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Most of the available data on diffusion in natural clayey rocks consider tracer diffusion in the absence of a salinity gradient despite the fact that such gradients are frequently found in natural and engineered subsurface environments. To assess the role of such gradients on the diffusion properties of clayey materials, through-diffusion experiments were carried out in the presence and absence of a salinity gradient using salt-diffusion and radioisotope tracer techniques. The experiments were carried out with vermiculite samples that contained equal proportions of interparticle and interlayer porosities so as to assess also the role played by the two types of porosities on the diffusion of water and ions. Data were interpreted using both a classical Fickian diffusion model and with a reactive transport code, CrunchClay that can handle multi-porosity diffusion processes in the presence of charged surfaces. By combining experimental and simulated data, we demonstrated that (i) the flux of water diffusing through vermiculite interlayer porosity was minor compared to that diffusing through the interparticle porosity, and (ii) a model considering at least three types of porous volumes (interlayer, interparticle diffuse layer, and bulk interparticle) was necessary to reproduce consistently the variations of neutral and charged species diffusion as a function of salinity gradient conditions.

Geologic features (e.g., fractures and alluvial fans) can play an important role in the locations and volumes of groundwater discharge and degree of groundwater-surface water (GW-SW) interactions. However, the role of these features in controlling GW-SW dynamics and streamflow generation processes are not well constrained. GW-SW interactions and streamflow generation processes are further complicated by variability in precipitation inputs from summer and fall monsoon rains, as well as declines in snowpack and changing melt dynamics driven by warming temperatures. Using high spatial and temporal resolution radon and water stable isotope sampling and a 1D groundwater flux model, we evaluated how groundwater contributions and GW-SW interactions varied along a stream reach impacted by fractures (fractured-zone) and downstream of the fractured hillslope (non-fractured zone) in Coal Creek, a Colorado River headwater stream affected by summer monsoons. During early summer, groundwater contributions from the fractured zone were high, but declined throughout the summer. Groundwater contributions from the non-fractured zone were constant throughout the summer and became proportionally more important later in the summer. We hypothesize that groundwater in the non-fractured zone is dominantly sourced from a high-storage alluvial fan at the base of a tributary that is connected to Coal Creek throughout the summer and provides consistent groundwater influx. Water isotope data revealed that Coal Creek responds quickly to incoming precipitation early in the summer, and summer precipitation becomes more important for streamflow generation later in the summer. We quantified the change in catchment dynamic storage and found it negatively related to stream water isotope values, and positively related to modeled groundwater discharge and the ratio of fractured zone to non-fractured zone groundwater. We interpret these relationships as declining hydrologic connectivity throughout the summer leading to late summer streamflow supported predominantly by shallow flow paths, with variable response to drying from geologic features based on their storage. As groundwater becomes more important for sustaining summer flows, quantifying local geologic controls on groundwater inputs and their response to variable moisture conditions may become critical for accurate predictions of streamflow.

Understanding connectivity between the soil and deeper bedrock groundwater is needed to accurately predict a watershed's response to perturbation, such as drought. Yet, the bedrock groundwater dynamics in mountainous environments are typically under-constrained and excluded from watershed hydrologic models. Here, we investigate the role of groundwater characterized with decadal and longer water ages on the hydrologic and mass-transport processes within a steep snow-dominated mountain hillslope in the Central Rocky Mountains (USA). We quantify subsurface and surface water mass-balance, groundwater flowpaths, and age distributions using the ParFlow-CLM integrated hydrologic and EcoSLIM particle tracking models, which are compared to hydrometric and environmental tracer observations. An ensemble of models with varied soil and hydrogeologic parameters reproduces observed groundwater levels and century-scale mean ages inferred from environmental tracers. The numerical models suggest soil water near the toe of the hillslope contains considerable (>60 % of the mass-flux) contributions from bedrock flowpaths characterized with water ages >10 years. Flowpath connectivity between the deeper bedrock and soil systems is present throughout the year, highlighting the potentially critical role of groundwater with old ages on processes such as evapotranspiration and streamflow generation. The coupled numerical model and groundwater age observations show the bedrock groundwater system influences the hillslope hydrodynamics and should be considered in mountain watershed conceptual and numerical models.

In this study we construct and compare three different deep learning (DL) models for estimating daily snow water equivalent (SWE) from high-resolution gridded meteorological fields over the Rocky Mountain region. To train the DL models, Snow Telemetry (SNOTEL) station-based SWE observations are used as the prediction target. All DL models produce higher median Nash-Sutcliffe Efficiency (NSE) values than a conceptual SWE model and interpolated gridded data sets, although mean squared errors also tend to be higher. Sensitivity of the SWE prediction to the model's input variables is analyzed using an explainable artificial intelligence (XAI) method, yielding insight into the physical relationships learned by the models. This method reveals the dominant role precipitation and temperature play in snowpack dynamics. In applying our models to estimate SWE throughout the Rocky Mountains, an extrapolation problem arises since the statistical properties of SWE (e.g., annual maximum) and geographical properties of individual grid points (e.g., elevation) differ from the training data. This problem is solved by normalizing the SWE with its historical maximum value to alleviate extrapolation for all tested DL models. Our work shows that the DL models are promising tools for estimating SWE, and sufficiently capture relevant physical relationships to make them useful for spatial and temporal extrapolation of SWE values.

Northern high-latitudes are projected to get warmer and wetter, which will affect rates of permafrost thaw and mechanisms by which thaw occurs. To better understand the impact of rain, as well as other factors such as snow depth, canopy cover, and microtopography, we instrumented a degrading permafrost plateau in south-central Alaska with high-resolution soil temperature sensors. The site contains ecosystem-protected permafrost, which persists in unfavorable climates due to favorable ecologic conditions. Our study (2020-2022) captured three of the snowiest years and three of the four wettest years since the site was first studied in 2015. Average thaw rates along an across-site transect increased nine-fold from 6 ± 5 cm yr−1 (2015-2020) to 56 ± 12 cm yr−1 (2020-2022). This thaw was not uniform. Hummock locations, residing on topographic high points with relatively dense canopy, experienced only 8 ± 9 cm yr−1 of thaw, on average. Hollows, topographic low points with low canopy cover, and transition locations, which had canopy cover and elevation between hummocks and hollows, thawed 44 ± 6 cm yr−1 and 39 ± 13 cm yr−1, respectively. Mechanisms of thaw differed between these locations. Hollows had high warm-season soil moisture, which increased thermal conductivity, and deep cold-season snow coverage, which insulated soil. Transition locations thawed primarily due to thermal energy transported through subsurface taliks during individual rain events. Most increases in depth to permafrost occurred below the ∼45 cm thickness seasonally frozen layer, and therefore, expanded existing site taliks. Results highlight the importance of canopy cover and microtopography in controlling soil thermal inputs, the ability of subsurface runoff from individual rain events to trigger warming and thaw, and the acceleration of thaw caused by consecutive wet and snowy years. As northern high-latitudes become warmer and wetter, and weather events become more extreme, the importance of these controls on soil warming and thaw is likely to increase.

Headwater catchments play a vital role in regional water supply and ecohydrology, and a quantitative understanding of the hydrological partitioning in these catchments is critically needed, particularly under a changing climate. Recent studies have highlighted the importance of subsurface critical zone (CZ) structure in modulating the partitioning of precipitation in mountainous catchments; however, few existing studies have explicitly taken into account the 3D subsurface CZ structure. In this study, we designed realistic synthetic catchment models based on seismic velocity-estimated 3D subsurface CZ structures. Integrated hydrologic modeling is then used to study the effects of the shape of the weathered bedrock and the associated storage capacity on various hydrologic fluxes and storages in mountainous headwater catchments. Numerical results show that the weathered bedrock affects not only the magnitude but also the peak time of both streamflow and subsurface dynamic storage.

