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Characterizing the spatiotemporal variability of water fluxes at the stream‐groundwater interface is extremely challenging due to the lack of methods for estimating hyporheic flows at different scales. To address this, we demonstrate the potential of Active‐Distributed Temperature Sensing (DTS) methods for measuring and mapping hyporheic flow in a lowland stream. Experiments were conducted by burying a few hundred meters of heatable Fiber‐Optic cables within streambed sediments in a large meander, where permanent stream‐losing conditions are observed along the stream reach. We propose a new methodology to filter ambient temperature variations along the heated section of the DTS cable and to extend the application of Active‐DTS to losing streams. After data processing, the results show that, along lateral and longitudinal stream profiles, both thermal conductivity and water flux values follow normal distributions with relatively small standard deviations. Hyporheic fluxes vary by one order of magnitude. The absence of correlation between water fluxes within the hyporheic zone and streambed topography variations suggests that the variability is mainly controlled by local streambed heterogeneities. This means that the spatiotemporal variability of fluxes may be used as a marker of the variability of streambed hydraulic conductivities. The relatively low spatial variability (one order of magnitude) in hyporheic flow suggests a small variability of streambed properties. This is an important result for calibrating models assessing hyporheic processes, in which the hydraulic conductivity distribution is generally assumed. Additionally, measurements made over three years yield similar estimates showing the remarkable stability of hyporheic flows through time. Plain Language Summary Characterizing the interactions between groundwater and surface water is extremely challenging although such interactions control water quality and ecosystems resilience to climate changes. Here, we used an innovative approach based on heated fiber optic cables, called Active‐Distributed Temperature Sensing, to image the spatial variability of hyporheic fluxes in a lowland stream. Our results show that the instrumental developments as well as the data processing methodology are very robust to accurately measure in‐situ the thermal conductivity of stream sediments and hyporheic fluxes within the streambed. Interestingly, groundwater flux variability was found relatively limited and not correlated to the morphology of the riverbed. In addition, measurements made over three years yield similar estimates showing the excellent reproducibility of the measurements and the remarkable stability of hyporheic flows through time. These results shed new light about the spatial and temporal variability of hyporheic fluxes in a lowland river. Key Points Active‐Distributed Temperature Sensing was used in a lowland stream to assess and map the spatiotemporal variability of stream infiltration An innovative field setup and a new methodology was developed to remove ambient temperature variations from the raw temperature signal Results suggest relatively homogeneous streambed properties and show remarkable stability of hyporheic flow during few years

In recent years, the rapid expansion of the wind power industry has increased awareness of its ecological impacts. A thorough understanding of these impacts is essential for scientifically planning wind farms (WFs) in the future. While previous studies have evaluated the influences of WFs on vegetation and climate indicators, analyses of the underlying mechanisms remain limited. Here, we focused on 169 cropland WFs in eastern China to investigate the impacts of this prevalent type of WF. Specifically, we developed an analytical framework that first evaluated the impacts of cropland WFs on ecological and environmental factors. Ridge regression was then applied to identify differences in the environmental variables inside and outside the WFs that affect gross primary productivity (GPP) and evapotranspiration (ET). Finally, structural equation modeling was used to delineate the pathways by which WFs influence GPP and ET. The results show that WFs reduce daytime land surface temperature by 0.186 °C, significantly increase soil moisture (0.003 m3 m−3), and decrease the vapor pressure deficit by 0.095 hPa. These environmental changes subsequently contributed to a significant rise in GPP (25.181 gC m−2) and ET (3.785 mm). By elucidating the complex ecological impacts of WFs from a pathway perspective, this study reveals the interactions among factors influenced by the local scale temperature effects of WFs, providing a deeper understanding of their ecological impacts and valuable insights for future research.

Where and how fast does water flow from soil into roots? The answer to this question requires direct and in situ measurement of local flow of water into roots of transpiring plants growing in soil. We used neutron radiography to trace the transport of deuterated water (D2O) in lupin (Lupinus albus) roots. Lupins were grown in aluminum containers (30 × 25 × 1 cm) filled with sandy soil. D2O was injected in different soil regions and its transport in soil and roots was monitored by neutron radiography. The transport of water into roots was then quantified using a convection–diffusion model of D2O transport into roots. The results showed that water uptake was not uniform along roots. Water uptake was higher in the upper soil layers than in the lower ones. Along an individual root, the radial flux was higher in the proximal segments than in the distal segments. In lupins, most of the water uptake occurred in lateral roots. The function of the taproot was to collect water from laterals and transport it to the shoot. This function is ensured by a low radial conductivity and a high axial conductivity. Lupin root architecture seems well designed to take up water from deep soil layers.

