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Expansive soils are widespread in the world and coincide with areas of high human activity. The main cause of deep instability of expansive soil slopes is due to their softening caused by excavation and seepage. By developing a comprehensive numerical model based on the theory of unsaturated soil, this study examines the characteristics of stress and displacement distribution of expansive soil slopes through hydraulic‐mechanical coupled numerical simulation. This study analyzes the evolution patterns of slopes with excavation unloading and seepage of water storage to reveal the mechanisms of deep‐seated instability of expansive soil slopes. The findings demonstrate that: The instability of expansive soil slopes begins at the foot of the slope and propagates along the interlayer, affecting the entire slope. Excavation leads to the softening of the expansive soil interlayer and the transfer of shear stress. During water storage, the weakening of the soil strength results in slope instability along the weak interlayer slip. Softening of the expansive soil interlayer facilitates the redistribution of shear forces in the slope and alters the distribution law of the plastic zone in the deep layer. Overly slowing down the slope leads to significant excavation unloading, which is detrimental to the slope's stability. [ABSTRACT FROM AUTHOR]

Summary: Co‐occurring plants show wide variation in their hydraulic and photosynthetic traits. Here, we extended 'least‐cost' optimality theory to derive predictions for how variation in key hydraulic traits potentially affects the cost of acquiring and using water in photosynthesis and how this, in turn, should drive variation in photosynthetic traits.We tested these ideas across 18 woody species at a temperate woodland in eastern Australia, focusing on hydraulic traits representing different aspects of plant water balance, that is storage (sapwood capacitance, CS), demand vs supply (branch leaf : sapwood area ratio, AL : AS and leaf : sapwood mass ratio and ML : MS), access to soil water (proxied by predawn leaf water potential, ΨPD) and physical strength (sapwood density, WD).Species with higher AL : AS had higher ratio of leaf‐internal to ambient CO2 concentration during photosynthesis (ci : ca), a trait central to the least‐cost theory framework. CS and the daily operating range of tissue water potential (∆Ψ) had an interactive effect on ci : ca. CS, WD and ΨPD were significantly correlated with each other. These results, along with those from multivariate analyses, underscored the pivotal role leaf : sapwood allocation (AL : AS), and water storage (CS) play in coordination between plant hydraulic and photosynthetic systems.This study uniquely explored the role of hydraulic traits in predicting species‐specific photosynthetic variation based on optimality theory and highlights important mechanistic links within the plant carbon–water balance. See also the Commentary on this article by Macinnis‐Ng, 244: 1681–1683. [ABSTRACT FROM AUTHOR]

Drylands are critical in regulating global carbon sequestration, but the resiliency of these semi‐arid shrub, grassland and forest systems is under threat from global warming and intensifying water stress. We used synergistic satellite optical‐Infrared (IR) and microwave remote sensing observations to quantify plant‐to‐stand level vegetation water potentials and seasonal changes in dryland water stress in the southwestern U.S. Machine‐learning was employed to re‐construct global satellite microwave vegetation optical depth (VOD) retrievals to 500‐m resolution. The re‐constructed results were able to delineate diverse vegetation conditions undetectable from the original 25‐km VOD record, and showed overall favorable correspondence with in situ plant water potential measurements (R from 0.60 to 0.78). The VOD water potential estimates effectively tracked plant water storage changes from hydro‐climate variability over diverse sub‐regions. The re‐constructed VOD record improves satellite capabilities for monitoring the storage and movement of water across the soil‐vegetation‐atmosphere continuum in heterogeneous drylands. Plain Language Summary: Drylands provide ecosystem services to more than two billion people but are under threat from global warming and intensifying water stress. We used a machine‐learning method to combine multi‐satellite observations for estimating vegetation water status and seasonal changes over drylands. Our enhanced‐resolution satellite vegetation retrievals (known as vegetation optical depth) were able to delineate diverse vegetation water conditions undetectable from the original 25‐km data record, and showed strong correspondence with in situ plant water measurements. Our approach improves satellite capabilities for monitoring the storage and movement of water across the soil, vegetation, and atmosphere layers in heterogeneous drylands. Key Points: Daily and 500‐m resolution vegetation optical depth (VOD) was derived through multi‐sensor data fusionOptical‐infrared and microwave synergy resolves heterogeneous vegetation water conditions within 25‐km grid cellsRe‐constructed VOD can improve understanding and monitoring of vegetation water potential at plant‐to‐stand scales [ABSTRACT FROM AUTHOR]

In the Caatinga, a Brazilian dry forest, low water availability is a limiting factor that affects the growth and survival of herbaceous plants. Thus, we hypothesize that simulated extreme changes in precipitation modify aboveground and belowground vegetative responses differently between annual and perennial herbaceous species. Seedlings of the species were obtained through seed germination. During 1 year, they were submitted to three rainfall simulation (RS) treatments, defined from a 60‐year sequence: Twet‐RS of rainy years; Tcontrol‐RS of years close to the historical average; Tdry‐RS of dry years; and with 30 repetitions per treatment. The daily water administration corresponded to the daily average of rainfall in the past. In the simulation of dry years, the annual species reduced the growth drastically, while the perennials showed a moderate reduction. All species reduced biomass and production and expansion of their leaves in dry years but invested in water storage and leaf longevity. Below ground, annuals explored surface water resources and perennials invested in root growth, accessing underground layers. If reductions in rainfall are confirmed, the allocation of herbaceous biomass may follow an “optimal partitioning” strategy, along with maintaining higher water content in the organs, which is an efficient survival strategy. [ABSTRACT FROM AUTHOR]

'Water potential' is the biophysically relevant measure of water status in vegetation relating to stomatal, canopy and hydraulic conductance, as well as mortality thresholds; yet, this cannot be directly related to measured and modelled fluxes of water at plot‐ to landscape‐scale without understanding its relationship with 'water content'. The capacity for detecting vegetation water content via microwave remote sensing further increases the need to understand the link between water content and ecosystem function. In this review, we explore how the fundamental measures of water status, water potential and water content are linked at ecosystem‐scale drawing on the existing theory of pressure‐volume (PV) relationships. We define and evaluate the concept and limitations of applying PV relationships to ecosystems where the quantity of water can vary on short timescales with respect to plant water status, and over longer timescales and over larger areas due to structural changes in vegetation. As a proof of concept, plot‐scale aboveground vegetation PV curves were generated from equilibrium (e.g., predawn) water potentials and water content of the above ground biomass of nine plots, including tropical rainforest, savanna, temperate forest, and a long‐term Amazonian rainforest drought experiment. Initial findings suggest that the stored water and ecosystem capacitance scale linearly with biomass across diverse systems, while the relative values of ecosystem hydraulic capacitance and physiologically accessible water storage do not vary systematically with biomass. The bottom‐up scaling approach to ecosystem water relations identified the need to characterise the distribution of water potentials within a community and also revealed the relevance of community‐level plant tissue fractions to ecosystem water relations. We believe that this theory will be instrumental in linking our detailed understanding of biophysical processes at tissue‐scale to the scale at which land surface models operate and at which tower‐based, airborne and satellite remote sensing can provide information. [ABSTRACT FROM AUTHOR]

Cultivated peatlands are increasingly regarded as hot spots due to climate change and other environmental concerns. Flexible water management, such as controlled drainage, is proposed to optimize cultivation and reduce environmental risks in peatland fields. The hydrological and environmental implications of controlled drainage depend on site‐specific variables, and it is unclear how controlled drainage should be implemented in various conditions. Simulation models are a promising approach to systemically study the field hydrology, as models can capture the complete water balance, which is difficult through experimental studies alone. We calibrated and validated the spatially distributed model FLUSH to describe the hydrology of a field block with a shallow peat cover and controlled drainage in central western Finland. The objectives were to analyze the hydrological effects of controlled drainage and detect hydrological connections between the field and an adjacent upslope forest area. The results showed how inflow from the forest can induce high observed drain discharge but impacted the block groundwater tables only in the proximity of the forest (distance <25 m). The effect of controlled drainage on groundwater tables was on average 0.15 m and seasonally varying. Controlled drainage reduced drain discharge, and the reduction was larger with the forest area included in the model. While controlled drainage effects on groundwater levels and soil moisture were insensitive to groundwater influxes from adjacent areas, the water balance impact highlights the role of hydrological connections in the hydrology of cultivated peatlands under controlled drainage. Core Ideas: Combined with limited dataset, FLUSH provided a transparent tool to quantify the hydrology of a cultivated peatland.Hydrological connections had a strong impact on the field water balance but not on groundwater tables.Controlled drainage reduced groundwater table depths on average 0.15 m but had a small impact on peat moisture.Complex interactions within peatlands are a challenge for reproducing point observations with a model. Plain Language Summary: Sustainable agriculture on peatlands requires water management that allows sufficient removal of excess water when needed by the cultivation practices while minimizing the negative environmental impacts of draining peatlands, especially carbon dioxide emissions and nutrient leaching. One promising method for this is controlled drainage, a subsurface drainage method that enables controlling the amount of drainage, but more research is needed to better understand how cultivated peatlands respond to it. In this study, a computational model FLUSH was used to describe the flow and storage of water and how controlled drainage affects them on a cultivated peatland field in central western Finland. The model supported our hypothesis that the large observed drain discharge was caused by groundwater flow from an adjacent forest area. According to the model, the controlled drainage scheme that was applied in the field raised groundwater tables on average 0.15 m and reduced drain discharge. [ABSTRACT FROM AUTHOR]

The moisture‐enabled electricity generators (MEGs) represent an appealing clean power strategy within the realm of hydrovoltaic technology. However, their stable operation under all‐weather conditions is still challengeable due to the effect of ambient humidity. Herein, a self‐water‐storage MEGs with multifunctionality was developed. Gel textiles are employed as the proton‐release layer, while graphite fibers loaded with tin‐bismuth alloy liquid metal serve as water‐storing electrode. The gel textiles absorb water permeating down from the electrodes, releasing protons, which then react with the active electrode to enhance output performance. The experiment demonstrates that a device can generate a voltage of 0.55 V and a current of 7.08 μA after water storage, with a maximum power density of 1.14 μW·cm‐². The device's performance output can remain stable within a wide temperature range. The experiment confirms that the electrical output of the device originates from ionic diffusion, while density functional theory (DFT) and molecular dynamics (MD) calculations reveal the importance of the strong hydrolysis ability of the polymer and the high adsorption energy of the liquid metal for H3O+ on the output performance of the device. Furthermore, the integrated device has demonstrated effective applications in driving microelectronic devices, charging capacitors and, self‐powered health monitoring. [ABSTRACT FROM AUTHOR]

Prussian blue analogues (PBAs) are a class of promising materials for capacitive deionization. However, the kinetic mismatch between their slow ion storage rate and the demand from short‐time desalination severely limits their desalination performance. Here, a group of structure‐tuneable Ni‐Mn PBAs have been developed by a combination strategy of surface‐protected chemical etching and Ostwald ripening to study their ion storage kinetics. Treating them as demos, the characterizations and investigations, e.g., in situ XRD in a three‐electrode system, dynamic impedance, finite element simulation, and DFT calculations etc., reveal that the slow ion diffusion caused by the severe agglomeration of the nanoparticles and the unsuitable lattice parameter controls the final desalination behavior. Therefore, the correspondingly optimized sample (HC‐t) possessing a microscale hollow structure, nanoscale shell thickness, and expanded lattice, displays a fast ion storage kinetics with the ratio of surface‐controlled current as high as 82% at a scan rate of 20 mV s−1. Consequently, it delivers an impressive desalination capacity of 120.8 mg g−1 (2.06 mmol g−1 Na+) with a fast average desalination rate of 0.25 mg g−1 s−1 (0.004 mmol g−1 s−1) at 1.2 V, competitive with those reported in the literature. Moreover, the elucidation of the structure‐performance correlation provides valuable insights for the development and design of next‐generation PBAs for capacitive deionization (CDI). [ABSTRACT FROM AUTHOR]