Residual trapping is an important process that affects the efficiency of cyclic storage and withdrawal and in-situ production of hydrogen in geological media. In this study, we have conducted pore-scale modeling to investigate the effects of pore geometry and injection rate on the occurrence and efficiency of residual trapping via dead-end bypassing. We begin our theoretical and numerical analyses using a single rectangular pore to understand the key controls in bypassing. We further investigated two factors affecting bypassing: (a) a continuous cycle of injection-extraction of H2, and (b) variable pore geometry. Based on our pore-scale simulations, we found that: (a) a higher pore height/width ratio (h/w) and a higher injection rate cause more residual trapping, which is unfavorable for withdrawal of H2; (b) the trapping percentage increases with the h/w first and then decreases after h/w reaches 0.5; (c) and a converging-shaped pore can result in less trapping volume. Based on a theoretical comparison of the residual trapping behavior of H2 and CO2, we discuss the mechanisms that are applicable to CO2 residual trapping and the possibility of developing engineering controls of H2 storage and production.

In this Reply, we address the concerns of Knoben and Clark (2023, https://doi.org/10.1029/2022WR034294) or KC23 that “the assumptions needed to effectively use difference-based variance estimation methods are not always met by hourly streamflow records.” There should be little doubt that the assumptions of our difference-based estimator will sometimes be violated in hourly streamflow records but the results from de Oliveira and Vrugt (2022, https://doi.org/10.1029/2022wr032263) and confirmed by the findings in our Reply show that such violations are sporadic enough not to affect much the error variance estimates. Snowmelt as pointed out by KC23 (https://doi.org/10.1029/2022WR034294) may not have received sufficient attention in our paper, yet their 365-day record is simply not long enough to demonstrate bias of our discharge error variance estimates (and their dependence on flow level). This would require analysis of a much longer, multi-year, streamflow record of a snowmelt-driven watershed. The snowmelt catchment analyzed in dOV22 (https://doi.org/10.1029/2022wr032263) did not demonstrate bias in the discharge error variance estimates. We also provide additional clarification on the interpretation of the variance estimates obtained with the nonparametric estimator, and discuss the main issues in the test case presented in Knoben and Clark (2023, https://doi.org/10.1029/2022WR034294)).

Understanding the impact of repository gas, generated from degradation of waste and its interaction with the host rock, is essential when assessing the performance and safety function of long-term disposal systems for radioactive waste. Numerical models based on conventional multi-phase flow theory have historically been applied to predict the outcome and impact of gas flow on different repository components. However, they remain unable to describe the full complexity of the physical processes observed in water-saturated experiments (e.g., creation of dilatant pathways) and thus, the development of novel representations for their description is required when assessing fully saturated clay-based systems. This was the primary focus of Task A within the international cooperative project DECOVALEX-2019 (D-2019) and refinement of these approaches is the primary focus of this study (Task B in the current phase of DECOVALEX-2023). This paper summarises development of enhanced numerical representations of key processes and compares the performance of each model against high-quality laboratory test data. Experimental data reveals that gas percolation in water-saturated compacted bentonite is characterised by four key features: (i) a quiescence phase, followed by (ii) the gas breakthrough, which leads to a (iii) peak value, which is then followed by (iv) a negative decay. Three models based on the multiphase flow theory have been developed. These models can provide good initial values and reasonable responses for gas breakthrough (although some of them still predict a too-smooth response). Peak gas pressure values are in general reasonably well captured, although maximum radial stress differences are observed at 48 mm from the base of the sample. Here, numerical peak values of 12.8 MPa are predicted, whereas experimental values are about 11 MPa. These models are also capable of providing a reasonable representation of the negative pressure decay following peak pressure. However, other key specific features (such as the timing of gas breakthrough) still require a better representation. The model simulations and their comparison with experimental data show that these models need to be further improved with respect to model parameter calibration, the numerical representation of spatial heterogeneities in material properties and flow localisation, and the upscaling of the related physical processes and parameters. To further understand gas flow localisation, a new conceptual model has been developed, which shows that discrete channels can possibly be induced through the instability of gas-bentonite interface during gas injection, thus providing a new perspective for modeling gas percolation in low-permeability deformable media.

Predicting the hydrological response of watersheds to climate disturbances requires a detailed understanding of the processes connecting hillslopes and streams. Using a network of soil moisture and temperature sensors, electrical resistivity tomography monitoring, and a weather station we assess the above and below-ground processes driving the hydrological response of a hillslope during snowmelt and summer monsoon. The transect covers bedrock and vegetation gradients, with a steep upper part characterized by shallow bedrock, and gentle lower part underlain by colluvium. The main vegetation cover is conifers on the upper, and grass and veratrum on the lower part. Combined with a simplified hydrological model, we show that the thin soil layer of the steep slope acts as a preferential flow path, leading to mostly shallow lateral flow, interrupted by vertical flow, mostly at tree locations, and likely facilitated by flow along fractures and roots. Vertical flow and upstream-driven groundwater dynamics are prevailing at the colluvium, presenting a very different hydrological behavior compared to the upper part. These results show that subsurface structure and features have a strong control on the hydrological response of a hillslope and that those can create considerably varying hydrological dynamics across small spatial scales.

Abstract: Floodplain lakes are widespread and ecologically important throughout tropical river systems, however data are rare that describe how temporal variations in hydrological, meteorological and optical conditions moderate stratification and mixing in these shallow lakes. Using time series measurements of meteorology and water‐column temperatures from 17 several day campaigns spanning two hydrological years in a representative Amazon floodplain lake, we calculated surface energy fluxes and thermal stratification, and applied and evaluated a 3‐dimensional hydrodynamic model. The model successfully simulated diel cycles in thermal structure characterized by buoyancy frequency, depth of the actively mixing layer, and other terms associated with the surface energy budget. Diurnal heating with strong stratification and nocturnal mixing were common; despite considerable heat loss at night, the strong stratification during the day meant that mixing only infrequently extended to the bottom at night. Simulations indicated that the diurnal thermocline up and downwelled creating lake‐wide differences in near‐surface temperatures and mixing depths. Infrequent full mixing creates conditions conducive to anoxia in these shallow lakes given their warm temperatures.

In order to understand the details of thermal and hydrologic processes attending CO2 injection into a deep aquifer in the context of Carbon Capture and Storage (CCS), we have carried out coupled well-reservoir simulations of CO2 injection using the simulator T2WELL-ECO2M. We focus on the injection of cold, dry CO2 into a warm aquifer and analyze in detail the thermal and hydraulic processes of the coupled well-reservoir system. The results demonstrate the effectiveness of T2WELL in accurately modeling non-isothermal, multiphase flow, phase changes, and identifying dry-out regions in porous media. We simulated heat exchange with the ambient environment, friction effects, convection, exothermic dissolution in brine, and cooling due to both Joule-Thomson effect and water vaporization. The temperature profile within the wellbore deviated from the geothermal profile, impacting CO2 properties at the bottomhole. The simulation revealed the presence of three fronts in the formation: the CO2 saturation, thermal, and evaporation fronts. The thermal and evaporation fronts were located farther behind the saturation front, indicating limited dry-out and thermal effects near the wellbore. This simulation capability and insights gained in this study form a foundation for ongoing work such as sensitivity analyses, injection optimization, performance assessment, and operational decision support.

Abstract: Toxic cyanobacterial blooms (CBs) are becoming more frequent globally, posing a threat to freshwater ecosystems. While making long‐range forecasts is overly challenging, predicting imminent CBs is possible from precise monitoring data of the underlying covariates. It is, however, infeasibly costly to conduct precise monitoring on a large scale, leaving most lakes unmonitored or only partially monitored. The challenge is hence to build a predictive model that can use the incomplete, partially‐monitored data to make near‐future CB predictions. By using 30 years of monitoring data for 78 water bodies in Alberta, Canada, combined with data of watershed characteristics (including natural land cover and anthropogenic land use) and meteorological conditions, we train a Bayesian network that predicts future 2‐week CB with an area under the curve (AUC) of 0.83. The only monitoring data that the model needs to reach this level of accuracy are whether the cell count and Secchi depth are low, medium, or high, which can be estimated by advanced high‐resolution imaging technology or trained local citizens. The model is robust against missing values as in the absence of any single covariate, it performs with an AUC of at least 0.78. While taking a major step toward reduced‐cost, less data‐intensive CB forecasting, our results identify those key covariates that are worth the monitoring investment for highly accurate predictions.

