This study compares long short-term memory (LSTM) neural networks with traditional hydrological models to predict future streamflow under climate change. Using data from 148 catchments, it finds that LSTM models, which learn from extensive data sequences, perform differently and often better than traditional hydrological models. The continental LSTM model, which includes data from diverse climate zones, is particularly effective for understanding climate impacts on water resources.

We measured nitrate-nitrogen concentrations using a UV nitrate sensor over 4 years in a shallow gravel aquifer. We observed pulses of nitrate nitrogen in response to forestry harvesting during subsequent rainfall recharge. Our novel hysteresis analysis helped elucidate event-scale nitrate-nitrogen dynamics, while integrating continuous data improved nitrate-nitrogen load estimates. This research provides new insights into the variability of nitrate nitrogen in permeable aquifers.

In this paper, we use models to demonstrate that even small flood reservoirs – which capture water to avoid floods downstream – can be repurposed to release water in drier conditions without affecting their ability to protect against floods. By capturing water and releasing it once levels are low, we show that reservoirs can greatly increase the water available in drought. Having more water available to the reservoir, however, is not necessarily better for drought protection.

Persistent droughts change how rivers respond to rainfall. Our study of over 5000 catchments worldwide found that hydrological and soil moisture droughts decrease river-flow response to rain, especially in arid regions, while vegetation decline slightly increases it. Snow-covered areas are more resilient due to stored water buffering changes. Droughts can also cause long-lasting changes, with short and intense droughts reducing river response to rainfall and prolonged droughts increasing it.

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 the onset 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 us to make informed decisions about water usage, and the framework can be applied to other regions as well.

Coordinate transformations are often used in the finite element method. Here, three existing approaches are discussed that can be applied to the same mapping problem. From a mathematical viewpoint, the approaches are quite different. It is shown that all three approaches provide the same result. Interestingly, the more mathematically challenging approaches provide an expression that is the most practical to implement in a finite element code.

There is a lack of understanding of how urban aquatic nature-based solutions (aquaNBSs) affect ecohydrology and how they in turn are affected by urbanization and climate change. We use a multi-tracer approach of stable water isotopes, hydrochemistry, and microbial and macrophyte diversity to disentangle the effects of hydroclimate and urbanization. The results show potential limitations of aquaNBSs regarding water quality and biodiversity in response to hydroclimate and urban water sources.

This study introduces a multiagent socio-hydrologic framework for city-, inter-city-, and watershed-scale integrated green infrastructures (GIs) and water resource management. Applied to the Upper Mississippi River basin, it explores GI-driven water-sharing dynamics in a watershed. It identifies four city-scale water use patterns and characterizes cost and equity on broader scales, thereby enhancing comprehension of the role of GIs in water resource management and aiding decision-making.

We assessed the value of high-resolution data and parameter transferability across temporal scales based on seven catchments in northern China. We found that higher-resolution data do 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 a smaller computational time step when building hydrological models even without high-resolution data.

In the last decade, the development of on-site field laboratories to measure water chemistry at sub-hourly measurement intervals has drastically advanced, while there is no literature that provides detailed technical, organisational and operational guidelines in running such equipment. Based on our experiences of running three French field laboratories over 7 years, we share the main stages in the deployment of this tool in the field, the difficulties encountered and the proposed solutions. 

There is an increasing interest in rewetting drained peatlands to regain their important ecosystem functions. However, as peatland rewetting is a relatively new strategy, the scientific foundation for this approach is not solid. Therefore, we investigated the impact of rewetting on flood mitigation using high-resolution hydrological field observations. Our results showed that peatland rewetting significantly reduced peak flow and runoff coefficient and mitigated flashy hydrograph responses.

Global soil information introduces uncertainty into models that simulate soil hydrothermal changes. Using the Noah with Multiparameterization (Noah-MP) model with two different global soil datasets, we find under-represented soil properties in wet loam, causing a dry bias in soil moisture. This bias is more pronounced and drought categories are more severe in the SoilGrids dataset. We conclude that models should incorporate detailed, region-specific soil information to minimize model uncertainties.

