This study develops a new method to improve soil moisture measurements from cosmic ray neutron sensors. Using advanced modeling, we account for changes in atmospheric pressure, air humidity, and cosmic ray intensity based on site and sensor characteristics. The results show that precise adjustments are needed to better measure soil moisture, supporting improved decisions in farming, water management, and climate monitoring.

In semi-arid regions, the timing and duration of the rainy season are crucial for agriculture. This study introduces a new framework for improving estimations of start and end of the rainy season by testing how well they fit local vegetation data. We improve the performance of existing methods and present a new one with higher performance. Our findings can help to make informed decisions about water usage, and the framework can be applied to other regions as well.

Our research presents an improved method to enhance the understanding and prediction of water flows in rivers and streams, focusing on key runoff components: surface flow, baseflow, and total runoff. Using a streamlined model, the MPS model, we analyzed over 600 catchments in China and the U.S., demonstrating its accuracy in capturing the spatial and temporal variability of these components. This model offers a practical tool for water resource management.

We analyzed annual, monthly, and extreme daily P loss trends from 1974 to 2021 across eight major P-contributing tributaries, and did not observe a clear reduction of annual P load among these tributaries. We found a decline trend for daily P load in Maumee River under extreme daily flow levels, and controlling P load remains a more pressing issue compared to nitrogen. We predicted that P load in almost all tributaries will continue to pose challenges to Lake Erie.

Accurate groundwater level predictions are essential for a sustainable groundwater management. This study applies two machine learning (ML) models—N-HiTS and TFT—to seasonally predict groundwater levels for 5,288 monitoring wells across Germany. Both approaches provided good predictions across diverse hydrogeological conditions, whereby N-HiTS outperformed the TFT. Both models showed better perforance in areas with high data density, in lowlands, and when distinct seasonal dynamics occurred.

Integrated Water Availability Assessments is a national initiative to characterize water availability in the U.S. The WRF-Hydro model is used to generate an estimate of hydrological fluxes and storage across the conterminous United States. The streamflow performance is reasonable especially in the eastern and western U.S. Model performance in estimating snow, evapotranspiration and soil moisture is also reasonable with some differences against the verification datasets in certain areas.

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.

This study explores how 20 years of remote-sensed discharge data from the ESA CCI improve large-scale hydrological models, CTRIP and MGB, through data assimilation. Using an EnKF framework across the Niger and Congo basins, it shows how assimilating denser temporal discharge data reduces biases and improves flow variability, enhancing accuracy. These findings underscore the role of long-term discharge data in refining models for climate assessments, water management, and forecasting.

This research explores different modelling approaches within a distributed and physically based hydrological model, emphasizing configurations that integrate groundwater recharge and dynamics. The study demonstrates that precise adjustments in model calibration can enhance the accuracy and representation of hydrological variables, which are crucial for effective water management and climate change adaptation strategies.

The Desaguadero-Salado-Chadiluevú-Curacó (DSCC) River is a semiarid river which is severely dammed in its tributaries which collect the snowmelt runoff. This runoff feeds mostly gravitational irrigation systems of very low efficiency. As a result, the DSCC River does not have natural runoff. The proposed Hydrological Regime Index (HRI) is able to discriminate and quantify regime alterations under permanent and non-permanent flow conditions and with low and high impoundment conditions.

Flooding is a major problem and predicting it accurately over large areas is tough. This study tested a new approach to forecast floods across a large region in the United States. By dividing the area into smaller areas to develop the prediction models and then combining, the method successfully simulated surface water extent for both high and low flow periods. The results were more accurate than existing approaches with similar methods which can improve flood forecasting for larger areas.

This study introduces a new way to analyze droughts over time and space, making it easier to track and understand their progression. By focusing on the severity, affected areas, and the movement of droughts, the research identifies regions in Northeast Brazil that require special attention. The findings help decision-makers better manage drought impacts, improving planning and response strategies to reduce risks. This work was driven by the need to enhance drought management tools.

We used explainable 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.

Our study focuses on the rising concern of pesticides damaging aquatic ecosystems, which puts drinking water, the environment, and human health at risk. We provided more accurate estimates of how pesticides break down and spread in small water systems, helping to improve pesticide management practices. By using unique chemical markers in our analysis, we enhanced the accuracy of our predictions, offering important insights for better protection of water sources and natural ecosystems.

This study uses an integrated water-energy-land optimization model to explore sustainable water use pathways in the Yellow River Basin. We find water conflicts between energy and irrigation water use, and quantify the mitigation and spillover effects of water transfer. We also highlight the critical role of energy production, implying that the energy sector transformation is key to the water system of the Yellow River 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.