Climate change intensifies longstanding tensions over groundwater sustainability and equity of access among users. Though private land ownership is a primary mechanism for accessing groundwater in many regions, few studies have systematically examined the extent to which farmland markets transform groundwater access patterns over time. This study begins to fill this gap by examining farmland transactions overlying groundwater from 2003-17 in California. We construct a novel dataset that downscales well construction behavior to the parcel level, and we use it to characterize changes in groundwater access patterns by buyer type on newly transacted parcels in the San Joaquin Valley groundwater basin during the 2011-17 drought. Our results demonstrate large-scale transitions in farmland ownership, with 21.1% of overlying agricultural acreage statewide sold at least once during the study period and with the highest rates of turnover occurring in critically overdrafted basins. By 2017, annual individual farmland acquisitions had halved, while acquisitions by limited liability companies increased to one-third of all overlying acres purchased. Together, these trends signal increasing corporate farmland acquisitions; new corporate farmland owners are associated with the construction, on comparable parcels, of agricultural wells 77-81 feet deeper than those drilled by new individual landowners. We discuss the implications of our findings for near-term governance of groundwater, and their relevance for understanding structural inequities in exposure to future groundwater level declines.

Understanding the mechanisms that control seasonal groundwater recharge at local and intermediate scales is critical for understanding contaminant transport. The recharge mechanism of seasonal precipitation and irrigation, companied with legacy wastewater in porewater in alluvial aquifer were complicated due to the seasonal variation of multiple recharge sources. In this study, a long-term wastewater leakage and irrigation region along the Tanghe Wastewater Reservoir (TWR) in the alluvial plain area of North China Plain to investigate the recharge mechanism from unsaturated zone to the upper shallow groundwater affected by multiple water sources. Water chemical ions and stable isotopes of water (2H and 18O) of 30 m deep sediment profiles and groundwater boreholes were used to trace the recharge processes. The porewater stable isotopes of the sediment profiles revealed vertical recharge rates ranging from 0.63 to 1.09 m/year for the layered unsaturated zone with silt and silty clay. Due to the matrix flow through unsaturated zone, the legacy wastewater in the unsaturated zone affected the variation of groundwater quality in long term. However, fast flow (i.e., preferential or lateral flow) occurred in sand layers of alluvial aquifers lead to significant precipitation contributions (44 to 61 %) to the upper shallow groundwater (USGW) recharge, resulting in the seasonal variation of stable isotopes and water chemical ions. Affected by the fast flow with seasonal variations and matrix flow with legacy wastewater in the unsaturated zone, the δ2H and δ18O relationship of the USGW showed two types of hysteresis loops in the dual isotope space: 1) groundwater in regions affected by the TWR wastewater leakage shows narrow loops and a nearly straight line with end-members of precipitation that recharged to groundwater by fast flow, and evaporated porewater plotting along the TWR evaporation line; and 2) groundwater in irrigated farmlands with low and high irrigation amounts and intense evaporation shows stronger hysteresis with loops overlapping with shallow porewater, suggesting the impact of legacy wastewater of the unsaturated zone on groundwater. The residual pollutants in soil and the type of fast flow determine the different seasonal variation of groundwater quality. Preferential flow in alluvial aquifer regions can increase the proportion of seasonal recharge from precipitation, resulting in the rapid introduction of contaminants from surface or shallow soils into the alluvial aquifer.

The North Campus Open Space (NCOS) restoration project has created more than 40 acres of estuarine and palustrine wetlands that historically comprised the upper portion of Devereux Slough that was filled in the mid-1960s to create the Ocean Meadows golf course. The project also restored more than 60 acres of upland habitats that include native grassland, coastal sage scrub, riparian, oak chaparral woodland, vernal pools and patches of annual wildflowers in clay and sandy soils. This report summarizes the results from monitoring native and non-native plants, birds, small mammals, aquatic invertebrates, tree growth, and hydrological functions.

Abstract: Precipitation and temperature extremes from daily data indexed using the CLIMDEX methodology were calculated over the Central American region. The data comprises the coarsened versions of the Climate Hazards and Infrared Precipitation with stations (CHIRPs) and the corresponding data set for temperature (CHIRTs) from the year 1981 to 2020 and 1983 to 2016, respectively. The objective is to detect trend patterns in extremes in recent periods, use novel statistical techniques for assessing the trend significance and study the monthly and annual trends for each of the indices. Trends of extreme temperature indices show more consistent, robust and widespread significant results according with the observed warming of the region. Significant extreme precipitation indices trends are more localized, and therefore harder to analyse, but it seems that one robust result from several indices is the trend toward more intense extreme precipitation events in Costa Rica. The findings of this work suggest possible impacts in human and environmental systems across the region.

The long-lived radiotoxicity of the high-level radioactive waste generated by nuclear power plants requires safe isolation from the biosphere for many hundreds of thousands of years. An international consensus has emerged that such isolation can best be provided by disposal in mined geologic repositories, a strategy that today is pursued by most countries dealing with radioactive waste. However, the need to predict the performance of such repositories over very long time periods generates large uncertainties that have to be accounted for in safety assessments. The findings from such safety assessments need to be conveyed to all stakeholders in a clear way, such that public confidence in geologic disposal solutions can be achieved. It is suggested here that close international collaboration on the technical aspects of geologic waste disposal has helped, and will continue to help, building trust and increasing confidence. This paper discusses a particular international collaboration initiative referred to as DECOVALEX, which brings together multiple teams and disciplines to collectively tackle complex experimental and modeling challenges related to geologic disposal. By describing how DECOVALEX works and by providing joint research examples, a case is made that such international collaboration contributes to knowledge transfer and confidence building in radioactive waste disposal science.

The classification of river catchments into groups with similar biophysical characteristics is useful to understand and predict their hydrological behavior. The increasing availability of remote sensing and other large-scale geospatial datasets has enabled the use of advanced data-driven approaches to classify catchments using traits such as topography, geology, climate, land cover, land use, and human influence. Unsupervised clustering algorithms based on the Euclidean distance are commonly used for trait-based classification but are not suitable for highly dimensional data. In this study we present a new network-based method for multi-scale catchment classification, which can be applied to large datasets and used to determine the traits associated with different catchment groups. In this framework, two networks are analyzed in parallel: the first being where the nodes are traits and the second being where the nodes are catchments. In both cases, edges represent pairwise similarity, and a network cluster detection algorithm is used for the classification. The trait network is used to investigate redundancy in the trait data and to condense this information into a small number of interpretable categories. The catchments network is used to classify the catchments into clusters and to identify representative catchments for the different groups using the degree centrality metric. We apply this method to classify 9067 river catchments across the contiguous United States at both regional and continental scales using 274 non-categorical traits. At the continental scale, we identify 25 interpretable trait categories and 34 catchment clusters of sizes greater than 50. We find that catchments with similar trait categories are typically located in the same region, with different spatial patterns emerging among clusters dominated by natural and anthropogenic traits. We also find that the catchment clusters exhibit distinct hydrological behavior based on an analysis of streamflow indices. This network approach provides several advantages over traditional means of classification, including better separation of clusters, the use of alternate similarity metrics that are more suitable for highly dimensional data, and reducing redundancy in the trait information. The paired catchment-trait networks enable analysis of hydrological behavior using the dominant trait categories for each catchment cluster. The approach can be used at multiple spatial scales since the network topologies adjust automatically to reflect the trait patterns at the scale of investigation. Finally, the representative catchments identified as hub nodes in the network can be used to guide transferable observational and modeling strategies. The method is broadly applicable beyond hydrology for classification of other complex systems that utilize different types of trait datasets.