ABSTRACT Identifying the physiological mechanisms by which plants are adapted to drought is critical to predict species responses to climate change. We measured the responses of leaf hydraulic and stomatal conductances (Kleaf and gs, respectively) to dehydration, and their association with anatomy, in seven species of California Ceanothus grown in a common garden, including some of the most drought‐tolerant species in the semi‐arid flora. We tested for matching of maximum hydraulic supply and demand and quantified the role of decline of Kleaf in driving stomatal closure. Across Ceanothus species, maximum Kleaf and gs were negatively correlated, and both Kleaf and gs showed steep declines with decreasing leaf water potential (i.e., a high sensitivity to dehydration). The leaf water potential at 50% decline in gs was linked with a low ratio of maximum hydraulic supply to demand (i.e., maximum Kleaf:gs). This sensitivity of gs, combined with low minimum epidermal conductance and water storage, could contribute to prolonged leaf survival under drought. The specialized anatomy of subg. Cerastes includes trichomous stomatal crypts and pronounced hypodermis, and was associated with higher water use efficiency and water storage. Combining our data with comparative literature of other California species, species of subg. Cerastes show traits associated with greater drought tolerance and reliance on leaf water storage relative to other California species. In addition to drought resistance mechanisms such as mechanical protection and resistance to embolism, drought avoidance mechanisms such as sensitive stomatal closure could contribute importantly to drought tolerance in dry‐climate adapted species. [ABSTRACT FROM AUTHOR]

The shortage of decades‐long continuous measurements of ecosystem processes limits our understanding of how changing climate impacts forest ecosystems. We used continuous eddy‐covariance and hydrometeorological data over 2002–2022 from a young Douglas‐fir stand on Vancouver Island, Canada to assess the long‐term trend and interannual variability in evapotranspiration (ET) and transpiration (T). Collectively, annual T displayed a decreasing trend over the 21 years with a rate of 1% yr−1, which is attributed to the stomatal downregulation induced by rising atmospheric CO2 concentration. Similarly, annual ET also showed a decreasing trend since evaporation stayed relatively constant. Variability in detrended annual ET was mostly controlled by the average soil water storage during the growing season (May–October). Though the duration and intensity of the drought did not increase, the drought‐induced decreases in T and ET showed an increasing trend. This pattern may reflect the changes in forest structure, related to the decline in the deciduous understory cover during the stand development. These results suggest that the water‐saving effect of stomatal regulation and water‐related factors mostly determined the trend and variability in ET, respectively. This may also imply an increase in the limitation of water availability on ET in young forests, associated with the structural and compositional changes related to forest growth. Summary statement: Water‐saving effect of stomatal regulation and water‐related factors mostly determined the trend and variability in ET, respectively. Increased drought response of ET may imply an increased limitation of water availability on ET in a young forest as it undergoes structural and compositional changes. [ABSTRACT FROM AUTHOR]

Distribution and change of freshwater resources is often simulated with global hydrological models. However, owing to process representation limitations and forcing data uncertainties, these model simulations have shortcomings. Combining them with observations via data assimilation, for example, with data from the Gravity Recovery and Climate Experiment (GRACE) mission or streamflow measured at in situ stations is considered to improve the realism of the simulations. We assimilate gridded total water storage anomaly (TWSA) from GRACE into the WaterGAP Global Hydrology Model (WGHM) over the Mississippi River basin via an Ensemble Kalman Filter. Our results agree with previous studies where assimilating GRACE observations nudges TWSA simulations closer to the observations, reducing the root mean square error (RMSE) by 21% compared to an uncalibrated model. However, simulations of streamflow show degeneration at more than 90% of all gauge stations for metrics such as RMSE and correlations; only the annual phase of simulated streamflow improves at half the stations. Therefore, for the first time, we instead assimilated streamflow observations into the WGHM, which improved simulated streamflow at up to nearly 80% of the stations, with normalized RMSE showing improvements of up to 0.1, while TWSA was well‐simulated in all metrics. Combining both approaches, that is, jointly assimilating GRACE‐derived TWSA and streamflow observations, leads to a trade‐off between a good fit of both variables albeit skewed to the GRACE observations. Overall, we speculate that our findings point to limitations of process representation in WGHM hindering consistent flux simulation from the storage history, especially in dry regions. Plain Language Summary: The distribution of freshwater on Earth can be simulated by hydrological models like the WaterGAP Global Hydrology Model (WGHM). Changes of the total water storages can also be derived from satellite gravimetry, for example, with the Gravity Recovery and Climate Experiment (GRACE) mission. Model and observations do not necessarily agree and are both prone to errors. We combined the model and observations over the Mississippi River basin taking the errors into account by applying a data assimilation technique. This led to a more realistic simulation of the total water storage changes. Since simulated streamflow was found to degenerate at nearly all gauge stations, we assimilated streamflow observations (instead of GRACE data) into the WGHM. This turned out to improve simulated streamflow at up to nearly 80% of the stations. The assimilation of both data sets (GRACE and streamflow) together leads to a compromise between the TWSA and the streamflow simulations, with the GRACE data affecting the simulations more than the streamflow data. Our findings are expected to aid reducing the weaknesses of the WGHM model equations and thus contribute to improved quantification of Earth's freshwater distribution in the future. Key Points: GRACE assimilation improves the WGHM storage representation, but degrades the simulated streamflow at up to 99% of the gauge stationsAssimilating streamflow data instead improves the streamflow simulations at up to 79% of the validation stationsJoint assimilation leads to a trade‐off between a good fit of simulated storages and streamflow albeit skewed to the GRACE observations [ABSTRACT FROM AUTHOR]

Objectives Materials and Methods Results Conclusions Clinical Significance To evaluate the influence of preheating and water storage on color, whiteness, and translucency of one‐shaded, group‐shaded, and multi‐shaded resin‐based composites.A total of 270 samples were fabricated from one‐shaded (Omnichroma), group‐shaded (Optishade MD), and multi‐shaded (Enamel Plus HRI) resin‐based composites and divided into six groups: no heating (control) and 1–5 heatings (test) (n = 15). The CIE L*, a*, b*, C*, and h° color coordinates were registered over white, black, and gray backgrounds with a spectrophotometer before and after 24, 72, 144 h water storage. Kruskal–Wallis test was used to evaluate differences in the color coordinates, whiteness index, and translucency parameter (α = 0.05). The Mann–Whitney U test was performed with a Bonferroni correction (p < 0.001) for pairwise comparisons. CIEDE2000 color, whiteness, and translucency differences between groups and subgroups were evaluated using their respective 50%:50% perceptibility and acceptability thresholds.Preheating influenced color coordinates, whiteness index, and relative translucency parameters (p < 0.001). Although color differences were below the acceptability threshold for all materials, translucency differences were above the acceptability threshold for Omnichroma. Water storage significantly influenced the whiteness index of Optishade MD (p < 0.001).Preheating had a more considerable effect on the color, whiteness, and translucency of one‐shaded resin‐based composite, while water storage influenced group‐shaded resin‐based composite more importantly.Preheating influences the color, whiteness level, and translucency of the resin‐based composites, but the variations remain generally clinically acceptable; however, practitioners should be aware that the modifications in translucency for one‐shaded resin‐based composites could be more important than for group‐shaded and multi‐shaded resin‐based composites. [ABSTRACT FROM AUTHOR]

BACKGROUND: The ridge‐furrow rainwater harvesting system (RFRH) is an advanced farmland management technology that plays a vital role in making full use of rainwater resources. However, it is not clear that RFRH affects crop yield and water use efficiency (WUE) by regulating soil water storage (SWS). Therefore, the present study conducted a meta‐analysis to make a large compilation of previous studies and indirectly quantify the impact of RFRH on crop yield and WUE by analysing the effect of RFRH on SWS. RESULTS: The results showed that RFRH improved crop yield and WUE by 26.71% and 25.86%, respectively, by increasing SWS by 3.93% compared to the traditional flat cultivation. RFRH had a significant effect on increasing crop yield and WUE and improving SWS. A low ridge‐furrow ratio and ridge‐furrow mulching were recommended to obtain positive effects on crop yield and WUE when potatoes are grown in areas with high precipitation (600–800 mm). Furthermore, when nitrogen fertilization is applied during the crop growth period, we also found that a medium nitrogen fertilizer rate is recommended to achieve a significant positive effect on crop yield and WUE. Importantly, a win‐win analysis showed the proportions of various groups in the target zone (zone I) to determine the appropriate strategy for RFRH of crops. CONCLUSION: The present study provides a scientific reference for the future application of the RFRH. The study provides scientific recommendations on crop types, ridge‐furrow configurations, plastic mulching patterns and nitrogen fertilizer rate for future RFRH applications. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

The Salton Sea has experienced significant recession over the past two decades due to changes in the diversion of Colorado River water to the Salton Trough for agricultural irrigation. As a result, wetlands have emerged in some exposed playa areas along the Salton Sea, primarily in regions with extensive agricultural return flows and agricultural drainage. One notable wetland system, known as the Bombay Beach Wetlands, has formed on the north shore of the Salton Sea, in an area devoid of agriculture. In many other areas with limited or no agriculture, wetlands have failed to develop, leaving exposed playa surfaces as the Salton Sea recedes. These dry playa surfaces pose a significant threat to the health of local residents due to the presence of toxins contained in windblown dust associated with playa deposits. In this study, stable water isotope data, combined with other hydrological information, led to identification of two potential water sources for the Bombay Beach Wetlands. The first possibility proposes that thermal artesian waters alone contribute to the wetlands' water source, while the second hypothesis involves a combination of drainage from Salton Sea bank storage water mixing with the thermal artesian water. The thermal artesian water discharges into drainage channels that flow towards the Bombay Beach Wetlands, initially devoid of possible groundwater baseflow until reaching the wetlands. Studies were subsequently done along the full reach of the drainage channels receiving thermal artesian water. Dissolved solids content, P and N nutrients, arsenic, and stable water isotopes were tested synoptically along the drainage channels. Channel investigations led to the development of a novel model of salinization, which is linked to channel discharge, channel morphometrics, and channel incision. [ABSTRACT FROM AUTHOR]

Water management is important for addressing the challenges posed by next‐generation fuel cell electric vehicles. Although X‐ray imaging techniques are useful for probing the mechanism of water transport in the gas diffusion layer of polymer electrolyte fuel cells, they cannot be easily applied to the Pt‐loading catalyst layer because of its low X‐ray transmittance due to the high absorption coefficient of Pt. Herein, a method to realize the high‐resolution X‐ray imaging of a 30 μm‐thick cathode catalyst layer in polymer electrolyte fuel cells using synchrotron X‐ray radiography is proposed, thus bridging the above gap. The results of operando synchrotron X‐ray radiography measurements reveal that water accumulation in the cathode catalyst layer depends on the cell temperature, feed gas humidity, and cell voltage, while time‐slice analysis shows that the water accumulation rate in the cathode catalyst layer immediately after the power generation is faster than that in the cathode gas diffusion layer. The proposed imaging method can be used to evaluate the water storage capacity of the catalyst layer and thus deepen the understanding of flooding phenomena and cold‐start behavior at subzero temperatures. [ABSTRACT FROM AUTHOR]

