rivers and groundwater interact in an arid region of Northwest China, where water is scarce and vital for farming. By analyzing the chemistry and natural markers in water samples, we found that the connection between river water and groundwater changes from mountains to plains. These findings help explain how water moves through the region and offer guidance for managing water resources more sustainably.

The hybrid variational mode decomposition–recurrent neural network (VMD-RNN) model provides a reliable one-step-ahead prediction, with better performance in predicting high and low values than the pure long short-term memory (LSTM) model. The universal multifractal technique is also introduced to evaluate prediction performance, thus validating the usefulness and applicability of the hybrid model.

We used interpretable machine learning to evaluate the accuracy of two continental-scale hydrologic models. We analyzed a suite of catchment attributes and found that soil water content had the biggest impact on model performance, especially in dry areas. Key thresholds for variables like precipitation and road density were identified, which could guide future improvements in these models. Our findings highlight the potential of data-driven methods to inform process-based models.

Decreases in streamflow during dry periods threaten ecosystems and society, and increases in evapotranspiration may contribute to them. From data for small catchments in Germany, summer low flows decreased over 1970–2019, and evapotranspiration increases significantly contributed to that. Lower-than-expected annual streamflow occurred during the 1989–1993 drought in catchments with increases in evapotranspiration. Changes in evapotranspiration need full consideration for streamflow prediction.

Including lateral flow increases evapotranspiration near major river channels in high-resolution land surface simulations in southeast Australia, consistent with observations. The 1-km resolution model shows a widespread pattern of dry ridges that does not exist at coarser resolutions. Our results have implications for improved simulations of droughts and future water availability.

We present a new strategy to calibrate large-domain land/hydrology models over diverse regions. Using the Structure for Unifying Multiple Modeling Alternatives (SUMMA) and mizuRoute models, our approach integrates catchment attributes, parameters, and performance metrics to optimize streamflow simulations. Leveraging advances in machine learning for hydrology, we improve calibration and enable regionalization to ungauged basins, which is valuable for national-scale water security studies.

Unpiloted aerial system (UAS) lidar and structure-from-motion (SfM) photogrammetry effectively map high-resolution snow depths. Our study found that UAS lidar outperformed SfM, particularly in capturing stable snow distribution patterns. Vegetation type was the primary factor influencing snow depth across forest and field areas, reflecting soil variables, such as organic matter. When analyzed separately, slope and forest canopy shadowing played key roles.

This study focused on the sources of the Zambezi River, revealing new insights into its longest tributary and Angola's key role in its flow. River expeditions and earth observation data show that Angola contributed approximately 73 % of the river's flow upstream of a major floodplain during the 2023 late wet season. We highlight Angola's wetlands, which support river health and help mitigate pollution. These findings stress the need for improved monitoring and regional cooperation in the basin.

Rain gauge networks from personal weather stations (PWSs) have a network density 100 times higher than dedicated rain gauge networks in the Netherlands. However, PWSs are prone to several sources of error, as they are generally not installed and maintained according to international guidelines. This study systematically quantifies and describes the uncertainties arising from PWS rainfall estimates. In particular, the focus is on the highest rainfall accumulations.

Existing correction methods may introduce large errors, and more seriously cause unrealistic negative values in P, ET and Q in up to 10 % of cases. A novel IWE-Res method is proposed to improve the accuracy and consistency of corrected satellite-based water budget component data. In most river basins (except cold regions), the best correction is achieved by adjusting 40 % to 90 % of the total water imbalance error.

We investigate the skill of the Community Earth System Model version 1 (CESM1) in simulating hydrologic processes over the Mississippi River basin. Simulated discharge is seasonally delayed relative to observations: model biases are diagnosed using variables from reanalysis data and attributed primarily to precipitation and runoff related processes. We also show that the seasonality of simulated runoff in several Coupled Model Intercomparison Phase 6 (CMIP6) models is improved relative to CESM1.

Nature-based solutions (NbSs) can mitigate floods and agricultural droughts by enhancing soil health and restoring hydrological cycles. This study highlights that leveraging soil diversity is key to optimizing NbS performance.

Rainfall and evaporation are two key variables influencing when droughts occur and their severity. We use the latest climate simulations for Australia to see how changes to rainfall and evaporation influence future droughts for Australia and show increases are likely over most of Australia, especially in the south. Increases in evaporation are shown to be larger than changes to rainfall over most of the continent. We show that keeping emissions to lower levels can work to mitigate these impacts.

Atmospheric variables from climate models often present biases relative to the past. In order to use these models to assess the impact of climate change on processes of interest, it is necessary to correct these biases. We tested several Multivariate Bias Correction Methods (MBCMs) for 5 physical variables that are input variables for 4 process models. We provide recommendations regarding the use of MBCMs when multivariate and time dependent processes are involved.

We demonstrate the capability of a global operational system to predict seasonal river discharges by accounting for interactions between the atmosphere, ocean, land and rivers. The fully coupled approach introduces a convenient single-step workflow, allowing the simultaneous production of atmospheric and streamflow forecasts. Overall, the approach outperforms the classical ensemble streamflow prediction approach, providing insight into the next-generation hydrological forecasting systems.