This technical note evaluates the efficacy of the Self-Organizing Map (SOM) algorithm to represent concentration-discharge hysteresis patterns in watersheds. Through a proof-of-concept with sediment hysteresis, we show that the SOM algorithm accurately characterizes loop shape and can also be used to reveal the controls of hysteresis patterns in watersheds. We developed a Python package along with this technical note to support broader adoption of the SOM algorithm for hysteresis workflows.

We captured an extreme drought and subsequent wet period, showing microbiomes track time-varying dissolved carbon metabolism and modulate carbon dioxide flux. Particularly, the drought boosts heterotrophs, accelerating dissolved organic carbon respiration and carbon dioxide emission. The biological carbon pump induces priming: rapid labile dissolved organic carbon loss but recalcitrant dissolved organic carbon accumulation, causing pulsed carbon dioxide after photosynthetic peaks.

In this research, we developed an AI-driven framework that rapidly predicts floods in coastal areas, considering various shoreline protection strategies and a different sea-level rise scenarios. By combining data from two coastal cities, our lightweight model delivers near real-time flood projections under various adaptation strategies. This approach can guide policymakers in designing effective defenses, ultimately promoting safer coastal communities and infrastructure.

Permeability is a crucial hydraulic parameter extensively utilized in groundwater research. Until now, permeability has only been determined using borehole experiments. However, conventional approaches possess numerous limitations and still cannot estimate permeability at large depths. This research uses geophysical methods for the first time to assess 2D and 3D permeability across extensive areas at significant depths in extremely heterogeneous hard rock.

How to evaluate pesticide persistence and degradation across an agricultural catchment to improve strategies for preserving aquatic ecosystems? By using a tracing method based on stable isotope signatures of pesticides at the catchment scale, degradation of the herbicide S-metolachlor could be distinguished from other dissipation processes. A limited number of isotopic measurements can provide critical insights for designing efficient strategies to protect aquatic ecosystems.

This study investigates how combining satellite soil moisture data from radar and radiometer measurements influences weather forecasts in the Korean Integrated Model. Using a weakly coupled data assimilation approach—where atmospheric and land observations are separately assimilated for their respective variables—we found that assimilating both types of soil moisture data together improves forecasts of humidity, temperature, and rainfall compared to using data from a single sensor.

This study evaluates the snow module of LISFLOOD by replacing the discharge-calibrated snowmelt coefficient with pixel-wise values calibrated using EO (Earth Observation) snow cover fraction after standard streamflow calibration. Using a gap-filled high-resolution daily snow dataset, the approach improves snow cover representation across European basins while largely preserving discharge performance, without requiring full model recalibration.

Bedform migration affects hyporheic exchange, its impact on surface-groundwater mixing and groundwater-borne contaminant removal in groundwater-fed streams remained unclear. This study numerically simulated how bedform migration influences mixing and mixing-induced nitrate reduction. In fine-to-medium sands, higher bedform migration celerities reshape mixing zones, enhance mixing flux and extent, but reduce nitrate removal rate and efficiency, thus impairing hyporheic purification capacity.

We studied the time it takes for solutes like pollutants to travel through the landscape and reach streams compared to water. Although water and solutes travel together, they can take different paths and exit at different times due to different processes like sorption, decay, or uptake by plants. Using a simple modeling approach, we identified the conditions under which these travel time differences occur. This helps improve how we understand and predict water quality in natural environments.

Europe was hit by severe droughts in recent years resulting in extreme low flow conditions in rivers. Here, we investigate future climate change effects on river droughts in Bavaria. We find increasing severity for the low peak discharge and low flow duration in a warmer climate. This is caused by hotter and drier summers, where the joint occurrence of heat and drought intensifies. Further, we show that conditions in the year before the drought gain more importance in a warmer climate.

Ecohydrological processes in heavily managed catchments are often inadequately represented in models. We applied a tracer-aided model STARR (Spatially distributed Tracer-Aided Rainfall-Runoff) in an ET (evapotranspiration)-dominated region (the Middle Spree, NE Germany) with major management impacts. Water isotopes were useful in identifying runoff contributions and partitioning ET even at sparse resolution. Trade-offs between discharge- and isotope-based calibrations could be partially mitigated by integrating more process-based conceptualizations into the model.