Land surface models (LSMs) are used to simulate the terrestrial component of water, energy, and biogeochemical cycles. These simulations are useful for water resources management, drought and flood prediction, and numerical climate/weather prediction. However, the usefulness of LSMs are dependent by their ability to reproduce states and fluxes realistically. Accurate measurements of water storage are useful to calibrate and validate LSMs outputs. Geological weighing lysimeters (GWLs) are instruments that can provide field‐scale estimates of integrated total water storage within a soil profile. We use field estimates of total water storage and subsurface storage to critically evaluate two different land surface models: the Modélisation Environnementale communautaire—Surface Hydrology (MESH) which uses the Canadian Land Surface Scheme (CLASS), and the Structure for Unifying Multiple Modeling Alternatives: (SUMMA). These models have differences in how the processes and properties of the land surface are represented. We attempted to parameterize each model in an equivalent manner, to minimize model differences. Both models were able to reproduce observations of total water storage and subsurface storage reasonably well. However, there were inconsistencies in the simulated timing of snowmelt; depth of soil freezing; total evapotranspiration; partitioning of evaporation between soil evaporation and evaporation of intercepted water; and soil drainage. No one model emerged as better overall, though each model had specific strengths and weaknesses that we describe. Insights from this study can be used to improve model physics and performance. [ABSTRACT FROM AUTHOR]

Understanding plant‐water relations is essential for effective regional water management and promoting ecologically sustainable development on the Loess Plateau, especially in the context of the Grain for Green project initiated in 1999. This study evaluated the variations in vegetation variables (leaf area index, enhanced vegetation index, solar‐induced chlorophyll fluorescence and gross primary production) and hydroclimatic variables (precipitation, total water storage, aridity index and standardized precipitation evapotranspiration index) from 2003 to 2020, along with their interactions across the Loess Plateau. Our analysis revealed a general increase in vegetation variables, with the largest increase observed in the forest expansion areas. Precipitation and the aridity index exhibited significant upwards trends, while total water storage showed a significant decline, particularly in the forest expansion areas. Vegetation variables were more sensitive to changes in total water storage across the Loess Plateau. In the northwest region, where large‐scale croplands and grasslands expansion occurred, vegetation variables also showed sensitivity to precipitation. Lag effect analysis revealed short time lags (1–3 months) between vegetation and hydroclimatic variables, expect for total water storage (6 months). Overall, human activities and climate factors contributed 58.4% and 41.6% to the increase in leaf area index, and 52.2% and 47.8% to the increase in gross primary production, respectively. In relatively arid environments, precipitation contributed over 50% to the observed vegetation greening. This study underscores the increasingly significant role of human activities in driving vegetation greening on the Loess Plateau, particularly in large‐scale afforestation areas. [ABSTRACT FROM AUTHOR]

Streamflow exhibits persistent decadal variability; however, it is unclear if the magnitude and spatial extent of these variabilities are symmetric for droughts and floods. Here, we examine drought‐rich and flood‐rich periods in 319 streamflow gauges in Brazil from 1940 to 2020. Drought‐ and flood‐rich periods are detected by computing annual streamflow minima and maxima time series and using scan statistics to verify if events exceeding a threshold follow a Bernoulli process. We contrast streamflow time clustering with rainfall, evaporation, water abstraction, the Atlantic Multidecadal Oscillation (AMO), and the Pacific Decadal Oscillation (PDO). We detected a higher spatial frequency of drought‐ than flood‐rich periods. For 5‐year return period thresholds, drought‐rich periods are observed in 81% of the basins, 16.7 times the false positive rate (4.8%) and 4.7 times flood‐rich periods (17%). This asymmetry is linked with sharp increases in water abstractions since the 1990s and a higher prevalence of rainfall‐poor periods (41% of gauges) compared to rainfall‐rich (22% of gauges), which we interpret as being further amplified into drought‐rich periods due to an interannual persistence of water storage deficits. Brazil experienced a dry period until the 1960s, extensive flooding in the 1980s, and record low flows from the 2000s onward. Drought and flood‐rich periods are well aligned with rainfall clustering, water abstractions, the AMO and PDO. Droughts‐rich periods are more frequent in shorter time scales (several years to one decade) and flood‐rich periods in longer time scales (a few decades). Our findings highlight the nonlinearity and asymmetry of drought and flood change at decadal scales. Key Points: We identify periods when streamflow anomalously often falls below/exceeds 5‐year return period thresholds for 319 basins in BrazilDrought‐rich periods were detected in 81% of the basins, while flood‐rich periods were detected in 17% of the basins in 1940–2020The high drought frequency is caused by increased water abstractions and long‐term rainfall deficits associated with climate modes [ABSTRACT FROM AUTHOR]

Topography is a key control on runoff generation, as topographic slope affects hydraulic gradients and curvature affects water flow paths. Simultaneously, runoff generation shapes topography through erosion, affecting landscape morphology over long timescales. Previous modeling efforts suggest that subsurface hydrological properties, relative to climate, are key mediators of this relationship. Specifically, when subsurface transmissivity and water storage capacity are low, (a) saturated areas and storm runoff should be larger and more variable, and (b) hillslopes shorter and lower relief, assuming other geomorphic factors are held constant. However, it remains uncertain whether subsurface properties can exert such strong controls on emergent properties in real landscapes. We compared emergent hydrological function and topography in two watersheds with very similar climatic and tectonic history, but very different subsurface properties due to contrasting bedrock lithology. We found that hillslopes were systematically shorter and saturated areas more dynamic at the lower transmissivity site. To test whether these features could be the result of coevolution between topography, hydrological function, and subsurface properties, we estimated all parameters of a coupled groundwater‐landscape evolution model for each site. Limitations were revealed in the model's ability to reproduce aspects of morphology and hydrologic behavior, however, model results suggested differences in hillslope length and variably saturated area between the sites could be explained by differences in subsurface properties, and not by differences in geomorphic process rates alone. This work demonstrates one way subsurface hydrology can profoundly affect landscape evolution. Plain Language Summary: In many humid landscapes, runoff is generated by water that flows through the shallow subsurface from ridges to valleys, eventually emerging and draining to rivers. When subsurface capacity to move water is greater, more water can be transported downslope before surface runoff begins. Surface water may cause erosion, which shapes topography over millions of years. We previously developed a computer model based on these principles and showed that subsurface capacity to store and transmit water affects both runoff generation and topographic evolution. Lower capacity results in more extensive surface runoff and shorter hillslopes, when all other factors are held constant. Here we tested this by comparing two watersheds that differ primarily in their bedrock composition, which affects subsurface water storage and transmissivity. We found that the low transmissivity site experienced more widespread surface runoff in response to precipitation, and had shorter hillslopes, supporting our predictions. We set up computer models for both sites, which suggested that subsurface differences are necessary to explain observed differences in runoff and topography. Finally, we discuss some key limitations of the model that could be improved upon in future attempts to understand how hydrology affects the long‐term evolution of Earth's surface. Key Points: We test theoretical predictions about the relationship between hillslope length and relief, saturated area, and transmissivityComparing two watersheds, we find lower transmissivity is associated with shorter hillslopes and larger variably saturated areasHydrogeomorphic modeling suggests that subsurface properties drive the coevolution of differences between the sites [ABSTRACT FROM AUTHOR]

Understanding the role of anthropogenic activities in the hydrological cycle is critical to support sustainable water management for the Yangtze River Basin (YRB), which experiences extensive dam operation, irrigation and water withdrawal. However, this remains challenging due to insufficient accuracies of existing process‐based models for fully depicting anthropogenic activities as part of the hydrological cycle. To this end, this study enhances a national‐scale coupled land surface‐hydrologic‐hydrodynamic model (CLHMS) with a dynamic irrigation scheme for distinct crops, an extended reservoir operation scheme incorporating both water storage anomalies and water demand anomalies, and a cost‐function‐based approach to link water demands with reservoir operation. The enhanced model is extensively validated against historical streamflow, water storage of 90 reservoirs, and irrigation water withdrawal in the YRB, and the water level and storage of the Poyang Lake (PYL). By setting up controlled experiments in the YRB, we show that the streamflow decreases by 2%–6% due to irrigation and water withdrawal, and manifests an attenuated seasonality due to reservoir operation. At the basin scale, the increasing trend of extreme flood peaks exhibits a reversal under human activities, with the flood mitigation effect of irrigation and water withdrawal accounting for up to 50% of that of reservoir operation. The hydrodynamics of the PYL also exhibits considerable human‐induced alterations, with a 1.79 m‐decrease in the water level at the end of flood season. Our study sheds light on quantifying anthropogenic hydrologic impacts at basin scales, with important implications for understanding the co‐evolution between anthropogenic activities and the hydrological cycle. Key Points: The human‐induced alterations of streamflow, extreme floods, and lake water level and storage are comprehensively analyzedA dynamic irrigation module for distinct crops is developed and coupled to reservoir operation with a cost‐function‐based approachA conceptual reservoir operation scheme is extended to include both water storage anomalies and water demand anomalies in hydrologic models [ABSTRACT FROM AUTHOR]

The spatiotemporal variability of groundwater level is an important property of peatland hydrology that directly alternates water storage. Nonetheless, the current understanding of the variations of groundwater level over long periods of time remains limited. In this study, we investigated two peatland watersheds (0.151 km2 for Watershed 1 and 0.844 km2 for Watershed 2) in the Zoige Basin in the upper watershed of the Yellow River to monitor temporal variability of groundwater level using self‐recorded water loggers over 4 years (2017–2021). The main results demonstrate that (1) groundwater level variations were controlled by gully drainage in sites adjacent to the gully but were more affected by rainfall in sites distant from the gully. The groundwater level near the gully downcut was lower than that near the gully without complete downcutting through the pear layer, with a maximum difference of 58.3 cm, indicating the longitudinal effect of groundwater level in the watershed. (2) Because the rainfall had a lag effect on the groundwater level, the length of lag gradually decreased with increased rainfall intensity (i.e., the lag time for sites distant from the gully was about 18 min shorter than that of sites close to the gully in Watershed 1). (3) The peak values of the groundwater level occurred simultaneously with the maximum and minimum rainfall in Watershed 2, and the peak occurrence time was related to the ratio of precipitation to evaporation. In the downstream sites, the groundwater level fluctuated more than the upstream ones in Watershed 2. Moreover, the average groundwater level in the upstream sites was 14.3 cm higher than that of the middle ones, indicating a decreasing trend of water storage along the gully. (4) The differences in groundwater level between wet and dry seasons were clear, but the difference was smaller in the upstream sites due to limited gully incision and higher water storage within the peat layer. Additionally, groundwater level changes were more extreme on rainy days during both the wet and dry seasons, but the different intensities of rainfall resulted in stable groundwater in the dry season and an oscillating groundwater level in the wet season in Watershed 2. This study uncovers the groundwater dynamics in the two peatland watersheds, which is of great significance for understanding runoff variation, ecohydrological processes, and wetland shrinkage. [ABSTRACT FROM AUTHOR]