Changes in the terrestrial water cycle are often discussed as either an acceleration or a deceleration of the cycle. However, different combinations of precipitation, runoff, and evapotranspiration changes are possible, and it is largely unknown which combinations actually occur around the world. We quantify water flux changes and their combinations from 1980–2000 to 2001–2020 based on: (a) observational data for 3,614 hydrological catchments with worldwide distribution; (b) a new ensemble of machine learning (ML) models, trained and tested on data for these catchments and applied globally; and, comparatively, (c) four alternative data sets for water flux changes from 1981–1995 to 1996–2010 in 1,561 catchments worldwide. The changes in precipitation, runoff, and evapotranspiration are mostly in opposite directions, with 51 ± 7% of the catchments or land area (based on (a–b); 56 ± 4% based on (c)) experiencing acceleration or deceleration in two fluxes and the opposite in the third. Unidirectional changes in all water fluxes are observed only in 27.5 ± 2.5% and 21.5 ± 4.5% of the catchments or land area (based on (a–b); 23.5 ± 6.5% and 19.5 ± 4.5% based on (c)) for full deceleration and full acceleration, respectively. Different terrestrial water fluxes thus concurrently decelerate and accelerate at both local and global scales. Interpretation of the ML modeling further shows different driver‐impact relationships for the water flux changes over time than across space. This space‐time difference challenges the usefulness of space‐for‐time substitution approaches for temporal flux changes. The ML model ensemble developed in this study offers a promising approach for addressing this challenge. Plain Language Summary The terrestrial water cycle includes the main fluxes of precipitation, runoff, and evapotranspiration. The variations and changes of these fluxes around the world and over time are largely unknown but critical for societies and ecosystems. This study quantifies the water flux changes over recent decades in numerous hydrological catchments with worldwide spreading based on several comparative data sets and machine learning (ML). More than half of the catchments and land area have experienced increases (acceleration) or increases (deceleration) in two of the main fluxes and the opposite in the third. In contrast to the common view of the terrestrial water cycle as either accelerating or decelerating, full deceleration or acceleration of all three fluxes is seen only in around 20%–28% of the catchments or land area. The study also indicates different dominant drivers of the water flux changes in time than in space. This challenges the usefulness of common space‐for‐time substitution approaches for temporal water flux changes. The ML modeling approach developed in this study presents a promising tool for meeting this challenge around the world. Key Points We quantify precipitation, runoff, and evapotranspiration changes around the world based on different data sets and machine learning The water flux changes combine mostly in opposite directions, concurrently accelerating and decelerating, locally and globally The dominant driver‐impact relationships for the water flux changes in time differ from those for the flux changes in space