Streamflow signatures can help characterize a watershed’s response to meteorological conditions. We explored if surface water storage-related variables, which are typically excluded from streamflow signature analyses, may help explain spatial variability in streamflow signatures. We found that remotely sensed surface water storage extent and duration were correlated with and explained a portion of the variability in many of the hydrologic signatures across the 72 streamflow gages.

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.

Understanding hydrological variability is essential for ecological conservation and sustainable development. Evapotranspiration influences the carbon cycle, finding what causes its variability is important for ecosystems. This study shows that not only El Niño – ENSO influences South America’s rainfall and evaporation, but also other phenomena in the Atlantic Ocean. The impacts change depending on the season, impacting the Amazon and Orinoco basins.

To manage Earth's water resources effectively amid climate change, it's crucial to understand both surface and groundwater processes. We developed a new modeling system that combines two advanced tools, ParFlow and LIS/Noah-MP, to better simulate both land surface and groundwater interactions. By testing this integrated model in the Upper Colorado River Basin, we found it improves predictions of hydrologic processes, especially in complex terrains.

We analyzed how well two global climate datasets can simulate river flows across Europe over the last 150 years. Our results show good performance overall, revealing important long-term changes in water availability and extreme events, like floods, in different regions. This research helps us better understand past and future water trends, providing insights to manage resources and address the challenges posed by climate change.

This paper presents a method to solve a certain type of equation controlling the storage of water in hydrological models. This equation is often solved with complex numerical methods that may lead to slow runtimes. Our method called “Quadratic Solution of the Approximate Reservoir Equation” (QuaSoARe) is both fast and applicable to any equation of this kind regardless of its complexity. The method reduces runtime by a factor of 10 to 50 depending on the model.

Simultaneous or sequential occurrence of different flood processes, including extreme storm surges and heavy precipitation, tends to trigger compound floods, which are often destructive. We develop a land-river-ocean coupled model for the prediction of compound floods in a big river delta region. The coupled model is shown to perform well in simulating the combined effect of heavy precipitation, river flood routing, storm surges, and inundation for events contributed by tropical cyclones.

This study describes a new methodology to identify regional structures in the dependence of extreme rainfall on global climate indexes. The study area is north-central Italy, but the methods are highly adaptable to other regions. We observe that the Western Mediterranean Oscillation Index has a strong influence, whose geographical pattern is in line with other studies. We show also that the use of the Index may improve the estimation of the extreme rainfall depth with a given probability.

The hydrology of cold regions in the human population is poorly understood due to complex motion and limited data, hindering streamflow analysis. Using existing models, we compared runoff from an extended model with snowmelt and frozen ground, validating its reliability and integration. This study focuses on the effects of snowmelt and frozen ground on runoff, affecting precipitation type, surface-groundwater partitioning, and evapotranspiration.

Modeling reservoir outflow and storage is challenging due to limited publicly available data and human decision-making. For 100 reservoirs in the USA, we examined how calibrating reservoir algorithms against outflow and storage-related variables affects performance. We found that calibration notably improves storage simulations, while outflow simulations are more influenced by the quality of inflow data. We recommend using remotely sensed storage anomalies to calibrate reservoir algorithms.

This study compares two hydrological modeling approaches for assessing climate change impacts on water systems. We evaluate the conventional method alongside the asynchronous method, which excels in capturing extreme events but faces challenges with event timing. Our findings suggest that a semi-asynchronous approach, blending the strengths of both methods, could offer better results. This research provides key insights for supporting sustainable resource planning in a changing climate.

We developed the first high-resolution, integrated surface water-groundwater hydrologic model of the entire continental China using ParFlow. The model shows good performance of streamflow and water table depth when compared to global data products and observations. It is essential for water resources management and decision making in China within a consistent framework in the changing world. It also has significant implications for similar modeling in other places in the world.

We assessed the value of high-resolution data and parameters transferability across temporal scales based on 7 catchments in northern China. We found that higher resolution data does not always improve model performance, questioning the need for such data; Model parameters are transferable across different data resolutions, but not across computational time steps. It is recommended to utilize smaller computational time step when building hydrological models even without high-resolution data.

The Hybrid Ensemble and Variational Data Assimilation framework for Environmental System (HEAVEN) enhances flood predictions by refining hydrologic models through improved data integration and uncertainty management. Tested in three Southeastern U.S. watersheds during hurricanes, HEAVEN assimilates real-time USGS streamflow data, boosting forecast accuracy.

This study estimates irrigation in the Po Valley using AquaCrop and Noah-MP models with sprinkler irrigation. Noah-MP shows higher annual rates than AquaCrop due to more water losses. After adjusting, both align with reported irrigation ranges (500–600 mm/yr). Soil moisture estimates from both models match satellite data, though both have limitations in vegetation and evapotranspiration modeling. The study emphasizes the need for observations to improve irrigation estimates.