By placing Apollo 17 regolith samples in a freezer, and storing an equivalent set at room temperature, NASA effectively performed a 50‐year experiment in the kinetics of natural thermoluminescence (TL) of the lunar regolith. We have performed a detailed analysis of the TL characteristics of four regolith samples: a sunlit sample near the landing site (70180), a sample 3 m deep near the landing site (70001), a sample partially shaded by a boulder (72320), and a sample completely shaded by a boulder (76240). We find evidence for a total of eight discrete TL peaks, five apparent in curves for samples in the natural state and seven in samples irradiated in the laboratory at room temperature. For each peak, we suggest values for peak temperatures and the kinetic parameters E (activation energy, i.e. "trap depth," eV) and s (Arrhenius factor, s−1). The lowest natural TL peak in the continuously shaded sample 76240 dropped in intensity by 60 ± 10% (1976 vs. present room temperature samples) and 43 ± 8% (freezer vs. room temperature samples) over the 50‐year storage period, while sunlit and partially shaded samples (70001, 70180, 72321, 72320) showed no change. These results are consistent with the E and s parameters we determined. The large number of peaks, and the appearance of additional peaks after irradiation at room temperature, and literature data, suggest that glow curve peaks are present in lunar regolith at ∼100 K and their intensity can be used to determine temperature and storage time. Thus, a TL instrument on the Moon could be used to prospect for micro‐cold traps capable of the storage of water and other volatiles. Plain Language Summary: Rocky soils, such as the regolith from the Apollo 17 landing site, glow when heated in the dark. This glow, known as thermoluminescence (TL), is caused by previous exposure to radiation, but it can fade depending on ambient temperatures. We therefore have a method for studying the radiation and thermal history of these rocks, but we need to learn the precise details of this process. Nearly 50 years ago, NASA placed some Apollo 17 regolith (a) in a metal cabinet at room temperature and (b) in a freezer. We found that freezer samples did not lose any TL, but the cabinet samples showed considerable fading relative to the freezer samples and relative to samples measured nearly 50 years ago. Combined with a detailed computer analysis of the data, these results enable us to understand the relationships between TL, time, and environmental conditions. Most importantly, this method will enable us to search for tiny locations in the polar regions of the Moon, called cold traps, where water and other volatiles may have accumulated. This information is important for understanding the history of the Moon, and it will support exploration efforts which need water and volatiles. Key Points: The thermoluminescence (TL) of Apollo 17 regolith samples has been studied. The kinetic parameters of 8 peaks present were determinedEquilibrium temperatures have been determined for four location types (2 massif, 2 valley), which agree well with independent thermometersThese results suggest that a TL instrument (hand held or robotic) could be used to locate micro‐cold traps (and volatiles) on the Moon [ABSTRACT FROM AUTHOR]

Existing approaches for predicting total water storage (TWS) rely on land surface or hydrological models using meteorological forcing data. Yet, such models are more adept at predicting specific water compartments, such as soil moisture, rather than others, which consequently impedes accurately forecasting of TWS. Here we show that machine learning can be used to uncover relations between nonseasonal terms of Gravity Recovery and Climate Experiment (GRACE) derived total water storage and the preceding hydrometeorological drivers, and these relations can subsequently be used to predict water storage up to 12 months ahead, and even exceptional droughts on the basis of near real‐time observational forcing data. Validation by actual GRACE observations suggests that the method developed here has the capability to forecast trends in global land water storage for the following year. If applied in early warning systems, these predictions would better inform decision‐makers to improve current drought and water resource management. Plain Language Summary: Traditional methods for predicting short‐term/seasonal variations in land total water storages rely on hydrological models. However, these models have a drawback—they are better at predicting water stored in specific parts of the land system like soil moisture than giving an accurate forecast for the overall integrated land total water storage. In this study, we demonstrate the applicability of machine learning in uncovering relationships between the de‐season and de‐linearized terms of global water storage variability as observed by the Gravity Recovery and Climate Experiment (GRACE) satellites, and the preceding hydrometeorological variables such as sea surface temperature. These relationships can then be utilized to forecast monthly changes in land total water storage up to 1 year ahead, and even to predict exceptional drought events based on near real‐time observational forcing data. The validation by actual GRACE observations, lends further credence to the effectiveness of the method developed here, showcasing its potential to forecast trends in global land total water storage for the upcoming year. The potential applications of these predictions in early warning systems are highly promising, and we anticipate that they can assist decision‐makers in enhancing current drought and water resource management practices. Key Points: We identify a stable lag relationship between hydrometeorological variables and the GRACE derived total water storage change (TWSC)Using this stable lag relationship, we are able to forecast the global TWSC up to 1 year ahead with solely observational data as inputsOur forecasts exhibit high consistency with actual GRACE data in terms of global mean land water storage trends for the following year [ABSTRACT FROM AUTHOR]

Summary: Freshness and physicochemical properties of myofibrillar protein (MP) from decapitated Harpiosquillid mantis shrimp (HMS) treated with pulsed electric field (PEF), followed by soaking in 2% chitooligosaccharide (D‐P‐COS) and whole HMS (U‐W) during storage in iced water of 3 days were investigated. Lower rate of increase in K‐value, freshness index, in D‐P‐COS was observed in comparison with U‐W. For physicochemical properties of MP, MP from U‐W had gradual decrease in ATPase activity throughout the storage of 3 days, whereas no drastic alteration was attained in MP from D‐P‐COS. Surface hydrophobicity and carbonyl contents of MP from both samples upsurged up to 3 days, nevertheless the increases were more pronounced in D‐P‐COS. The secondary conformation changes were affected by decapitation, treatment and storage time. Decapitation and treatments maintained freshness, however, slightly enhanced physicochemical changes of MP from HMS. [ABSTRACT FROM AUTHOR]

Understanding mass (re‐)distribution within the Earth system, and addressing global challenges such as the impact of climate change on water resources requires global time‐variable terrestrial water storage (TWS) estimates along with reasonable uncertainty fields. The Gravity Recovery and Climate Experiment (GRACE) and GRACE‐FO satellite missions provide time‐variable gravity fields with full variance‐covariance information. A rigorous uncertainty propagation of these errors to TWS uncertainties is mathematically challenging and computationally inefficient. We propose a Monte Carlo Full Variance‐Covariance (MCFVC) error propagation approach to precisely compute TWS uncertainties. We also establish theoretical criteria to predict the actual convergence and accuracy of MCFVC, showing a convergence after 10,000 realizations with the relative error of 2.8% for variance and 4.7% for covariance at the confidence level of 95%. This can be achieved in few seconds using a single CPU to compute the uncertainties of each 1° resolution globally gridded TWS field. A validation against the rigorous error propagation method indicates relative differences of less than 0.8%. A global uncertainty assessment shows that neglecting the covariance of gravity coefficients can considerably bias the TWS uncertainties, that is, up to 60%, in some basins like Eyre. Flexibility of MCFVC allows the quantification of filtering impacts on the uncertainty of TWS fields, for example, up to 35% in the Tocantins River Basin. An empirical model is provided to reproduce GRACE‐like TWS uncertainty fields for hydrological studies. Finally, experiments of GRACE(‐FO) data assimilation for hydrological applications and sea‐level budget estimation are presented that indicate the importance of accounting for the full covariance information in these studies. Plain Language Summary: Terrestrial water storage (TWS) represents a vertical summation of all available water of the Earth's land surface including groundwater, soil layers, surface water, and snow. Therefore, understanding TWS changes is essential for hydrological assessments. Satellite gravity missions such as the Gravity Recovery and Climate Experiment (GRACE) and its Follow‐On mission (GRACE‐FO) provide a unique opportunity to estimate TWS globally. However, to use GRACE(‐FO)‐like TWS estimates in geophysical and hydrological studies, one needs to quantify their uncertainties reliably at desired spatial resolution. In this study, an efficient statistical Monte Carlo method is presented, which provides high flexibility of processing choices, and satisfies the accuracy needed to compute the uncertainties of GRACE(‐FO)‐like TWS products. The simplicity and generality of this method also allows it to be easily integrated in the operational data post‐processing chains. We demonstrate the importance of the full covariance estimation of TWS products in hydrological data assimilation and sea‐level budget estimation studies. Key Points: A Monte Carlo Full Variance‐Covariance method is proposed to propagate errors of GRACE(‐FO) level‐2 data, where the covariance is hardly reached by traditional methodsConsidering full variance‐covariance information and filtering methods have considerable impacts on the uncertainty of GRACE(‐FO)‐like terrestrial water storage (TWS) fieldsAn empirical model is provided to account for spatial‐temporal variation of GRACE(‐FO)‐like TWS uncertainty fields, and for hydrological studies [ABSTRACT FROM AUTHOR]

Canopy rainfall interception is one key hydrological process, affecting rainwater redistribution and effectiveness in semiarid regions. Canopy rainfall interception loss is jointly influenced by meteorology, vegetation and topography. The canopy water storage capacity (S), rainfall interception depth (Im) and ratio (I%) and vegetation characteristics, together with topographic factors of three grassland communities (dominated by Bothriochloa ischaemum, Lespedeza davurica and Artemisia gmelinii, respectively) were investigated on the Loess Plateau of China during the main growing season (June to September). Results showed that Im ranged from 0.55 to 0.89 mm and I% ranged from 6.14% to 12.1%, with the maximum values occurring in August for three communities, and A. gmelinii community had the largest Im (0.89 mm) and I% (12.1%). The Im and I% were positively correlated with aboveground biomass (AGB), coverage (Cov), leaf area index (LAI), community‐weighted mean height (CWMH) and altitude (Alt), but negatively correlated with slope degree and rainfall intensity (RI). Hierarchical partitioning analysis (HPA) showed that AGB had the highest contribution for Im (20.3%), while Alt had the highest contribution for I% (18.2%). The regression models based on forward selection could effectively predict the values of Im (R2 = 0.802, RMSE = 0.049) and I% (R2 = 0.546, RMSE = 1.434). Topographic factors (altitude, slope degree and aspect) indirectly influenced both Im and I% by modulating vegetation characteristics (AGB, Cov, etc.). All these indicated that aboveground biomass mainly determines grassland community rainfall interception loss in the semiarid Loess Plateau. [ABSTRACT FROM AUTHOR]

Regional floods result from various flood generation mechanisms. Traditional analyses mainly link flooding to extreme rainfall, with limited input from soil moisture. Total water storage (TWS) is a holistic measure of basin wetness, including additional storage components from surface water, snow, and groundwater. Utilizing a new 5‐day Gravity Recovery and Climate Experiment and its Follow On (GRACE(‐FO)) data set, we investigated the linkage between short‐term TWS anomaly (TWSA) and regional flooding. The 5‐day TWSA solutions revealed flood signals missed by monthly TWSA solutions. Global basins exhibit distinct storage‐discharge co‐evolution patterns, offering new insights into flood mechanisms and propensity. Our bivariate event analyses show the annual maximum river discharges co‐occur more often with the TWSA maxima than with precipitation in many basins. Further analyses revealed TWSA's time‐lagged effect on river discharge, particularly in basins susceptible to floods triggered by saturation‐excess runoff. The 5‐day TWSA provides a new source of information for enhancing global flood preparedness. Plain Language Summary: Every year, floods impact millions of people and result in significant economic losses. Despite this, we still face uncertainties in flood forecasting. In this study, we evaluated a new 5‐day total water storage anomaly (TWSA) data set derived from the Gravity Recovery and Climate Experiment (GRACE) satellites for flood early warning. TWSA measures changes in snow, surface water, soil moisture, and groundwater storage, providing a direct way to assess how wet an area is. The 5‐day TWSA data help us understand for the first time the connection between sub‐monthly TWSA and regional river flooding. Our findings show the 5‐day TWSA reveals new flood signals that were previously not captured using monthly TWSA data. This finding fills a crucial gap in global flood early warning systems, helping us better predict and prepare for floods. Key Points: We investigated the role of total water storage anomalies (TWSA) in mediating regional flooding using a new 5‐day GRACE(‐FO) data setRegional floods tend to have higher number of joint occurrences with the 5‐day TWSA extremes than with precipitation extremesThe causal strength of 5‐day TWSA on river discharge is relatively high, suggesting its potential value for flood early warning [ABSTRACT FROM AUTHOR]