Spatial and temporal patterns of water supply and consumptive water use were analyzed from 475 Detailed Analysis Units by County (DAUCOs) spatial units across California during 2002 through 2016 to evaluate spatial and temporal variability and how it might associate with precipitation variability and other factors. Many, but not all, DAUCOs have relatively low total water supply variability compared to that of state-wide precipitation. Such low variability, in DAUCOs having sufficient diversity of water supply sources, is the result of switching between sources as needed to maintain a reliable total water supply. We used multiple approaches to explore these variations which involved four categories of water supply (local, groundwater, imported, and other) and two categories of water use (agricultural and urban). First, a cluster analysis of the volumetric water balance data identified a small set of clusters having similar magnitudes and proportions of water supply sources and water use—some of them composed of only a few DAUCOs but accounting for a disproportionate amount of the state’s water use. Second, a principal components analysis identified leading modes of anomalous water supply and water use among the 475 DAUCOs, capturing most of the time variation during 2002 to 2016. The most prominent mode exhibits a multi-year trend, most strongly involving increasing groundwater supply and agricultural water use, and decreasing urban water use and imported water supply. Over the study period, trends in both supply and use were pronounced, but differed considerably across California DAUCOs. One predominant subset of DAUCOs grew their agricultural water use with increased groundwater supply; in contrast to a widespread group of DAUCOs which reduced their urban water use. An important result for planners is our finding that variation in precipitation—itself important—is amplified by the human response to water supply availability and regulatory policy.

Hydrogeophysical methods have been increasingly used to study subsurface soil–water dynamics, yet their application beyond the soil compartment or the quantitative link to soil hydraulic properties remains limited. To examine how these methods can inform model-based evapotranspiration (ET) calculation under varying soil water conditions, we conducted a pilot-scale field study at an experimental maize plot with manipulated irrigation treatments. Our goal was to develop a workflow for (1) acquiring and inverting field electrical resistivity tomography (ERT) data, (2) correlating ERT to soil hydraulic properties, (3) spatially characterizing soil water stress that feeds into ET modeling (the FAO-56 model), and (4) evaluating the performance of ERT-based ET computation. Our results showed that ERT was able to capture decimeter-scale soil water content (SWC) dynamics from root water uptake and irrigation manipulation and the contrast of soil water stress between deficiently and fully irrigated maize. We also demonstrated the flexibility of using ERT to spatially integrate soil water stress in the soil volume of interest, which could be adjusted based on different crops and plot layouts. The integration of the ERT datasets into ET modeling provided insights into the spatial heterogeneity of the subsurface that has been challenging for point-based sensing, which can further our understanding of the hydraulic dynamics in the soil-plant-atmosphere continuum.

In this study, we develop a machine-learning (ML)-enabled strategy for selecting hillslope-scale ecohydrological monitoring sites within snow-dominated mountainous watersheds, with a particular focus on snow-soil–plant interactions. Data layers rely on spatial data layers from both remote sensing and hydrological model simulations. Specifically, a Landsat-based foresummer drought sensitivity index is used to define the dependency of the annual peak plant productivity on the Palmer drought severity index in the early growing season. Hydrological simulations provide the spatiotemporal dynamics of near-surface soil moisture and snow depth. In this framework, a regression analysis identifies the key hydrological variables relevant to the spatial heterogeneity of drought sensitivity. We then apply unsupervised clustering to these key variables, using the Gaussian mixture model, to group hillslopes into several zones that have divergent relationships regarding soil moisture, snow dynamics, and drought sensitivity. Using the datasets collected in the East River Watershed (Crested Butte, Colorado, United States), results show that drought sensitivity is significantly correlated with model-derived soil moisture and snow-free timing over space and time. The relationship is, however, non-linear, such that the correlation decreases above a threshold elevation and in a heavy snow year due to large snowpacks, lateral flow, and soil storage limitations. Clustering is then able to define the zones that have high or low sensitivity to drought, as well as the mid-elevation regions where sensitivity is associated with the topographic aspect and net potential radiation. In addition, the algorithm identifies the most representative hillslopes with road/trail access within each zone for installing monitoring sites. Our method also aims to significantly increase the use of ML and model-simulation results to guide critical zone and watershed monitoring activities.

About 80 % of the precipitation at the Colorado River s headwaters is snow, and the resulting snowmeltdriven hydrograph is a crucial water source for about 40 million people. Snowmelt from alpine and subalpine snowpack contributes substantially to groundwater recharge and river flow. However, the dynamics of snowmelt progression are not well understood because observations of the highelevation snowpack are difficult due to challenging access in complex mountainous terrain as well as the cost and labor intensity of currently available methods. We present a novel approach to infer the processes and dynamics of highelevation snowmelt contributions predicated upon stable hydrogen and oxygen isotope ratios observed in streamflow. We show that deuterium-excess (d-excess) values of stream water could serve as a comparatively cost-effective proxy for a catchment-integrated signal of high-elevation snowmelt contributions to catchment runoff. We sampled stable hydrogen and oxygen isotope ratios of the precipitation, snowpack, and stream water in the East River, a headwater catchment of the Colorado River, and the stream water of larger catchments at sites on the Gunnison River and Colorado River. The d-excess of snowpack increased with elevation; the upper subalpine and alpine snowpack (> 3200 m) had substantially higher d-excess compared to lower elevations (< 3200 m) in the study area. The d-excess values of stream water reflected this because d-excess values increased as the higher-elevation snowpack contributed more to stream water generation later in the snowmelt/runoff season. End-member mixing analyses based on the d-excess data showed that the share of high-elevation snowmelt contributions within the snowmelt hydrograph was on average 44 % and generally increased during melt period progression, up to 70 %. The observed pattern was consistent during 6 years for the East River, and a similar relation was found for the larger catchments on the Gunnison and Colorado rivers. High-elevation snowpack contributions were found to be higher for years with lower snowpack and warmer spring temperatures. Thus, we conclude that the d-excess of stream water is a viable proxy to observe changes in high-elevation snowmelt contributions in catchments at various scales. Inter-catchment comparisons and temporal trends of the d-excess of stream water could therefore serve as a catchment-integrated measure to monitor if mountain systems rely on high-elevation water inputs more during snow drought compared to years of average snowpack depths.

The two-term infiltration equation (Formula presented.) is commonly used to determine the sorptivity, (Formula presented.) (Formula presented.), and product, (Formula presented.) (Formula presented.), of the dimensionless multiple (Formula presented.) and saturated soil hydraulic conductivity (Formula presented.) (Formula presented.) from cumulative vertical infiltration measurements (Formula presented.) (L) at times (Formula presented.) (T). This reduced form of the quasi-analytical power series solution of Richardson's equation of Philip enjoys a solid physical underpinning but at the expense of a limited time validity. Using simulated infiltration data, Jaiswal et al. have shown this time validity to equal about 2.5 cm of cumulative infiltration. The goals of this work are twofold. First, we investigate the extent to which cumulative infiltration measurements larger than 2.5 cm bias the estimates of (Formula presented.) and (Formula presented.). Second, we investigate the impact of epistemic errors on the inferred time validities and parameters. Partial infiltration curves up to 2.5 cm of cumulative vertical infiltration improve substantially the agreement between actual and least squares estimates of (Formula presented.) and (Formula presented.). But this only holds if the data generating infiltration process follows Richardson's equation and experimental conditions satisfy assumptions of soil homogeneity and a uniform initial water content. Otherwise, autocorrelated cumulative infiltration residuals will bias the least squares estimates of (Formula presented.) and (Formula presented.). Our findings reiterate and reinvigorate earlier conclusions of Haverkamp et al. and show that epistemic errors deteriorate the physical significance of the coefficients of infiltration functions. As a result, the parameters of infiltration functions cannot simply be used in storm water and vadose zone flow models to forecast runoff and recharge at field and landscape scales unless these predictions are accompanied by realistic uncertainty bounds. We conclude that the time validity of Philip's two-term equation is an elusive theoretical quantity with arbitrary physical meaning.