The EVapotranspiration Assessment from SPAce (EVASPA) algorithm that will generate TRISHNA evapotranspiration (TRISHNA ET) products shows good agreement with observations. Analyses conducted on 4 variables used in contextual ET modelling showed that land surface temperature and evaporative fraction methods introduce the largest uncertainty in ensemble estimates, followed by radiation. Soil heat flux methods contribute the least. Robust ET products will require optimizing the sensitive variables.

Machine learning (ML) techniques have become widely used due to the availability of large data repositories and advancements in computing resources and methods. Our study explored the connection between a model’s accuracy and the information content of input data. Results showed that the accuracy of three ML models significantly improved when high-quality input data were included. These findings highlight the importance of data quality in ML model training.

Accurate prediction of terrestrial water storage (TWS) changes is essential for disaster response. This study evaluates TWS forecast skill over Africa using observations from the Gravity Recovery and Climate Experiment (GRACE). Results show that the NASA Catchment Land Surface Model (CLSM) outperforms Noah with Multi-Parameterization (Noah-MP) across 1-6 months lead times, owing to more accurate reanalysis-based initial conditions and stronger representation of TWS interannual variability.

This study shows that streamflow isotope data in a catchment-scale isotope-based transport model were sufficient to isolate preferential flow in the unsaturated zone but insufficient to isolate the preferential release of young groundwater, as isotope signals were strongly damped by large passive groundwater storage. As a result, groundwater age-selection assumptions affected estimates of long transit times, making it difficult to quantify the fraction of stream water older than 100 days.

Explore2 provides hydrological projections for 1,735 French catchments. Using QUALYPSO (Quasi-Ergodic Analysis of Climate Projections Using Data Augmentation), this study assesses uncertainties, including internal variability. By the end of the century, low flows are projected to decline in southern France under high emissions, while other indicators remain uncertain. Emission scenarios and regional climate models are key uncertainty sources. Internal variability is often as large as climate-driven changes.

The GRACE (Gravity Recovery and Climate Experiment) and GRACE Follow-On satellites monitor changes in Earth's water storage by observing gravity variations. By integrating these observations into hydrological models through data assimilation, estimates of groundwater, soil moisture, and hydrological trends are improved, helping to monitor droughts, floods, and human water use. This review highlights recent advances in GRACE data assimilation, identifies key challenges, and discusses future directions with upcoming satellite missions.

Accurate estimates of interception are important to assess the water availability in ecosystems. We analyzed rainfall interception for a forest site from plot to stand scale. During interception, eddy-covariance measurements of evaporation were systematically underestimated accounting for 24 % of precipitation, while modelled interception evaporation accounted for 45 %. As a consequence, we developed a hybrid correction approach to fit the evaporation data to both the energy and the water balance.

In hydrological science, better accounting for regime shift (abrupt and/or irreversible changes) remains a challenge that could lead to a new paradigm for the adaptation to extreme events (flood , drought). In this article, we present a simple model that can account for a hydrological regime shift in Sahelian watersheds. Based on this model, we find that the Gorouol, Dargol, Nakanbé, and Sirba watersheds have shifted during the droughts of the '70s–'80s.

The UK Hydrological Outlook river flow forecasting system recently implemented the Historic Weather Analogues method. The method improves winter river flow forecast skill across the UK, especially in upland, fast-responding catchments with low catchment storage. Forecast skill is highest in winter due to accurate prediction of atmospheric circulation patterns like the North Atlantic Oscillation. The Ensemble Streamflow prediction method remains a robust benchmark, especially for other seasons.

Abandoned mine systems release contaminants through episodic connectivity rather than steady seepage. At the Reiche Zeche mine, we show that low flow and pre-flush phases accumulate solutes that are rapidly exported during short-lived reconnection events. These hot moments dominate annual metal loads, highlighting the need for event-sensitive monitoring and targeted, near-source remediation strategies.