Drought occurs when there is a sustained decrease in rainfall over an extended period, impacting the socio‐cultural and environmental aspects of humans and other living beings. The geographic distribution and timing of droughts play a crucial role in drought management and mitigation strategies. Identifying and predicting the onset of droughts in specific regions, especially in watershed areas, is a primary concern in the field of hydrology. This study focuses on how the spatiotemporal patterns of drought are developing in Turkish Basins using detailed data on Terrestrial Water Storage (TWS), precipitation, and temperature at the pixel level. GRACE (Gravity Recovery and Climate Experiment), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), and WorldClim (World Climate) data sets are employed to assess long‐term changes of drought on a basin‐scale. Spatial analyses are conducted in a Geographic Information System (GIS) environment for the derivation of basinal monthly mean, minimum, and maximum statistics of TWS, precipitation, and temperature anomalies within Turkish Basins. Time series analyses are implemented to investigate the temporal evolution of droughts in these basins, for the basinal monthly mean, minimum, and maximum statistics obtained. The Mann–Kendall trend test and Pettitt change point detection tests are used to assess the statistical significance of the calculated trends and to expose the existence of any change point therein, respectively. The findings of the study indicate that Turkiye faces a significant risk of drought development in nearly all its basins, particularly after 2016. The GRACE dataset provides realistic insights into the temporal behaviour of hydrological droughts. PERSIANN is effective in identifying years with extreme meteorological conditions, and the standardized precipitation index (SPI) shows similar effectiveness, while they are ineffective in exposing significant trends due to the nature of the precipitation data. WorldClim data proves insufficient for modelling the temporal behaviour of droughts in these basins. [ABSTRACT FROM AUTHOR]

The abnormal deformation of urban surfaces threatens the human living environment, and extreme regional weather can affect the response law of surface deformation. To explore the changes in surface time series response after extreme weather and the causes of deformation in Zhengzhou, the MTInSAR was used to obtain the surface deformation from 2020 to 2022, and the time series changes of groundwater equivalent water height were retrieved by GRACE. The results show that: (a) There are three large subsidence bowls in Zhengzhou, and the maximum subsidence rate is −40.2 mm/yr. (b) The extreme rainstorm in Zhengzhou alleviated the surface deformation quickly, lasting approximately 6 months. However, surface subsidence still occurred after the extreme rainstorm. The water storage coefficient of the elastic skeleton in the Zhengzhou area showed an increasing trend. (c) Precipitation can lead to surface uplift by influencing the change of groundwater level. There is a delay time of 0.75–1 month between groundwater level change and surface uplift response in the characteristic region. These results provide scientific data support and causal analysis for disaster prevention and reduction of abnormal deformation in Zhengzhou. Key Points: Impact of extreme rainstorm on surface in Zhengzhou from 2020 to 2022There are three subsidence bowls in Zhengzhou area, and the uneven deformation is obviousExtreme rainstorm weather can alleviate the occurrence of surface subsidence in short term [ABSTRACT FROM AUTHOR]

Climate change will impact mountain watershed streamflow both directly—with changing precipitation amounts and variability—and indirectly—through temperature shifts altering snowpack, melt, and evapotranspiration. To understand how these complex processes will affect ecosystem functioning and water resources, we need tools to distinguish connections between water sources (rain/snowmelt), groundwater storage, and exit fluxes (streamflow/evapotranspiration), and to determine how these connections change seasonally and as climate shifts. Here, we develop novel watershed‐scale approaches to understand water source, storage, and exit flux connections using a dynamic‐flux particle tracking model (EcoSLIM) applied in California's Cosumnes Watershed, which connects the Sierra Nevada and Central Valley. This work develops new visualizations and applications to provide mechanistic understanding that underpins the interpretation of isotopic field data at watershed scales to distinguish sources, flow paths, residence times, and storage selection. In our simulations, streamflow comes primarily from snow‐derived water while evapotranspiration generally comes from rain. Most streamflow starts above 1,000 m while evapotranspiration is sourced relatively evenly across the watershed and is generally younger than streamflow. Modeled streamflow consists primarily of water sourced from precipitation in the previous 5 years but before the current water year, while ET consists primarily of water from precipitation in the current water year. ET, and to a lesser extent streamflow, are both younger than water in groundwater storage. However, snowmelt‐derived streamflow preferentially discharges older water from snow‐derived storage. Dynamic‐flux particle tracking and new approaches presented here enable novel model‐tracer comparisons in large‐scale watersheds to better understand watershed behavior in a changing climate. Plain Language Summary: Climate change will alter the hydrologic cycle: storms will be more intense and higher temperatures will result in less snow, earlier melt, and more early‐season water use by plants. To understand the combined effect of these changes, we need models to simulate water flows and precisely study the effects of rain, snowmelt, subsurface storage, streamflow, and plant water uptake. These models can show us how water flows between different components of the landscape, and how this flow changes seasonally and in response to climate change. This study develops new modeling tools that simulate the Cosumnes River in the Sierra Nevada (California, USA) and will help interpret field data at watershed scales. The majority of simulated streamflow originates from snow while plants rely on rainfall for evapotranspiration (ET). Most streamflow starts as precipitation above 1,000 m while ET comes from water that falls evenly across the watershed. Water used for ET is generally younger than streamflow: most streamflow is 1–5 years old, while most ET is less than 1 year old. Both ET and streamflow are younger than water stored in the subsurface. These new modeling tools, called dynamic‐flux particle tracking, will help us predict how watersheds will behave as climate changes. Key Points: Dynamic‐flux particle tracking (DFPT) is a novel technique to investigate watershed function and connectionWater source wedge plots illustrate connectivity between source water elevation, precipitation phase, and water exit pathways and locationDFPT shows how source phase and watershed conditions influence age‐ranked storage selection behavior [ABSTRACT FROM AUTHOR]

The Gravity Recovery and Climate Experiment (GRACE) satellite provides valuable data to monitor groundwater variation. This study investigates groundwater dynamics in India during deficit and excess monsoon years. It finds possible interactions by exploring how groundwater levels respond to meteorological and hydrological conditions. Groundwater anomalies (GWA) are estimated using Terrestrial Water Storage Anomalies (TWSA) from GRACE and hydrological parameters from the Global Land Data Assimilation System (GLDAS). The statistical analysis reveals that the standard deviation is higher for hydrological parameters in summer (June to September), whereas it is higher in fall (October to November) for GWA in many parts of India. The modulation in hydrological parameters subsequently impacts GWA during the following seasons. The study reports increased groundwater across many parts of India during fall. However, groundwater decline is closely linked to unsaturated soil moisture from fall to winter (December to February). Groundwater storage is at its lowest levels during the following spring (March to May) over India. Four deficit and three excess summer monsoons occurred from 2002 to 2022. Composite analysis of GWA reveals that there is replenishment (depletion) of groundwater during the following fall and winter in excess (deficit). The analysis based on extreme deficit monsoon years (2002, 2009) and excess monsoon year (2019) reveals that during the deficit years, groundwater recharge is less, and excess monsoon year it is more; and is vice versa for GWA. The study reports decline in GWA during the summer 2019, which is attributed to the hydrological conditions of the preceding year and modulation of the hydrological processes. As climate variability and water scarcity become increasingly pressing issues, understanding the relationships between hydrological factors and groundwater dynamics is essential for ensuring sustainable water use and resilience to extremely varying climate conditions. [ABSTRACT FROM AUTHOR]

Satellite remote sensing enhances model predictions by providing insights into terrestrial and hydrological processes. While data assimilation techniques have proven promising, there is a lack of standardized and effective approaches for integrating multiple observations simultaneously. This study presents a novel assimilation framework, the multi‐observation local ensemble‐Kalman‐filter (MoLEnKF), designed to effectively integrate multiple variables, even at scales different than the model. Evaluation of MoLEnKF in the Amazon River basin includes assimilation experiments with remote sensing data only, including water surface elevation (WSE), terrestrial water storage (TWS), flood extent (FE), and soil moisture (SM). MoLEnKF demonstrates improvements in a scenario where regions lack in‐situ hydroclimatic records and when assuming uncertainties of large‐scale hydrologic‐hydrodynamic models. Assimilating WSE outperforms daily discharge and water‐level estimations, achieving 38% and 36% error reduction, respectively. However, the monthly evapotranspiration estimate achieves the greatest error reduction by assimilating SM with 11%. MoLEnKF always remains in second position in a ranking of error and uncertainty reduction, providing an intermediate condition, being able to holistically outperform univariate experiments. MoLEnKF also outperform state‐of‐the‐art models in many cases. This study suggests potential improvements, urging exploration of correlations between assimilated variables and adaptive localization methods based on seasonality. The flexibility and the elegant way of expressing the LEnKF equations by MoLEnKF facilitates their application with different types of variables, compatible with large‐scale hydrologic‐hydrodynamic models and missions such as SWOT. Its robustness ensures easy replicability worldwide, facilitating hydrological reanalysis and improved forecasting, establishing MoLEnKF as a valuable tool for the scientific community in hydrological research. Plain Language Summary: The use of satellites to collect information from far away helps us to understand how water behaves on the continents. But combining all this data with uncertain computer models is complicated. This study introduces a new method called multi‐observation local ensemble‐Kalman‐filter (MoLEnKF) to combine many different kinds of data at once. We tested MoLEnKF in the Amazon River basin, using satellite data on water levels, terrestrial water storage, flood extent and soil moisture. MoLEnKF by using all these observations at the same time obtained better results holistically than the individual experiments, improving our ability to predict aspects such as the amount of discharge, water level and evapotranspiration. This study is a step forward and could be really useful for understanding and predicting water‐related phenomena worldwide, especially in a context of scarce or no availability of in‐situ observations. Key Points: Multi‐observation local ensemble‐Kalman‐filter (MoLEnKF) advances multi‐observation and multi‐scale assimilation, overcoming holistically univariate experimentsMoLEnKF improves the simulation of large‐scale hydrologic‐hydrodynamic uncertain models using only remotely sensed dataMoLEnKF flexibility for global applications: Simplicity and compatibility with various data types make it a robust tool, for example, SWOT mission [ABSTRACT FROM AUTHOR]