Electricity and water systems in the Western US (WUS) are closely connected, with hydropower comprising 20% of total annual WUS generation, and electricity related to water comprising about 7% of total WUS electricity use. Because of these interdependencies, the threat of climate change to WUS resources will likely have compounding electricity impacts on the Western Interconnect grid. This study describes a WUS-wide water system model with a particular emphasis on estimating climate impacts on hydropower generation and water-related electricity use, which can be linked with a grid expansion model to support climate-resilient electricity planning. The water system model combines climatically-driven physical hydrology and management of both water supply and demand allocation, and is applied to an ensemble of 15 climate scenarios out to 2050. Model results show decreasing streamflow in key basins of the WUS under most scenarios. Annual water-related electricity use increases up to 4%, and by up to 6% during the summer months, driven by growing agricultural demands met increasingly through a shift toward energy-intensive groundwater to replace declining surface water. Total annual hydropower generation changes by +5% to −20% by mid-century but declines in most scenarios, with decreases in summer generation by up to nearly −30%. Water-related electricity use increases tend to coincide with hydropower generation declines, annually and seasonally, demonstrating the importance of concurrently evaluating the climate signal on both water-for-energy and energy-for-water to inform planning for grid reliability and decarbonization goals.

Antecedent hydrological conditions are recorded through the evolution of dissolved lithium isotope signatures (δ7 ${\delta }^{7}$Li) by juxtaposing two storm events in an upland watershed subject to a Mediterranean climate. Discharge and δ7 ${\delta }^{7}$Li are negatively correlated in both events, but mean δ7 ${\delta }^{7}$Li ratios and associated ranges of variation are distinct between them. We apply a previously developed reactive transport model (RTM) for the site to these event‐scale flow perturbations, but observed shifts in stream δ7 ${\delta }^{7}$Li are not reproduced. To reconcile the stability of the subsurface solute weathering profile with our observations of dynamic stream δ7 ${\delta }^{7}$Li signatures, we couple the RTM to a distribution of fluid transit times that evolve based on storm hydrographs. The approach guides appropriate flux‐weighting of fluid from the RTM over a range of flow path lengths, or equivalently fluid residence times. This flux‐weighted RTM approach accurately reproduces dynamic storm δ7 ${\delta }^{7}$Li‐discharge patterns distinguished by the antecedent conditions of the watershed. Plain Language Summary: Storm events often cause characteristic shifts in stream solute chemistry. Interpreting these signals offers insight into the water‐rock interactions occurring within watersheds. Here, we use lithium stable isotopes and reactive transport modeling to relate how long water spends in a catchment, or how deep water infiltrates through a catchment, to the extent of chemical weathering. We show that the first significant storm after a dry season exports more chemically evolved water, while a wet season storm releases less evolved, shallower, and younger water. Our results indicate that stream flow δ7 ${\delta }^{7}$Li in small watersheds offers a sensitive record of hydrological conditions prior to the storm, reflecting subtle shifts in the efficiency of the Critical Zone to generate, transport, and ultimately export solutes. Key Points: Stream lithium stable isotope ratios (δ7Li) recorded at high frequency over storm events are sensitive to antecedent conditionsA reactive transport model cannot produce observed shifts in stream chemistry through variations in flow rate aloneFlux‐weighting of model fluid outputs based on time‐varying fluid transit time distributions describes stream δ7Li over storm hydrographs [ABSTRACT FROM AUTHOR]

Mars has experienced drastic environmental evolution in terms of pH, redox, and desiccation. NASA's Curiosity rover discovered that the sediments of the Pahrump Hills member of the Murray formation in Gale Crater contained different redox states of Fe oxides. To interpret the observations, understanding the factors that control iron redox states within early Gale lakes on Mars is needed. Here, we present the results of a one-dimensional geochemical model of a closed-basin lake that considers the Fe(II) photo-oxidation reaction. Depending on the source of Fe(II) (groundwater vs. river flow), lake pH, advection rate, and atmospheric composition, we defined four types of chemical profiles in the lake; namely, Fe(II) dominated, Fe(III) dominated, carbonate precipitating, and redox-stratified profiles. The Fe(III) dominated profile corresponds to the end member case in which photo-oxidation effectively oxidizes the supplied Fe(II). The redox-stratified profile, interpreted as an intermediate condition between Fe(III) dominated profile and Fe(II) dominated profile, appears in broad parameter set when Fe(II) is supplied from the groundwater, typically with a low to moderate Fe(II) input flux at acidic to alkaline pH (pH 5–9) conditions; however, the low water flux due to the nature of the groundwater may be unable to explain the measured abundance of Fe oxides in the sediments. Another endmember case, the Fe(II) dominated profile, which would also be expected to generate mixed valence Fe oxides, occurs when the pH is acidic (pH 4–5); nevertheless, the acidic pH is inconsistent with the mineral assemblages of the sediments. Carbonate precipitating profiles were limited to the case in which Fe(II) is supplied by groundwater with high Fe input flux and alkaline pH of 8–9. These results may imply either the Fe(II) input flux was high enough for the redox-stratified case or Fe(II) photo-oxidation was suppressed by turbidity or by dusty/cloudy atmosphere, or possibly that Fe(II) oxides found in the Pahrump Hills member are, at least partly, of detrital origin. Although numerous other factors (ex., effect of lake depth, variable pH throughout the lake water, detailed chemical speciation) remain unconsidered, the diverse profiles in Fe redox resulting from Fe(II) photo-oxidation reaction sheds light on interpreting the variety and the evolution of redox condition on early Mars. [ABSTRACT FROM AUTHOR]

Climate change has several effects, including the changes of water discharge values and flood occurrences. In the present study five catchments are analysed using the European hydrology and climate data explorer. Two hydrological models and eight regional climate model simulations with three different representative concentration pathway (RCP) scenarios were selected for the investigation, focusing on the period 2071–2100 relative to 1971–2000. The catchments are located in different climate zones in Europe, therefore, it is possible to compare how climate and its changes determine mean and maximum water discharge. In addition, there is a special focus on the 10- and 50-year recurrence of floods. [ABSTRACT FROM AUTHOR]

Pacific Islands present unique challenges for water resource management due to their environmental vulnerability, dynamic climates, and heavy reliance on groundwater. Quantifying connections between meteoric, ground, and surface waters is critical for effective water resource management. Analyses of the stable isotopes of oxygen and hydrogen in the hydrosphere can help illuminate such connections. This study investigates the stable isotope composition of rainfall on O'ahu in the Hawaiian Islands, with a particular focus on how altitude impacts stable isotope composition. Rainfall was sampled at 20 locations from March 2018 to August 2021. The new precipitation stable isotope data were integrated with previously published data to create the most spatially and topographically diverse precipitation collector network on O'ahu to date. Results show that δ18O and δ2H values in precipitation displayed distinct isotopic signatures influenced by geographical location, season, and precipitation source. Altitude and isotopic compositions were strongly correlated along certain elevation transects, but these relationships could not be extrapolated to larger regions due to microclimate influences. Altitude and deuterium excess were strongly correlated across the study region, suggesting that deuterium excess may be a reliable proxy for precipitation elevation in local water tracer studies. Analysis of spring, rainfall, and fog stable isotope composition from Mount Ka'ala suggests that fog may contribute up to 45% of total groundwater recharge at the summit. These findings highlight the strong influence of microclimates on the stable isotope composition of rainfall, underscore the need for further investigation into fog's role in the water budget, and demonstrate the importance of stable isotope analysis for comprehending hydrologic dynamics in environmentally sensitive regions. [ABSTRACT FROM AUTHOR]

Determining the efficacy of restoration practices is critical for enhancing and rebuilding degraded ecosystems. A fundamental question in marine restoration design is whether to employ active (e.g. using living plants or oysters that form the biogenic habitat) or passive (e.g. restoring the hydrology and sediment materials at the site) restoration strategies. Furthermore, the identity of sources used in restoring biogenic habitats could influence the effectiveness of restoration efforts. We present results from two independent experimental restoration efforts designed to explore the efficacy of these different approaches for the recovery of Crassostrea virginica (eastern oyster) reef habitat. Conducted from 2015–2020 in Rhode Island, USA, the restoration experiments compared 1) passive (unseeded shell) vs. active (seeded spat on shell) restoration methods in Ninigret Pond, and 2) restored reefs seeded with hatchery sourced vs. wild oyster lineages in Quonochontaug Pond. In Quonochontaug Pond, the hatchery-sourced line exhibited greater mortality than the wild lines. Restored reefs in both projects remained viable all four years post-restoration; however, low recruitment continues to limit the long-term sustainability of restored reefs in these Rhode Island systems. Natural mortality varied spatially within each coastal pond, ranging from 33–81% per year. Across both experiments, oyster mortality increased with rising temperature, salinity, and disease prevalence. Our results suggest that utilizing active restoration with spat spawned from multiple sources, including wild populations, may improve the success of oyster restoration. Future habitat restoration efforts should consider the environmental variability associated with restoration sites and the source of restoration species during project planning. [ABSTRACT FROM AUTHOR]

