There have been many machine learning simulation studies based on eddy-covariance observations for water flux and evapotranspiration. We performed a meta-analysis of such studies to clarify the impact of different algorithms and predictors, etc., on the reported prediction accuracy. It can, to some extent, guide future global water flux modeling studies and help us better understand the terrestrial ecosystem water cycle.

Rainwater infiltrates aquifers and travels a long subsurface journey towards the ocean where it eventually enters below sea level. In its path towards the sea, water becomes enriched in many compounds that are naturally or artificially present within soils and sediments. We demonstrate that extreme rainfall events may significantly increase the inflow of water to the ocean, thereby increasing the supply of these compounds that are fundamental for the sustainability of coastal ecosystems.

Facing the increasing challenge of sustainable crop supply with limited water resources due to climate change, large-scale responses in the water footprint (WF) and WF benchmarks of crop production remain unclear. Here, we quantify the effects of future climate change scenarios on the WF and WF benchmarks of maize and wheat in time and space in China. Differences in crop growth between rain-fed and irrigated farms and among furrow-, sprinkler-, and micro-irrigated regimes are identified.

This work quantifies future snow changes and their impacts on hydrology in the upper Salween River (USR) under SSP126 and SSP585 using a cryosphere–hydrology model. Future warm–wet climate is not conducive to the development of snow. The rain–snow-dominated pattern of runoff will shift to a rain-dominated pattern after the 2040s under SSP585 but is unchanged under SSP126. The findings improve our understanding of cryosphere–hydrology processes and can assist water resource management in the USR.

Given the crucial impact of irrigation practices on the water cycle, this study aims at estimating irrigation through the development of an innovative data assimilation system able to ingest high-resolution Sentinel-1 radar observations into the Noah-MP land surface model. The developed methodology has important implications for global water resource management and the comprehension of human impacts on the water cycle and identifies main challenges and outlooks for future research.

This study explores the link between hydrometeorological droughts and their socioeconomic impact at a subnational scale based on the newly developed disaster dataset with subnational location information. Hydrometeorological drought-prone areas were generally consistent with socioeconomic drought-prone areas in the disaster dataset. Our analysis clarifies the importance of the use of subnational disaster information.

We used a combination of aerial, thermal, hydrologic, and radionuclide monitoring to investigate intertidal springs flowing into a coastal lagoon with a threatened ecosystem. Field data highlight the critical hydrologic and thermal role of these springs in the nearshore zone, and modelling results reveal that the groundwater springs will likely warm substantially in the coming decades due to climate change. Springs sourced from shallower zones in the aquifer will warm first.

Due to climate change, it is extremely important to determine evaporation as accurately as possible. In nature, there are sediments and macrophytes in the open waters; thus, one of the aims was to investigate their effect on evaporation. The second aim of this paper was to estimate daily evaporation by using different models, which, according to results, have high priority in the evaporation prediction. Water management can obtain useful information from the results of the current research.

In this study, we combine a deep-learning approach that predicts sap flow with a hydrological model to improve soil moisture and transpiration estimates at the catchment scale. Our results highlight that hybrid-model approaches, combining machine learning with physically based models, are a promising way to improve our ability to make hydrological predictions.

The Digital Earth (DE) metaphor is very useful for both end users and hydrological modelers. We analyse different categories of models, with the view of making them part of a Digital eARth Twin Hydrology system (called DARTH). We also stress the idea that DARTHs are not models in and of themselves, rather they need to be built on an appropriate information technology infrastructure. It is remarked that DARTHs have to, by construction, support the open-science movement and its ideas.

Methodology for developing an operational 7-day ensemble streamflow forecasting service for Australia is presented. The methodology is tested for 100 catchments to learn the characteristics of different NWP rainfall forecasts, the effect of post-processing, and the optimal ensemble size and bootstrapping parameters. Forecasts are generated using NWP rainfall products post-processed by the CHyPP model, the GR4H hydrologic model, and the ERRIS streamflow post-processor inbuilt in the SWIFT package