Across diverse biomes and climate types, plants use water stored in bedrock to sustain plant transpiration. Bedrock water storage (Sbedrock), in addition to soil moisture, thus plays an important role in water cycling and should be accounted for in the context of surface energy balances and streamflow generation. Yet, the extent to which bedrock water storage impacts hydrologic partitioning and influences latent heat fluxes has yet to be quantified at large scales. This is particularly important in Mediterranean climates, where the majority of precipitation is offset from energy delivery and plants must rely on water retained from the wet season to support summer growth. Here we present a simple and modified water balance approach to quantify the role of Sbedrock on controlling hydrologic and energy partitioning. Specifically, we tracked evapotranspiration in excess of precipitation and mapped soil water storage capacity (Ssoil, mm) across the western US in the context of Budyko's water partitioning framework. Our findings indicate that Sbedrock is necessary to sustain plant transpiration across forests in the Sierra Nevada—some of the most productive forests on Earth—as early as April every year, which is counter to the current conventional thought that bedrock is exclusively used late in the dry season under extremely dry conditions. We found that the proportion of water that returns to the atmosphere would decrease dramatically without access to Sbedrock. When converted to latent heat energy, the median monthly flux associated with evapotranspiration of Sbedrock can exceed 100 W/m2 during the dry season. Plain Language Summary: Plants frequently use water stored in bedrock (Sbedrock) in order to grow. However, the proportion of precipitation that returns to the atmosphere (evapotranspiration) versus to streams (runoff), and the amount of latent heat—the energy associated with evaporating water—used as a result of access to Sbedrock has not been measured. In Mediterranean climates, such as parts of the western US, the majority of energy (sunlight) is received during the dry season and plants must rely on water stored belowground during the wet season to sustain summer growth. In this study, we present two methods for calculating how much Sbedrock influences the amount of water returning to the atmosphere versus streams and what that corresponds to in terms of latent heat energy at the surface. We use gridded data to compare the amount of water entering (precipitation) and exiting (evapotranspiration) the area and use a mapped soil water storage capacity product to draw conclusions about the timing and magnitude of plant transpiration that is a result of access to bedrock water. Our findings indicate that some of the Earth's most productive forests use Sbedrock early in the growing season, consuming over 100 W/m2 of latent heat energy in the summer. Key Points: Plant use of bedrock storage impacts water partitioning in seasonally dry climatesIn many parts of the western United States, root‐zone storage deficits do not reset annuallyPlants may exhaust soil water storage and require bedrock water as early as April each year [ABSTRACT FROM AUTHOR]

High‐frequency monitoring of reservoir inundation and water storage changes is crucial for reservoir functionality assessment and hydrological model calibration. Although the integration of optical data with synthetic aperture radar (SAR) backscattering coefficients (backscatters) offers an effective approach, conventional methods struggle to consistently provide accurate retrievals over diverse regions and seasons. In this study, we introduce reservoir‐ and monthly‐specific classification models to enhance the integration of Sentinel‐1 SAR backscatters with optical‐based water dynamics. Our method covers 721 reservoirs with a capacity greater than 0.1 km3 in China during 2017–2021. Furthermore, we leverage multisource satellite altimetry records (e.g., ICESat‐2, CryoSat‐2, and GEDI) and digital elevation models to derive hypsometry relationship (i.e., water level–water area relationship) for reservoirs, enabling the transformation of inundated areas into monthly water storage changes for 662 reservoirs, representing 93% of the total storage capacity of large reservoirs. Validation against in‐situ measurements at 80 reservoirs reveals improved monthly inundated area monitoring compared to existing data sets. Additionally, our reservoir water storage change estimates exhibit an average R2 of 0.79 and a mean relative root mean square error (rRMSE) of 21%. Our findings highlight reservoir water increases from May/June to November and declines in winter–spring in most regions. However, the inter‐annual patterns vary among regions, with increases in Northeast China, the Yellow River basin (YR), and Southwest China, contrasted by declines in Eastern and Northwest China. Inter‐ and intra‐annual variability in reservoir water storage is mainly influenced by natural inflow in Northeast and Northwest China, while anthropogenic factors dominate in the YR, Eastern, and Southwest China. Plain Language Summary: Reservoirs can supply water, control flood and provide electricity. Without high temporal frequency data on inundated area and water storage over a large spatial extent, we could not clearly understand how and to which degree numerous reservoirs have altered the natural water cycle and provided multiple benefits to people all across the world. The development of remote sensing technology has facilitated the long‐term monitoring of inundated areas and water levels of large quantities of reservoirs. However, high‐temporal‐resolution monitoring remains challenging. In this study, by developing a novel method which integrates active microwave remote sensing data with optical remote sensing observations, we largely improved the satellite‐based monthly inundated area and water storage change monitoring at almost all large reservoirs in China. The results reveal distinct seasonal patterns and interannual variations of reservoir water in different regions of China. Specifically, we found that reservoirs in Northeast China, the Yellow River basin, and Southwest China generally expanded, whereas those in Eastern and Northwest China generally shrank during 2017–2021. The climatic and anthropogenic impacts on the inter‐ and intra‐annual variations in reservoir water across different regions were also analyzed. Key Points: A new methodology for integrating synthetic aperture radar backscattering coefficients with optical remote sensing‐based inland surface water dynamics is developedMonthly inundated area and water storage changes of 721 large reservoirs in China during 2017–2021 are retrieved at improved accuracyImpacts of natural climate and human control on the inter‐ and intra‐annual variations in reservoir water across different regions in China are analyzed [ABSTRACT FROM AUTHOR]

In the recent past, the Gravity Recovery and Climate Experiment (GRACE) satellite mission and its successor GRACE Follow‐On (GRACE‐FO), have become invaluable tools for characterizing drought through measurements of Total Water Storage Anomaly (TWSA). However, the existing approaches have often overlooked the uncertainties in TWSA that stem from GRACE orbit configuration, background models, and intrinsic data errors. Here we introduce a fresh view on this problem which incorporates the uncertainties in the data: the Probabilistic Storage‐based Drought Index (PSDI). Our method leverages Monte Carlo simulations to yield realistic realizations for the stochastic process of the TWSA time series. These realizations depict a range of plausible drought scenarios that later on are used to characterize drought. This approach provides probability for each drought category instead of selecting a single final category at each epoch. We have compared PSDI with the deterministic approach (Storage‐based Drought Index, SDI) over major global basins. Our results show that the deterministic approach often leans toward an overestimation of storage‐based drought severity. Furthermore, we scrutinize the performance of PSDI across diverse hydrologic events, spanning continents from the United States to Europe, the Middle East, Southern Africa, South America, and Australia. In each case, PSDI emerges as a reliable indicator for characterizing drought conditions, providing a more comprehensive perspective than conventional deterministic indices. In contrast to the common deterministic view, our probabilistic approach provides a more realistic characterization of the TWS drought, making it more suited for adaptive strategies and realistic risk management. Plain Language Summary: Total Water Storage (TWS) is defined as the sum of water stored as surface water (e.g., lakes and rivers), groundwater, soil moisture, snow, ice, and vegetation biomass. Since its launch in 2002, the Gravity Recovery and Climate Experiment (GRACE) satellite mission has provided unique TWS change measurements with many applications in hydrology, including characterizing drought events. Scientists have been using satellites like GRACE and its successor, GRACE‐FO, to understand drought by measuring the Total Water Storage Anomaly (TWSA). However, previous methods didn't consider uncertainties from satellite orbits, models, and data errors. This study offers a novel probabilistic approach for characterizing drought, Probabilistic Storage‐based Drought Index (PSDI), which acknowledges the uncertainties in the GRACE TWS change. We use simulations to create different drought scenarios, offering probabilities for each category instead of one fixed category. Comparing PSDI to traditional methods, we found that traditional methods tend to overestimate drought severity. We tested PSDI across different regions, and it consistently proved to be a reliable way to understand drought conditions, offering a more comprehensive perspective. Our probabilistic approach offers a more realistic view of TWS drought, making it suitable for adaptive strategies and risk management. Key Points: A novel probabilistic framework is introduced to characterize drought using Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On observations and propagating their stochasticsOur study suggests a tendency of deterministic approaches to overestimate storage‐based drought severityThe probabilistic approach captures global hydrological droughts while delivering more realistic results suited for risk management [ABSTRACT FROM AUTHOR]

Non‐tidal ocean loading (NTOL) signals are known to be a significant source of geophysically induced noise in gravimetric and geodetic observations also far‐away from the coast and especially during extreme events such as storm surges. Operationally available corrections suffer from a low temporal and spatial resolution and reveal too small amplitudes on continental stations. Dedicated high‐resolution sea‐level modeling of the North and Baltic Sea provides an improved prediction of NTOL signals. Superconducting gravimeter and Global Navigation Satellite Systems observations on the small offshore island of Heligoland in the North Sea are used for an evaluation of the model values revealing largely increased correlations of up to 0.9 and signal reductions of up to 50% during a storm surge period of one month in January and February 2022. Evaluations on additional continental superconducting gravimeter stations also show significant improvements through the recommended high‐resolution modeling for improved signal separation further away from the coast. Plain Language Summary: Terrestrial gravimetry is a technique to monitor temporal variations of the gravity acceleration at the Earth's surface that are induced by mass variations and deformations caused by a large number of geophysical effects on very different temporal and spatial scales. Current applications of high social relevance are the estimation of terrestrial water storage variations under climate change conditions, for example, groundwater depletion or polar and alpine ice mass loss, as well as hazard and geothermal monitoring. Before analyzing such signals of interest, it is essential to separate all other signals included in the gravimetric observations usually on the basis of adequate models. Amongst these disturbing signals, NTOL is one of the smaller but still significant effects. Up to now, the available operational corrections show a weak correlation with gravimetric observations and their application does not lead to a significant reduction of the observational signal variation. This situation largely improves with a high‐resolution model for gravity observations on the North Sea island of Heligoland. The model results are also applied to gravity records that were observed further away from the coast to assess the benefits of the model for an improved signal separation even at continental stations. Key Points: High‐resolution ocean model of the North and Baltic Sea provides improved non‐tidal ocean loading signalsModel evaluation by geodetic observations on the island of Heligoland shows correlation of 0.9 and signal reduction of 50%Continental gravimetric stations further away from the coast benefit from high‐resolution model for an improved signal separation [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) deliver significant and essential precipitation to the western United States (US) with consequential interannual variability. The intensity and frequency of ARs strongly influence reservoir levels, mountain snowpack, and groundwater recharge, which are key drivers of water‐resource availability and natural hazards. Between October 2022 and April 2023, western states experienced exceptionally heavy precipitation from several families of powerful ARs. Using observations of surface‐loading deformation from Global Navigation Satellite Systems, we find that terrestrial water‐storage gains exceeded 100% of normal within vital California watersheds. Independent water‐storage solutions derived from different data‐analysis and inversion methods provide an important measure of precision. The sustained storage increases, which we show are closely associated with ARs at daily‐to‐weekly timescales, alleviated both meteorological and hydrological drought conditions in the region, with a lag in hydrological‐drought improvements. Quantifying water‐storage recovery associated with extreme precipitation after drought advances understanding of an increasingly variable hydrologic cycle. Plain Language Summary: Human communities in the western United States rely on seasonal mountain snowpack and precipitation for agriculture, industry, and the civilian water supply. Over the past two decades, increasingly severe drought conditions have plagued western states. In this study, we investigate gains in water storage related to recent atmospheric rivers, which are long and narrow corridors of atmospheric moisture associated with extreme precipitation, using an interdisciplinary approach called hydrogeodesy. Hydrogeodesy uses satellite‐ and ground‐based sensors to track subtle changes in the shape of the Earth and its gravity field caused by the redistribution of water through the Earth system. By treating the Earth as a natural scale, we can infer how much water is gained or lost from a particular area over time. Advancing our understanding of water‐resource availability, the Earth's water cycle, and the Earth's changing climate is critical for improving water‐resource management, sustaining natural ecosystems, and mitigating natural hazards. Key Points: We use surface deformation to assess changes in terrestrial water storage (TWS) associated with families of strong atmospheric rivers (ARs) in 2022–2023Large jumps in mountain water storage from ARs spurred recoveries from major California droughts in 2012–2015 and 2019–2022Early‐season water‐storage gains, driven by ARs, nearly equal precipitation inputs, indicating extensive reservoir recharge [ABSTRACT FROM AUTHOR]