The new scientific decade (2023-2032) of the International Association of Hydrological Sciences (IAHS) aims at searching for sustainable solutions to undesired water conditions – whether it be too little, too much or too polluted. Many of the current issues originate from global change, while solutions to problems must embrace local understanding and context. The decade will explore the current water crises by searching for actionable knowledge within three themes: global and local interactions, sustainable solutions and innovative cross-cutting methods. We capitalise on previous IAHS Scientific Decades shaping a trilogy; from Hydrological Predictions (PUB) to Change and Interdisciplinarity (Panta Rhei) to Solutions (HELPING). The vision is to solve fundamental water-related environmental and societal problems by engaging with other disciplines and local stakeholders. The decade endorses mutual learning and co-creation to progress towards UN sustainable development goals. Hence, HELPING is a vehicle for putting science in action, driven by scientists working on local hydrology in coordination with local, regional, and global processes. [ABSTRACT FROM AUTHOR]

Hydrological models are critical for water resources planning and management. The precision and reliability of the simulations hinge greatly on the accessibility and quality of available input data. Particularly in developing nations, the major challenge in modeling is the scarcity of fine-scale spatiotemporal input data, specifically precipitation. Remotely sensed weather data has been increasingly used in recent years. However, they possess bias compared to ground observations due to the nature of indirect measurement and may affect the simulated water balance. To address these limitations, we explored data assimilation techniques to improve the Global Precipitation Measurement product (IMERG) precipitation using limited ground observations. Multiple assimilation methods are applied by incorporating Linear scaling Correction Factor (CF) and Power Transformation Function methods (PF). The assimilated IMERG precipitation from the most effective method identified, is utilized in an eco-hydrological model, and the resulting stream flow simulations are validated against observed flow data. The findings indicate that assimilated precipitation enhances the monthly flow statistics in both CF and PF methods and also in conditional merged precipitation. An ensemble of hydrological simulations, outperformed those based on raw IMERG precipitation. Additionally, the hydrological simulations are compared with observed gauge precipitation data and the widely used Climate Forecast System Reanalysis (CFSR) dataset in data-limited watersheds. The simulations utilizing the assimilated IMERG dataset (NSE = 0.52) are comparable to gauge precipitation-based simulations (NSE = 0.61) and significantly superior to CFSR-based simulations (NSE=-0.2). These results highlight the potential of utilizing assimilated remote sensing data for hydrological modeling in data-limited watersheds, leading to improved simulation accuracy and reliability. [ABSTRACT FROM AUTHOR]

Fire-adaptive traits in plants of tropical and subtropical grassy ecosystems have been the subject of considerable global research, but only recently studied in pyrogenic Florida subtropical grasslands. Plant growth forms, belowground organs, and post-fire recovery strategies were studied for 198 grassland specialists in peninsular Florida. Community types (dry-mesic, mesic, wet-mesic, and wet) were sampled with 1m2 plots along the edaphic-hydrologic gradient and the association between these variables and fire-related plant traits was tested using fourth-corner analysis. Caulescent herbs (43), cespitose graminoids (27), and rosette herbs (31) are the most common growth forms among species sampled. Plants with epigeogenous and hypogeogenous rhizomes dominate the sample plots, including matrix graminoids, shrub geoxyles, and an acaulescent rhizomatous fire-resilient palm (Serenoa repens). Most species (163; 82%) exhibit resprouting, including 30 facultative resprouters and 133 obligate resprouters. All woody rhizomatous species are obligate resprouters, and 35 ephemeral herbaceous species are obligate reseeders. Community type was a better predictor of species abundances than hydrology, however, hydrology was significantly associated with species traits measured, particularly rhizome texture, with woody rhizomes prevalent in all but the wet sites. Belowground organs (xylopodia, geoxylic suffructices) and growth form were associated with frequent fire and phylogeny, suggesting fire regime as a driver of community phylogenetic diversity. Persistence, rapid resilience and co-occurrence of geoxyles align Florida subtropical grasslands with other global geoxyle grasslands. The old-growth, pyrogenic grassy ecosystems of peninsular Florida are the center of geoxyle diversification on the southeastern US coastal plain. [ABSTRACT FROM AUTHOR]

Rapid urbanization has an accelerated impact on catchment hydrology and geomorphology due to land use and land cover changes as a result of anthropogenic development activities in river catchment areas that cause increased surface runoff and higher sediment yield. The present study investigates the channel changes and sediment transport phenomena, including bed material movement from the upstream to the estuaries of the Pahang River Basin, Peninsula Malaysia. The river geometry survey associated with the spatial variation in sediment transport has confirmed that changes in river bed profile occur due to the erosion and sediment deposition along the Pahang River. The sediment size distribution for the Pahang River was very coarse sand and gravel after the December 2014 flood. The river geometry survey showed a gentle slope that can induce lower discharge typical of alluvial rivers relating to flood events. Quantitative assessment of sediment load is, therefore, of critical importance for future river basin management. [ABSTRACT FROM AUTHOR]

Mountainous zones are often characterized by complex orography and contacts between different aquifers that usually complicate the use of isotope hydrology techniques. The Apennine chain (Italy) and 10 mountain and mid‐mountain areas belonging to it are the objective of this study. An original isotopic data treatment, able to identify the most probable recharge area for several springs/springs' groups/wells, has been developed. The method consists of a two‐step approach: (1) the determination of the spring/wells computed isotope recharge elevation; (2) an advanced δ18O precipitation distribution model over the study area supported by statistical and GIS‐based procedures implemented by two processes: first, the clipping of precipitation δ18O values (depicted from the δ18O–elevation relationships obtained for each study area) over a most probable recharge area for each analyzed spring or well and, second, the calculation of the overlapping distribution between the spring/well mean δ18O values ± σ and the precipitation δ18O content for each outcropping aquifer. A new regional δ18O gradient covering 150 km latitudinal length of central Italy has been defined. Seven LMWL and δ18O–elevation relationships able to represent the local precipitation isotopic composition have been obtained. The mean elevation of the springs and wells recharge areas have been assessed by a comparison between the obtained gradient with nine δ18O gradients available in the literature and those obtained at a local scale. The new isotopic modeling approach can stress whether the mere isotope modeling based on the stable isotope of oxygen agrees with the hydrogeological setting of the study areas. [ABSTRACT FROM AUTHOR]

The development of a basin‐specific comprehensive flood record using publicly available data for the James River Basin, Virginia. Using mixed surface analysis maps (NOAA, NWS), historical hurricane records (IBTrACS, HURDAT2), and flood and river gauge records (NWS, USGS), a database is created for analysis. Related to creating this database, a novel classification of six unique weather systems (non‐tropical and tropical) responsible for flooding is presented. The analysis includes Two‐Way tables with observed marginal and joint probabilities in various potential flood, storm, and regional combinations. Results show 233 tropical systems passed within the 500‐km study area, with 12.4%, or 29 systems of those systems causing flooding within the basin. An additional 713 events were non‐tropical, composing the majority of flood sources in the basin. Non‐tropical systems are responsible for 84% of the worst floods recorded, leading to an increased frequency of 6% yearly since 1941 and nearly 770% between 2014 and 2018. The findings of this study will be the basis for assessing land use and population patterns in the area, along with a future compound flooding model for the lower James River Basin. [ABSTRACT FROM AUTHOR]

