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.

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.

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.

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. 

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.

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.

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.

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.