Water scarcity is a major challenge facing many regions worldwide, especially arid and semi‐arid areas that are increasingly vulnerable to climate change. This study aimed to project water availability in the Amu Darya Basin (ADB) of Central Asia under four Shared Socioeconomic Pathways (SSPs) from the Coupled Model Intercomparison Project Phase Six (CMIP6) during two upcoming periods (2020–2059 and 2060–2099). The study used a robust machine learning approach, namely a Random Forest (RF) model, to simulate Gravity Recovery and Climate Experiment (GRACE) Terrestrial Water Storage (TWS) data from precipitation and maximum and minimum temperatures (Tmax and Tmin). It then incorporated precipitation, Tmax and Tmin from four selected CMIP6 GCMs, into a water storage model to project spatiotemporal changes in water availability across the basin. The study also evaluated the relative impacts of land use and population on TWS. Results indicate an increase in TWS by approximately 4 cm in the basin's eastern, northwestern and southwestern regions in both future periods, while a decrease by approximately −4 cm in the remaining areas. These projections suggest that TWS will decline in densely populated regions and increase in certain intensively cultivated areas. The most pronounced increase in TWS is anticipated in the snow‐covered Tundra climate zone of the basin. This is attributed to the melting of glaciers, which contributes to runoff in the tributaries of the Amu River. The findings highlight the importance of considering climate change and socioeconomic factors when projecting water availability in arid and semi‐arid regions. The projected changes in TWS have important implications for water resources management in the ADB, particularly in densely populated and intensively cultivated areas. [ABSTRACT FROM AUTHOR]

In regions characterized by a high concentration of small reservoirs, there is often public debate about the effectiveness of these structures in locally adapting to and mitigating drought impacts, bearing in mind their potential to modify or induce drought events in downstream areas. In this study, we investigated the influence of a Dense Network of Small Reservoirs (DNR) on the emergence and intensification of drought impacts at catchment scale, as well as their local social benefits. This analysis was based on the Socio‐Hydrological‐Agricultural‐Reservoir (SHARE) model, specially developed for this purpose, with a medium‐sized catchment in the semi‐arid region of Brazil as a case study. We identified that, while a DNR can prolong the effects of a hydrological drought on storage in a large strategic reservoir at the catchment outlet by obstructing surface‐runoff connectivity, it plays a crucial role in mitigating drought impacts at a local level. Specifically, the presence of small reservoirs has the potential to boost local agricultural production by up to 5 times compared to scenarios without these structures. In addition, our simulation results suggest there is a notable reduction in the need for emergency water distribution by water trucks in the presence of a DNR. This study highlights the need for a balanced approach to implementing public policies, weighing the local benefits of small reservoirs against the possible downstream impacts on large reservoirs. Plain Language Summary: This study analyzed the impact of a dense network of small reservoirs in a semi‐arid region of Brazil on drought effects. Although there is criticism about the use of these structures because they can influence drought events in neighboring areas, our results showed that, locally, these reservoirs are essential for mitigating the impacts of drought. They may prolong the drought in large reservoirs, but they play a crucial role in reducing local impacts, increasing agricultural production by up to five times. In addition, the presence of these reservoirs considerably reduces the need for emergency water distribution by water trucks. The study highlights the importance of a balanced approach when implementing public policies, considering the local benefits of small reservoirs in relation to the possible impacts on more distant areas. Key Points: We explore the pro's and con's of small reservoirs as drought coping strategyEvaporation puts more pressure on surface water storage in semi‐arid regions than the demands of small farmersSmall reservoirs boost the local agricultural production up to five times while reducing the need for emergency water supply [ABSTRACT FROM AUTHOR]

Quantifying evapotranspiration (ET) is critical to accurately predict vegetation health, groundwater recharge, and streamflow generation. Hillslope aspect, the direction a hillslope faces, results in variable incoming solar radiation and subsequent vegetation water use that drive ET. Previous work in watersheds with a single dominant vegetation type (e.g., trees) have shown that equator‐facing slopes (EFS) have higher ET compared to pole‐facing slopes (PFS) due to higher evaporative demand. However, it remains unclear how differences in vegetation type (i.e., grasses and trees) influence ET and water partitioning between hillslopes with opposing aspects. Here, we quantified ET and root‐zone water storage deficits between a PFS and EFS with contrasting vegetation types within central coastal California. Our results suggest that the cooler PFS with oak trees has higher ET than the warmer EFS with grasses, which is counter to previous work in landscapes with a singule dominant vegetation type. Our root‐zone water storage deficit calculations indicate that the PFS has a higher subsurface storage deficit and a larger seasonal dry down than the EFS. This aspect difference in subsurface water storage deficits may influence the subsequent replenishment of dynamic water storage, groundwater recharge and streamflow generation. In addition, larger subsurface water deficits on PFS may reduce their ability to serve as hydrologic refugia for oaks during multi‐year droughts. This research provides a novel integration of field‐based and remotely‐sensed estimates of ET required to properly quantify hillslope‐scale water balances. These findings emphasize the importance of resolving hillslope‐scale vegetation structure within Earth system models, especially in landscapes with diverse vegetation types. Plain Language Summary: Understanding how much water leaves hillslopes as evapotranspiration (i.e., evaporation and plant water use) is important for predicting water storage and movement within hillslopes. Small differences in solar radiation between adjacent hillslopes that face opposite directions can produce contrasting plant water use and hillslope water storage patterns. However, previous studies have focused on landscapes with trees on either hillslope. It remains unclear how the combination of differences of solar radiation and plant type (i.e., grasses and trees) influence evapotranspiration and water storage within hillslopes that face opposite directions. Here, we combined on‐site measurements and remote‐sensing data to show that in central coastal California, a hillslope with oak trees that received less direct sunlight had higher evapotranspiration than a hillslope with grasses that received more direct sunlight. Importantly, we suggest that the cooler hillslope with oak trees may be drier and have lower groundwater recharge than the warmer hillslope with grasses, which is opposite the findings of studies with trees on both hillslopes. Our findings highlight the critical need for forest managers and modelers to consider hillslope‐scale vegetation types to more accurately predict groundwater recharge, streamflow generation, and vegetation health within oak savannas. Key Points: Higher evapotranspiration on cooler pole‐facing slope than warmer equator‐facing slope within an oak savanna in a Mediterranean climateAspect‐differences in plant functional groups and phenology drive observed evapotranspiration variabilityHigher evapotranspiration contributes to a drier subsurface on pole‐facing slopes [ABSTRACT FROM AUTHOR]

The history of experimental hydrology in China is important but poorly documented. Here we review 94 papers (86 of these in Chinese) from the CNKI and Web of Science websites to chronicle the history of experimental studies in China before 2000. Professor Wei‐Zu Gu—the focus of this Special Issue—factors heavily into this history. Perhaps more than any country, the experimental hydrology history of China is influenced by socioeconomic development and political change. Our analysis shows 4 main periods of development associated with (1) an early development stage before 1949, the founding year of People's Republic of China (PRC), (2) an initial period of rapid transition in experimental studies in China (1949–1965), (3) a struggling and recovering stage for during and shortly after the Cultural Revolution, from 1965 to 1978, and (4) significant progress associated with new policies for reform and opening up in China from 1978 to 2000, that included contributions of Wei‐Zu Gu and other scientists. In terms of experimental hydrological findings, two notable contributions are a mathematical description of saturation overland flow derived from streamflow and precipitation data and calculated water storage deficit in the unsaturated zone by Zhao and Zhuang (1963), several years before its process‐based 'discovery' in the USA (and elsewhere) and the 'interface runoff generation law' of Yu (1985) which was ahead of its time in understanding linking the notion of the commonality of all runoff forms. In terms of model development stemming from experimental studies, the development of the Xinanjiang Model of Zhao (1980) was a significant achievement, later partly adopted into the VIC model in the USA and the ARNO model in Europe. Finally, the Hydrohill catchment developed by Wei‐Zu Gu led the way for the rapid rise in process studies and internationalization of experimental hydrology in China. Overall, the experimental hydrological studies from 1949 to 2000 had a strong engineering focus, with many links to water conservation construction and management. [ABSTRACT FROM AUTHOR]

As the climate warms, snow‐dominated regions are increasingly susceptible to precipitation phase changes and earlier snowmelt. In alpine catchments, snowmelt is the majority of runoff, governed by heterogeneous interactions among snowfall, wind, topography and vegetation. The role of the alpine snowpack in delaying the downstream release of accumulated snowfall remains understudied, despite its fundamental role in sustaining lower‐elevation ecosystems and populations. We evaluated fine‐scale changes in the magnitude and duration of alpine snow water storage and release by comparing effective precipitation seasonality to that of surface water inputs using a Snow Storage Index (SSI). We forced the Distributed Hydrology Soil Vegetation Model with historical climate conditions and an end‐of‐century warming signal down‐scaled from a 4‐km regional climate model, to simulate future changes in snow water storage and partitioning of precipitation to runoff and evapotranspiration. The hydrological modelling was conducted on a 2‐m spatial grid to resolve fine scale processes over a 0.6‐km2 headwater catchment within the Niwot Ridge Long‐Term Ecological Research site located in the Rocky Mountains, Colorado, USA. Proportionally more rainfall, reduced snowpack size and extent, and earlier snowmelt occurred in the future scenario with decreased catchment average SSI and thus snow water storing capabilities, with inordinately large decreases occurring in areas of the catchment with reconstructed snowdrifts (−57% average and −100% maximum). Using the Budyko framework to evaluate annual hydrologic partitioning, precipitation inputs to streamflow increased by a maximum 10% in large SSI regions with snowdrifts under warming conditions, signifying a potential buffer for total catchment and annual streamflow loss in a future climate. The fine‐scale analysis of hydrologic sensitivities to warming presented herein are important because climate conditions are expected to further reduce alpine snow water storage, alter hydrologic partitioning, and runoff generation for ecosystems and people living downstream. [ABSTRACT FROM AUTHOR]