The water and carbon exchange between the land surface and the atmosphere is regulated by meteorological conditions and plant physiological processes. Traditional mechanistic modeling approaches, for example, the Earth system model ICON‐ESM with the land component JSBACH4, are hampered by relatively rigid parameterizations for stomatal conductance to model land‐atmosphere coupling. We develop a hybrid modeling approach integrating data‐driven flexible parameterizations based on eddy‐covariance flux measurements (FLUXNET) with mechanistic modeling. We replace specific empirical parametrizations of the coupled photosynthesis (gross primary production [GPP]) and transpiration (Etr${E}_{\\text{tr}}$ ) modules with feed‐forward neural network models pre‐trained on observations. In a proof‐of‐concept, we demonstrate that our approach reconstructs original JSBACH4 parameterizations for stomatal conductance (gs${g}_{s}$ ), maximum carboxylation rates (Vcmax${V}_{\\text{cmax}}$ ) and the maximum electron transport rates (Jmax${J}_{\\text{max}}$ ), that decisively control GPP and Etr${E}_{\\text{tr}}$ . We then replace JSBACH4's original parametrizations by calling the emulator parameterizations trained on original JSBACH4 output using a Python‐Fortran bridge. Adapting the approach to observational data, Hybrid‐JSBACH4 infers these parametrizations from eddy‐covariance measurements to construct observation‐informed modeling of water and carbon fluxes in JSBACH4. The mean hourly residuals of Etr${E}_{\\text{tr}}$in Hybrid‐JSBACH4 with respect to FLUXNET observations vary between −0.1 and 0.15 kg m−2 hr−1 while the JSBACH4 Etr${E}_{\\text{tr}}$residuals vary between −0.3 and 0.2 kg m−2 hr−1 for forest and grassland sites. The mean hourly residuals for GPP of Hybrid‐JSBACH4 with respect to observations vary between −0.5 and 0.5 gC m−2 hr−1, compared to the original JSBACH4 with residuals ranging between −1.0 and 0.5 gC m−2 hr−1, for forest and grassland sites. Our Hybrid‐JSBACH4 model improves the representation of plant physiological responses, and reduces biases in transpiration and GPP simulations under varying atmospheric dryness and water availability conditions. Plain Language Summary This study presents a novel hybrid modeling approach, Hybrid‐JSBACH4, that combines machine learning with traditional process‐based modeling to enhance the representation of carbon and water exchanges in terrestrial ecosystems. Specifically, we integrate a feed‐forward neural network (FNN) trained on eddy‐covariance flux data from FLUXNET to replace key parameterizations in the JSBACH4 model, which is part of the ICON‐ESM framework. We demonstrate that our hybrid model captures latent variables, stomatal conductance (gs${g}_{s}$ ), maximum carboxylation rates (Vcmax${V}_{\\text{cmax}}$ ), and maximum electron transport rates (Jmax${J}_{\\text{max}}$ ) and emulates the gross primary productivity gross primary production (GPP) and transpiration (Etr${E}_{\\text{tr}}$ ) outputs of the original JSBACH4 model. By utilizing FLUXNET observations, the Hybrid‐JSBACH4 offers a more adaptable and data‐driven representation of plant physiological responses for GPP and Etr${E}_{\\text{tr}}$ . The results indicate that Hybrid‐JSBACH4 significantly improve simulations of GPP and Etr${E}_{\\text{tr}}$under varying environmental conditions, effectively capturing the influences of vapor pressure deficit and soil water content. Compared to JSBACH4, Hybrid‐JSBACH4 reduces biases in transpiration and GPP, better‐capturing ecosystem dynamics. These refinements enhance our ability to predict ecosystem responses, particularly in balancing supply‐driven and demand‐driven water stress, leading to a more physically consistent and unbiased representation of land‐atmosphere interactions. Key Points The Hybrid‐JSBACH4 model integrates pre‐trained neural networks with land surface modeling to improve photosynthesis and transpiration representation The Hybrid‐JSBACH4 reduces transpiration and water‐use efficiency biases under varying soil moisture and atmospheric demand conditions Structural rigidity in carbon cycle formulation limits photosynthesis improvement in trade‐off for improved transpiration Hybrid‐JSBACH4

A 38-year record of bottom-water dissolved oxygen concentrations in coastal marine ecosystems around Denmark (1965-2003) and a longer, partially reconstructed record of total nitrogen (TN) inputs (1900-2003) were assembled with the purpose of describing long-term patterns in hypoxia and anoxia. In addition, interannual variations in bottom-water oxygen concentrations were analyzed in relation to various explanatory variables (bottom temperature, wind speed, advective transport, TN loading). Reconstructed TN loads peaked in the 1980s, with a gradual decline to the present, commensurate with a legislated nutrient reduction strategy. Mean bottom-water oxygen concentrations during summer have significantly declined in coastal marine ecosystems, decreasing substantially during the 1980s and were extremely variable thereafter. Despite decreasing TN loads, the worst hypoxic event ever recorded in open waters occurred in 2002. For estuaries and coastal areas, bottom-water oxygen concentrations were best described by TN input from land and wind speed in July-September, explaining 52% of the interannual variation in concentrations. For open sea areas, bottom-water oxygen concentrations were also modulated by TN input from land; however, additional significant variables included advective transport of water and Skagerrak surface-water temperature and explained 49% of interannual variations in concentrations. Reductions in the number of benthic species and alpha diversity were significantly related to the duration of the 2002 hypoxic event. Gradual decreases in diversity measures (number of species and alpha diversity) over the first 2-4 weeks show that the benthic community undergoes significant changes before the duration of hypoxia is severe enough to cause the community to collapse. Enhanced sediment-water fluxes of NH4⁺₄ and PO₄3 occur with hypoxia, increasing nutrient concentrations in the water column and stimulating additional phytoplankton production. Repeated hypoxic events have changed the character of benthic communities and how organic matter is processed in sediments. Our data suggest that repeated hypoxic events lead to an increase in susceptibility of Danish waters to eutrophication and further hypoxia.

In this study, silica thin film pH=6 (precursor TEOS:tetraethyl ortosilicate) developed from sol gel process and deposited (2 layers) directly onto alumina substrate(tubular support with 100 nm pore size) without depositing interlayer (interlayer-free).Then, the desalination process via pervaporation was applied to test the membranesperformanceusing artificial saline water and wetland saline water. Results show the decrease of water flux (1.9 to 1.43 kg m-2 h-1) and salt rejection(97 to 95%) when using artificial salty water (0-7.5 wt%) and the long-term stability of silica membrane was stable at 1.7 kg m-2 h-1 for over 100 hours when using wetland saline water as a feed.