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Stable isotope techniques are precise methods for studying various aspects of hydrology, such as precipitation characteristics. However, understanding the variations in the stable isotope content in precipitation is challenging in Iran due to numerous climatic and geographic factors. To address this, forty-two precipitation sampling stations were selected across Iran to assess the fractional importance of these climatic and geographic parameters influencing stable isotopes. Additionally, deep learning models were employed to simulate the stable isotope content, with missing data initially addressed using the predictive mean matching (PMM) method. Subsequently, the recursive feature elimination (RFE) technique was applied to identify influential parameters impacting Iran’s precipitation stable isotope content. Following this, long short-term memory (LSTM) and deep neural network (DNN) models were utilized to predict stable isotope values in precipitation. Interpolated maps of these values across Iran were developed using inverse distance weighting (IDW), while an interpolated reconstruction error (RE) map was generated to quantify deviations between observed and predicted values at study stations, offering insights into model precision. Validation using evaluation metrics demonstrated that the model based on DNN exhibited higher accuracy. Furthermore, RE maps confirmed acceptable accuracy in simulating the stable isotope content, albeit with minor weaknesses observed in simulation maps. The methodology outlined in this study holds promise for application in regions worldwide characterized by diverse climatic conditions. [ABSTRACT FROM AUTHOR]

Hydrological drought is a pervasive and reoccurring challenge in managing water resources. Reservoirs are critical for lessening the impacts of drought on water available for many uses. We use a novel and generalized approach to identify periods of unusually low reservoir storage—via comparisons to operational rule curves and historical patterns—to investigate how droughts affect storage in 250 reservoirs across the conterminous U.S. (CONUS). We find that the maximum amount of water stored in reservoirs is decreasing, and that periods of unusually low storage are becoming longer, more severe, and more variable in (a) western and central CONUS reservoirs, and (b) reservoirs with primarily over‐year storage. Results suggest that reservoir storage has become less reliable and more vulnerable to larger deviations from desired storage patterns. These changes have coincided with ongoing shifts to the hydroclimate of CONUS, and with sedimentation further reducing available reservoir storage. Plain Language Summary: Drought in water systems is a major challenge in managing water resources. Reservoirs are important as they can lessen the impacts of drought on water availability for many users. However, they are impacted by drought as well. We use a novel and generally applicable method to identify when reservoir storage is unusually low, potentially from drought, at 250 reservoirs across the conterminous U.S. We find that the maximum amount of water stored in reservoirs is decreasing across the U.S. We also find that periods of unusually low storage are becoming longer and more severe in western and central U.S. regions as well as for certain types of reservoirs. This suggests that reservoir storage may be less reliable and more vulnerable to extreme conditions and may be further impacted by changing climate and hydrology across the U.S. and by sediment building up behind reservoirs. Key Points: Low‐storage periods are longer, more severe, and more variable in over‐year storage reservoirs and in the western and central CONUSLonger periods of low storage for some regions in recent years suggests decreased reservoir reliability in a changing hydroclimateMaximum annual storage is also declining across CONUS, furthered by storage losses from sedimentation [ABSTRACT FROM AUTHOR]

In this study, two hydrodynamic models, TELEMAC-2D and HEC-RAS 2D, were compared for their Rain-on-Grid (RoG) technique with a particular focus on runoff generation processes in a small and steep catchment. Curve number (CN) method was applied in both the models to simulate two single storm events up to 20 h of duration, whereas the Green-Ampt Redistribution (GAR) method was additionally applied in HEC-RAS 2D for a multi-peak flood event with sustained flow between the peaks. CN and GAR methods were compared for this flood event, and a sensitivity analysis of the GAR parameters was also done. Moreover, the two models were compared for their calibration process, computational time, mesh size and shape, and model availability, in general, as well as the results including inundated areas, water depth, and velocity. The results indicate that both the models are capable of reproducing short duration single storm floods. NSE and R2 for both models ranged from 0.70 to 0.90 and from 0.93 to 0.95. However, the models struggled to reproduce the long-duration multipeak flood event. The sensitivity analysis showed that the results are not very sensitive to the two GAR parameters which are responsible to influence the flow of the second peak in the flood event. Neither the CN nor the GAR infiltration method successfully replicated such events because the hydraulic models permanently lose infiltrated water from the domain. The returned sub-surface flow significantly contributes to river flow during these flood events; however, none of the model incorporates a return flow algorithm. [ABSTRACT FROM AUTHOR]

Two outstanding questions for the future of the Greenland Ice Sheet are (a) how enhanced meltwater draining beneath the ice will impact the behavior of large tidewater glaciers, and (b) to what extent tidewater glacier velocity is driven by changes at the terminus versus changes in sliding velocity due to meltwater. We present a two‐way coupled framework to simulate the nonlinear feedbacks of evolving subglacial hydrology and ice dynamics using the Subglacial Hydrology And Kinetic, Transient Interactions (SHAKTI) model within the Ice‐sheet and Sea‐level System Model (ISSM). Through coupled simulations of Helheim Glacier, we find that terminus effects dominate the seasonal velocity pattern up to 15 km from the terminus, while hydrology drives the velocity response upstream. With increased melt, the hydrology influence yields seasonal acceleration of several hundred meters per year in the interior, suggesting that hydrology will play an important role in future mass balance of tidewater glaciers. Plain Language Summary: Water draining under glaciers and ice sheets affects the friction between the ice and the bed, and controls how fast the ice can slide into the ocean, contributing to sea‐level rise. We present a framework for simulating the feedbacks between hydrology and ice flow. We investigate the relative influence of changes at the terminus of the glacier where it meets the ocean, versus changes in meltwater drainage, in determining how fast the glacier moves. Our modeling of Helheim Glacier in southeast Greenland highlights the importance of terminus effects up to 15 km from the terminus, and hydrology farther upstream, with increased melt yielding higher inland acceleration. These results suggest that meltwater will play an increasingly important role in the future behavior of glaciers. Key Points: We couple a subglacial hydrology model with an ice flow model to simulate the relationship between sliding velocity and effective pressureTerminus effects at Helheim Glacier drive velocity up to 15 km upstream, but seasonal hydrology controls velocity patterns further inlandIncreased melt accelerates ice inland of the main trunk, implying importance of hydrology in tidewater glacier future mass balance [ABSTRACT FROM AUTHOR]

Despite in‐situ observations of perennial firn aquifers (PFAs) at specific locations of the Antarctic ice sheet, a comprehensive continent‐wide mapping of PFA distribution is currently lacking. We present an estimate of their distribution across Antarctica in the form of a probability assessment using a Monte Carlo technique. Our approach involves a novel methodology that combines observations from Sentinel‐1 and Advanced SCATterometer (ASCAT) with output from a regional climate model. To evaluate our method, we conduct an extensive comparison with Operation Ice Bridge observations from the Greenland Ice Sheet. Application to Antarctica reveals high PFA probabilities in the Antarctic Peninsula (AP), particularly along its northern, northwestern, and western coastlines, as well as on the Wilkins, Müller, and George VI ice shelves. Outside the AP, PFA probability is low, except for some locations with marginally higher probabilities, such as on the Abbot, Totten, and Shackleton ice shelves. Plain Language Summary: We explore the presence of subsurface water storage within the firn layer in Antarctica, known as perennial firn aquifers (PFA), using a new method that combines satellite data and climate models. These PFAs, previously identified in Greenland, store and transmit meltwater, and could influence ice‐sheet behavior. Our study maps the likelihood of PFAs across Antarctica, finding high probabilities along the Antarctic Peninsula's northern and western coasts, as well as on specific ice shelves. Outside this region, PFAs are unlikely, except for a few spots with somewhat enhanced probability on Abbot, Totten, and Shackleton ice shelves. This inventory enhances our understanding of Antarctic hydrology and has broader implications for understanding ice‐sheet dynamics. Key Points: An innovative aquifer detection method, combining satellite and regional climate model data, is applied to AntarcticaThe evaluation of the methodology in Greenland, showing 91% correspondence, suggests good adaptability for AntarcticaThe Antarctic Peninsula stands out as the only region with high aquifer probability; in the rest of Antarctica, the likelihood is low [ABSTRACT FROM AUTHOR]