GRACE (Gravity Recovery and Climate Experiment) has been widely used to evaluate terrestrial water storage (TWS) and groundwater storage (GWS). However, the coarse‐resolution of GRACE data has limited the ability to identify local vulnerabilities in water storage changes associated with climatic and anthropogenic stressors. This study employs high‐resolution (1 km2) GRACE data generated through machine learning (ML) based statistical downscaling to illuminate TWS and GWS dynamics across twenty sub‐regions in the Indus Basin. Monthly TWS and GWS anomalies obtained from a geographically weighted random forest (RFgw) model maintained good consistency with original GRACE data at the 25 km2 grid scale. The downscaled data at 1 km2 resolution illustrate the spatial heterogeneity of TWS and GWS depletion within each sub‐region. Comparison with in‐situ GWS from 2,200 monitoring wells shows that downscaling of GRACE data significantly improves agreement with in‐situ data, evidenced by higher Kling‐Gupta Efficiency (0.50–0.85) and correlation coefficients (0.60–0.95). Hotspots with the highest TWS and GWS decline rate between 2002 and 2023 were Dehli Doab (−442, −585 mm/year), BIST Doab (−367, −556 mm/year), Rajasthan (−242, −381 mm/year), and BARI (−188, −333 mm/year). Based on a general additive model, 47%–83% of the TWS decline was associated with anthropogenic stressors mainly due to increasing trends of crop sown area, water consumption, and human settlements. The decline rate of TWS and GWS anomalies was lower (i.e., −25 to −75 mm/year) in upstream sub‐regions (e.g., Yogo, Gilgit, Khurmong, Kabul) where climatic factors (downward shortwave radiations, air temperature, and sea surface temperature) explained 72%–91% of TWS/GWS changes. The relative influences of climatic and anthropogenic stressors varied across sub‐regions, underscoring the complex interplay of natural‐human activities in the basin. These findings inform place‐based water resource management in the Indus Basin by advancing the understanding of local vulnerabilities. Plain Language Summary: We used GRACE data to understand how water storage has changed over time across the Indus Basin at a resolution of 1 square kilometer. We generated the new high‐resolution data using machine learning techniques that implemented statistical methods. The new data for analyzing water storage matched well with the original data on a larger scale. Additionally, comparing this detailed data with measurements from 2,200 wells showed that our new method works well. The new high‐resolution data help us detect hotspots of water storage decline where water availability may face challenges in the future if status quo continues. Human activities like more farming, using more water, and building more areas for people to live are a major driver of the water storage decline. In upstream areas less influenced by human impacts, the decline is driven more by climatic factors. By improving understanding of local vulnerabilities, our study supports planning interventions for specific regions based on the need to reduce the impact of human activities or adapt to climate change. Key Points: Terrestrial water storage (TWS)/groundwater storage (GWS) derived from downscaled GRACE data show a declining trend across most sub‐regions of the Indus Basin between 2002 and 2023Anthropogenic stressors explain 47%–83% of TWS decline in the majority of sub‐regionsTWS/GWS changes in upstream sub‐regions, where shortwave radiations mainly control the TWS changes, are well explained by climatic factors [ABSTRACT FROM AUTHOR]

Assessing spatiotemporal water storage variability in the Great Lakes Watershed (GLW) is critical given its transboundary status impacting both Canada and the United States. Here, we apply a novel inversion strategy to global positioning system (GPS) vertical movements to achieve high spatial resolution total water storage (TWS) variations in GLW through improved processing. The steps are composed of removing load changes driven by the lake water fluctuation by forward modeling, isolating the Great Lakes grids to solve the ill‐conditioned problem in inversion, and inverting the GPS residual series to estimate TWS variations on land (TWSGPS). The results show that the regional dense continuous GPS observation network can successfully resolve TWS on land at monthly timescales with 30–45 km spatial resolution. We also could effectively capture fine‐scale TWS features than GRACE/GFO mascon products. GRACE/GFO satellites largely underestimate seasonal and long‐term TWS spatial fluctuations, but their temporal patterns coincide with those from GPS. The average annual amplitude of TWSGPS on land reaches 82.0 mm, greatly exceeding estimates from GRACE/GFO (∼48.0 mm) and composite hydrological model outputs (∼62.0 mm). The seasonal groundwater fluctuations inferred from GPS have peak‐to‐peak amplitudes of ∼40 km3 with the maximum around September. This coincides with that from GRACE/GFO. However, the magnitudes and phases of groundwater storage from GPS vary markedly among the subbasins in GLW, and the different snow and soil moisture amounts measured in each subbasin cause discrepancies among these GPS estimates. This study shows the value of GPS data in spatially downscaling GRACE/GFO data and providing high‐resolution output at spatiotemporal scales with low latency. Key Points: A new inversion strategy for Total Water Storage (TWS) was applied to global positioning system (GPS) data by first removing lake water driven load through forward modelingGRACE/GFO TWS underestimates spatial patterns of seasonal and long‐term TWS fluctuations but coincides with temporal TWS patterns from GPS dataGPS provides high spatiotemporal resolution in TWS relative to GRACE/GFO and improved understanding water storage dynamics in Great Lakes Watershed [ABSTRACT FROM AUTHOR]

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Water storages for energy production in mountain valleys or snow retention basins on mountain slopes or mountain ridges are common buildings in Alpine regions. When such water reservoirs are situated on or nearby of a large slow‐moving landslide, low factors of safety and ongoing deformation of the reservoir structure – or parts of it – need to be handled in some cases. Questions about the justification but also the reliability of reservoirs influenced by such landslides need to be discussed. Verification procedures which proof a save operation of the reservoir over time but also an estimation of the behaviour of the reservoir influenced by the landslide for exceptional load cases need to be investigated. In this paper main issues and problems related to such questions are discussed for reservoirs in the design as well as in the operation state which are influenced by such slow moving landslides. [ABSTRACT FROM AUTHOR]

Saline aquifers are the most popular waste and CO2 injection and storage reservoirs worldwide. This project proposes that several optimal injection positions should be investigated as hydraulic pressure‐focused positions, in order to relieve the high demands of pump performance. The comprehensive indices (Fi) representing the injectivity of different burial depths were obtained by using information entropy, based on the mercury injection experimental data of 13 rock samples. The results demonstrated that the burial depths of No. 4, No. 1 and No. 2 in the Liujiagou Formation were the most suitable positions for hydraulic focused injection, which means the upper 30 m thickness could be regarded as the hydraulic focused range in the saline aquifer with an average thickness of 400 m. In addition, some laboratory experiments and in situ tests were carried out for the purpose of certifying and analyzing results, including SEM, XRD, brittleness index and logging. The results suggested that the rock samples at the No. 4, No. 1 and No. 2 burial depth ranges have loose microstructure, weak cementation, as well as dual pores and fractures. The lithology is mainly quartz and feldspar, but the clay mineral content is high (10%–25%), which is positive for dissolution. The lithology is suitable for hydraulic fracturing to form extended cracks and micro‐fissures during high‐TDS (total dissolved solids) mine water injection, because of the high brittleness index. Finally, a theoretical and technical framework for high‐TDS mine water injection was established, based on operating pilot engineering. Some theoretical defects and drawbacks learned from the field practices were summarized and solutions proposed. The research in this study could provide guidance and a paradigm for the inexpensive treatment of high‐TDS mine water by injection and storage. [ABSTRACT FROM AUTHOR]

Climate change‐induced shifts in snow storage and snowmelt patterns pose risks for adverse impacts to people, the environment, and irrigated agriculture. Existing research primarily focuses on evaluating these risks to irrigated agriculture at large scales, overlooking the role of local context in shaping risk dynamics. Consequently, many "at‐risk" areas lack insight into how adaptation strategies for managing risk through water supply augmentation or water conservation vary across contexts and over time. To address this gap, we develop a comprehensive index for evaluating irrigated agriculture's risk and adaptation potential to changes in snow at local scales and apply it throughout the western US. Results confirm trends toward escalating risk for changes in snow storage and snowmelt patterns over the century. However, substantial heterogeneity in the extent and drivers of risk exists due to variability in localized interactions between declines in water supply (approximately −9% ± 13% by 2100) and increased agricultural demand (approximately 7% ± 5% by 2100). Despite an existing focus on supply augmentation as a critical adaptation strategy to reduce risk, we show its effectiveness diminishes for many areas over time, declining to an average of −54% of historical augmentation potential by 2100. Conserving water through historical changes in crop acreage and type emerges as a more stable adaptation measure, reducing demand by 7%–8% regardless of time. While particularly relevant for higher elevation, less intensive agricultural settings in snowmelt‐dependent regions, findings underscore the need for strategies that support local‐scale, context‐appropriate adaptation to effectively manage escalating risk as snow changes. Plain Language Summary: Climate change‐induced shifts in snow storage and snowmelt patterns threaten water supplies for irrigated agriculture, necessitating adaptation. In agriculture, adaptation requires balancing water supply and demand locally, which is often overlooked in larger‐scale analyses. We develop a framework for evaluating risk and adaptation at local scales in the agricultural sector and apply it to the western US, which is broadly at risk for adverse effects (i.e., physical harm, economic losses, and socio‐cultural damage) if water demand cannot be met. Changing snow dynamics negatively impact many local areas over time, although impacts can often be reduced through supply augmentation and water conservation. While the degree of risk and the effectiveness of these adaptation strategies vary across regions and time, increasing demand and supply volatility diminishes opportunities for supply augmentation. This trend underscores the importance of supporting context‐appropriate adaptations for agricultural communities facing increased risk to their water supplies. Key Points: Changes to snow storage and release patterns pose localized and heterogeneous risks to irrigated agriculture, which accelerate in the futurePotential to manage risk from less snow by augmenting water storage is limited by water supply‐demand imbalances in many locations over timeConserving water by reducing demand could buffer irrigated agriculture against increased risk even as augmentation potential diminishes [ABSTRACT FROM AUTHOR]

Questions: Plants largely vary in their capacity for foliar water uptake (FWU), that is, the capacity to increase leaf water content by directly absorbing water from leaf‐wetting events. Climate change will reduce leaf wetting and increase drought events. Therefore, we need a better understanding of the underlying traits and mechanisms that facilitate FWU. Location: Seasonally dry tropical montane grasslands in Brazil (Campos de Altitude). Methods: We measured FWU and leaf traits related to wettability, surface conductance, water potential and water storage on up to 55 plant species. By using Direct Acyclic Graph theory and Bayesian modelling, we tested how those leaf traits affect FWU. Results: We found that stomatal conductance largely explained interspecific variation in FWU, which was also favoured in species with hydrophilic leaves, high cuticular conductance, more negative leaf water potentials, low dry‐matter content, isohydric behaviour, and more elastic cell walls. Conclusions: Due to the existence of trade‐offs, not all species exhibit an optimal combination of traits that favours FWU. Instead, co‐occurring species have achieved a similar capacity for FWU through distinct trait combinations. Consequently, species engaged in FWU may exhibit differential vulnerabilities to climate change as they can cope with drought using other strategies beside FWU. [ABSTRACT FROM AUTHOR]

The response of plants to increasing atmospheric CO2 depends on the ecological context where the plants are found. Several experiments with elevated CO2 (eCO2) have been done worldwide, but the Amazonian forest understory has been neglected. As the central Amazon is limited by light and phosphorus, understanding how understory responds to eCO2 is important for foreseeing how the forest will function in the future. In the understory of a natural forest in the Central Amazon, we installed four open‐top chambers as control replicates and another four under eCO2 (+250 ppm above ambient levels). Under eCO2, we observed increases in carbon assimilation rate (67%), maximum electron transport rate (19%), quantum yield (56%), and water use efficiency (78%). We also detected an increase in leaf area (51%) and stem diameter increment (65%). Central Amazon understory responded positively to eCO2 by increasing their ability to capture and use light and the extra primary productivity was allocated to supporting more leaf and conducting tissues. The increment in leaf area while maintaining transpiration rates suggests that the understory will increase its contribution to evapotranspiration. Therefore, this forest might be less resistant in the future to extreme drought, as no reduction in transpiration rates were detected. Summary statement: The Amazonian understory plants demonstrate a remarkable response to elevated CO2, including an increase in leaf area and a higher investment in the maximum electron transport rate. These findings suggest enhanced carbon storage and water flux, stressing the important role of the understory in the overall functioning of the forest ecosystem. [ABSTRACT FROM AUTHOR]