Plants mediate water fluxes within the soil–vegetation–atmosphere continuum. This water transfer in soils, through plants, into the atmosphere can be effectively traced by stable isotopologues of water. However, rapid dynamic processes have only recently gained attention, such as adaptations in root water uptake depths (within hours to days) or the imprint of transpirational fluxes on atmospheric moisture, particularly promoted by the development of real-time in-situ water vapour stable isotope observation techniques. We focus on open questions and emerging insights at the soil–plant and plant–atmosphere interfaces, aswebelieve that these are the controlling factors for ecosystem water cycling. At both interfaces, complex pictures of interacting ecophysiological and hydrological processes emerge: root water uptake dynamics depend on both spatiotemporal variations in water availability and species-specific regulation of adaptive root conductivity within the rooting system by, for example, modulating soil–root conductivity in response to water and nutrient demands. Similarly, plant water transport and losses are a fine-tuned interplay between species-specific structural and functional strategies of water use and atmospheric processes. We propose that only by explicitly merging insights from distinct disciplines – for example, hydrology, plant physiology and atmospheric sciences – will we gain a holistic picture of the impact of vegetation on processes governing the soil–plant–atmosphere continuum.

Understanding how agroecosystems respond to environmental variability is fundamental to predicting productivity and sustainability under a changing climate. We analyzed 55 site‐years of high‐frequency eddy covariance observations from five agroecosystems—two perennial grasses (miscanthus and switchgrass), two annual rotation systems (maize–soybean and sorghum–soybean), and a restored native prairie—to examine ecosystem‐scale carbon, water, and energy fluxes. Using an interpretable machine‐learning framework with regression tree ensembles, Shapley Additive Explanations, and Accumulated Local Effects, we quantified how environmental and temporal factors regulate gross primary productivity (GPP), evapotranspiration (ET), water‐use efficiency, and the Bowen ratio. Perennials exhibited stronger physiological buffering and maintained fluxes across a broader range of temperature and moisture conditions, reflecting deeper rooting and persistent canopy cover. Annuals, in contrast, showed greater short‐term variability and stronger coupling to atmospheric demand, with GPP and ET declining rapidly under low humidity or soil moisture. Differences in temperature sensitivity of Bowen ratio further revealed that perennials sustained proportionally greater sensible heat flux under cool conditions, whereas annuals exhibited constrained energy exchange when evaporative demand was low. Together, these results demonstrate that crop life cycle and canopy structure are fundamental determinants of ecosystem‐scale carbon–water–energy coupling. By integrating long‐term flux observations with interpretable machine learning, this study identifies the environmental drivers that shape agroecosystem function and highlights how conversion from annual to perennial feedstocks can enhance climatic resilience and alter land–atmosphere energy feedbacks. These findings provide a data‐driven basis for improving crop and Earth‐system models and for guiding bioenergy landscape design under future climate scenarios. Environmental drivers regulate carbon, water, and energy fluxes differently in perennial and annual bioenergy agroecosystems. Long‐term eddy covariance data and interpretable machine learning reveal broader temperature responses, more stable water‐use efficiency, and sustained air heating in perennials, contrasted with stronger atmospheric sensitivity in annual crops.

Man-made reservoirs play a key role in the terrestrial water system. They alter water fluxes at the land surface and impact surface water storage through water management regulations for diverse purposes such as irrigation, municipal water supply, hydropower generation, and flood control. Although most developed countries have established sophisticated observing systems for many variables in the land surface water cycle, long-term and consistent records of reservoir storage are much more limited and not always shared. Furthermore, most land surface hydrological models do not represent the effects of water management activities. Here, the contribution of reservoirs to seasonal water storage variations is investigated using a large-scale water management model to simulate the effects of reservoir management at basin and continental scales. The model was run from 1948 to 2010 at a spatial resolution of 0.25° latitude–longitude. A total of 166 of the largest reservoirs in the world with a total capacity of about 3900 km³ (nearly 60%of the globally integrated reservoir capacity) were simulated. The global reservoir storage time series reflects the massive expansion of global reservoir capacity; over 30 000 reservoirs have been constructed during the past half century, with a mean absolute interannual storage variation of 89 km³. The results indicate that the average reservoir-induced seasonal storage variation is nearly 700 km³ or about 10% of the global reservoir storage. For some river basins, such as the Yellow River, seasonal reservoir storage variations can be as large as 72% of combined snow water equivalent and soil moisture storage.