The data-driven approach enhances soil water content measurements by improving the precision of cosmic ray neutron sensors (CRNSs). This study demonstrates a new method to account for dynamics in air pressure, atmospheric humidity, and incoming neutron intensity. Soil water content measured by CRNSs showed reduced errors compared to reference measurements. The findings highlight the need for precise adjustments to better measure soil moisture for agricultural, water, and climate monitoring.

Stream water temperature (SWT) is a key indicator of water quality with implications for public use and health, ecosystem function, and aquatic life survival. Advances in modeling have helped improve our understanding of SWT dynamics, but challenges remain. Recently, machine learning (ML) has been used in SWT modeling, but questions remain about how the use of ML improves insight into SWT causes and effects. This work reviews ML-SWT modeling studies (and metrics) and considers future directions.

The 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 induced by tropical cyclones.

During geological carbon sequestration, the injected supercritical CO₂, being less dense, floats above the brine. The dissolution of CO₂ into brine helps mitigate the risk of CO₂ leakage. As CO₂ dissolves into the brine, it increases the density of brine in the upper layer, initiating gravity-driven convection (GDC), which significantly enhances the rate of CO₂ dissolution. We derived two empirical formulas to predict the asymptotic dissolution rate driven by GDC in heterogeneous fields.

Stream chemistry in permafrost watersheds is highly seasonal due to ground thaw and declining streamflow after spring melt, reducing hydrologic connectivity with organic-rich flow paths in fall. We quantified the magnitude of stream chemistry seasonality (driven by processes other than seasonal streamflow) across 10 watersheds. Watersheds with steeper slopes saw a more rapid decline in DOC (dissolved organic carbon) concentrations, while greater permafrost extent led to a greater relative increase in ion concentrations.

Accurately estimating groundwater recharge using numerical models is particularly difficult in cold regions with snow and soil freezing. This study evaluated a physics-based model against high-resolution field measurements. Our findings highlight a need for a better representation of soil-freezing processes, offering a roadmap for future model development. This leads to more accurate models to aid in water resource management decisions in cold climates.

The classic logistic function characterizes the stationary relationship between drought loss and intensity. This paper accounts for time in the magnitude, shape and location parameters of the logistic function and derives nonstationary intensity loss functions. A case study is designed to test the functions for drought-affected populations by province in mainland China from 2006 to 2023. Overall, the nonstationary intensity loss functions are shown to be a useful tool for drought management.

Seasonal streamflow forecasts are an important component of flood risk management. Here, we train and test a machine learning model to predict the monthly maximum daily streamflow up to 4 months ahead. We train the model on precipitation and temperature forecasts to produce probabilistic hindcasts for 579 stations across the UK for the period 2004–2016. We show skilful results up to 4 months ahead in many locations, although, in general, the skill declines with increasing lead time.

The Hybrid Ensemble and Variational Data Assimilation framework for Environmental Systems (HEAVEN) enhances flood predictions by refining hydrologic models through improved data integration and uncertainty management. Tested in three southeastern US watersheds during hurricanes, HEAVEN assimilates real-time United States Geological Survey (USGS) streamflow data, boosting forecast accuracy.

The release of carbon from landscapes into streams is one important component within the global carbon cycle. We measured the concentrations of dissolved organic carbon (DOC), one of the forms in which carbon can be present, in the streams of three nested forested subcatchments over 12 months. The export of DOC is closely linked to water flow processes within the subcatchments, but the interplay of soils, vegetation, topography, and microclimate results in distinct seasonal DOC release patterns.

Stream networks expand and contract, affecting the amount and quality of water in perennial streams. This study presents measurements of changes in water chemistry and the flowing portion of the drainage network during rainfall events in two neighboring catchments. Despite the proximity and similar size, soil, and bedrock, water chemistry and stream network dynamics differed substantially in the two catchments. These differences are attributed to the differences in the slope and channel network.

Hydrologic models are needed to provide simulations of water availability, floods, and droughts. The accuracy of these simulations is often quantified with so-called performance scores. A common thought is that different models are more or less applicable to different landscapes, depending on how the model works. We show that performance scores are not helpful in distinguishing between different models and thus cannot easily be used to select an appropriate model for a specific place.