Water depth affects many aspects of wetland ecology, hydrology, and biogeochemistry. However, acquiring water depth data is often difficult due to inadequate monitoring or insufficient funds. Satellite-derived bathymetry (SBD) data provides cost-effective and rapid estimates of the water depth across large areas. However, the applicability and performance of these techniques for inland wetlands have not been thoroughly evaluated. Here, a time series of bathymetry data for inland wetlands in West Kentucky and Tennessee were derived from Landsat 8 images using two widely used empirical models, Stumpf and a modified Lyzenga model and three machine learning models, Random Forest, Support Vector regression, and k-Nearest Neighbor. We processed satellite images using Google Earth Engine and compared the performance of water depth estimation among the different models. The performance assessment at validation sites resulted in an RMSE in the range of 0.18–0.47 m and R2 in the range of 0.71–0.83 across all models for depths <3.5 m, while in depths >3.5 m, an RMSE = 1.43–1.78 m and R2 = 0.57–0.65 was obtained. Overall, the empirical models marginally outperformed the machine learning models, although statistical tests indicated the results from all the models were not significantly different. Testing of the models beyond the domain of the training and validation data suggested the potential for model transferability to other regions with similar hydrologic and environmental characteristics. [ABSTRACT FROM AUTHOR]

The accurate measurement of water vapor is essential for research about and the applications of meteorology, climatology, and hydrology. Based on the BeiDou PPP-B2b service, real-time precipitable water vapor (PWV) can be retrieved with the precise point positioning (PPP) software (XTW-PPP version 0.0). The experiment was conducted in Beijing in January 2023. Three solutions were designed with PPP using the BeiDou system only, the GPS system only, and the BeiDou-GPS combined solution. Real-time PWVs for the three solutions were validated with the ERA5 reanalysis data. Between the PWV values from the single BeiDou and ERA5, there was a bias of 0.7 mm and an RMSE of 1.8 mm. For the GPS case, the bias was 0.73 mm and the RMSE was 1.97 mm. The biases were less than 1 mm and RMSEs were less than 2 mm. Both the BeiDou and the GPS processing performed very well. But little improvement was found for the BeiDou-GPS combined solution, compared with the BeiDou system-only and the GPS system-only solution. This may be due to the poor handling of two different kinds of errors for the GPS and the BeiDou systems in our PPP software. A better PWV estimation with the two systems is to estimate PWV with a single system at the first step and then obtain the optimization by Bayesian model averaging. [ABSTRACT FROM AUTHOR]

Of concern to Colorado River management, as operating guidelines post-2026 are being considered, is whether water resource recovery from low flows during 2000–20 is possible. Here, we analyze new simulations from phase 6 of the Coupled Model Intercomparison Project (CMIP6) to determine plausible climate impacts on Colorado River flows for 2026–50 when revised guidelines would operate. We constrain projected flows for Lees Ferry, the gauge through which 85% of the river flow passes, using its estimated sensitivity to meteorological variability together with CMIP6-projected precipitation and temperature changes. The critical importance of precipitation, especially its natural variability, is emphasized. Model projections indicate increased precipitation in the upper Colorado River basin due to climate change, which alone increases river flows by 5%–7% (relative to a 2000–20 climatology). Depending on the river's temperature sensitivity, this wet signal compensates for some, if not all, of the depleting effects of basin warming. Considerable internal decadal precipitation variability (∼5% of the climatological mean) is demonstrated, driving a greater range of plausible Colorado River flow changes for 2026–50 than previously surmised from treatment of temperature impacts alone: the overall precipitation-induced Lees Ferry flow changes span from −25% to +40%, contrasting with a range from −30% to −5% from expected warming effects only. Consequently, extreme low and high flows are more likely. Lees Ferry flow projections, conditioned on initial drought states akin to 2000–20, reveal substantial recovery odds for water resources, albeit with elevated risks of even further flow declines than in recent decades. Significance Statement: Increasing temperatures have led to concerns that Colorado River flows will be permanently reduced due to global warming. Here, we analyze precipitation, surface temperature, and streamflow (at Lees Ferry) in the upper Colorado River basin since 1895 revealing precipitation to be the largest contributor to runoff variability while temperature variability has been a much smaller contributor. New climate model projections indicate increased precipitation in the upper basin due to global warming, acting to increase river flows by 5%–7% during 2026–50. This wet signal compensates for some of the depleting effects from further basinwide warming. Critically, the large intrinsic variability in precipitation is demonstrated to yield a wider range of future Colorado River flows than previously foreseen based solely on considering temperature effects. [ABSTRACT FROM AUTHOR]

ABSTRACT River regulations have resulted in changes in the hydrology and particle budgets of fluvial systems. Since the 19th century, many rivers have been significantly modified to control flood hazards, to gain land from swamp areas for agricultural purposes, and to stabilize river‐levels and lake‐levels to facilitate navigation. These dramatic changes of the river courses have impacted the sediment budgets and grain‐size dissemination along them as well as the sediment distribution at the delta mouths in the downstream lakes, which could lead to slope instabilities. Deposits of such catastrophic lacustrine mass movements caused by delta collapses have been, for instance, observed in Lake Brienz (Switzerland), where relatively thick (0.5 to 1.3 m) and voluminous (>1 million m3) megaturbidites are stacked in the deep basin witnessing these processes. This study uses sediment cores and seismic data to reconstruct the megaturbidites' history in Lake Brienz. Data reveal that mass‐movement deposits, originating from the Aare Delta, one of the two main inflows, have mean ages of 1853, 1905, 1942 and 1996 ce and that they were unprecedented in, at least, half a millennium. The fact that the numbers of floods and earthquakes have not changed radically over this time period implies that human impact is the most likely explanation for these failure events. Therefore, the recurrent delta collapses are attributed to the focused sediment accumulation at the front of the channelized inflow in the proximal delta region, caused by the modification of the Aare River through its straightening and channelization during the late 19th century. These findings indicate that river regulation can affect delta sedimentation, leading to autocyclic delta collapses. Those collapses, in turn, can potentially generate tsunami waves, representing an additional natural hazard for shoreline communities. [ABSTRACT FROM AUTHOR]

Climate warming exacerbates the degradation of the mountain cryosphere, including glacier retreat, permafrost degradation, and snow cover reduction. These changes dramatically alter the local and downstream hydrological regime, posing significant threats to basin-scale water resource management and sustainable development. However, this issue is still not adequately addressed, particularly in mountainous catchments. We developed an integrated cryospheric–hydrologic model, the FLEX-Cryo model, to comprehensively consider glaciers, snow cover, and frozen soil and their dynamic impacts on hydrological processes. Taking the mountainous Hulu catchment located in the upper Heihe River of China as a case study, we utilized the state-of-the-art climate change projection data under two scenarios (SSP2-4.5 and SSP5-8.5) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to simulate the future changes in the mountainous cryosphere and their impacts on hydrology. Our findings showed that under the medium-emission scenario (SSP2-4.5) and high-emission scenario (SSP5-8.5), by the end of the 21st century, the glacier will completely melt out around the years 2051 and 2045, respectively. The annual maximum snow water equivalent is projected to decrease by 41.4 % and 46.0 %, while the duration of snow cover will be reduced by approximately 45 and 70 d. The freeze onset of seasonally frozen soil is expected to be delayed by 10 and 22 d, while the thaw onset of permafrost is likely to advance by 19 and 32 d. Moreover, the maximum freeze depth of seasonally frozen soil is projected to decrease by 5.2 and 10.9 cm per decade, and the depth of the active layer will increase by 8.2 and 15.5 cm per decade. Regarding hydrology, catchment total runoff exhibits a decreasing trend, and the tipping point of glacier runoff occurs approximately between 2019 and 2021. Permafrost degradation will likely reduce the duration of low runoff in the early thawing season; the discontinuous baseflow recession gradually transitions into linear recessions, and the baseflow increases. Our results highlight the significant changes expected in the mountainous cryosphere and hydrology in the future. These findings enhance our understanding of cold-region hydrological processes and have the potential to assist local and downstream water resource management in addressing the challenges posed by climate change. [ABSTRACT FROM AUTHOR]