Abstract Differentiation in water use strategies is essential for maintaining the function and resilience of dryland ecosystems. Under prolonged drought, dominant shrubs self‐organize into two typical spatial configurations: scattered with separated individuals and clumped with clustered individuals. How the coordination between root water uptake and leaf physiological traits drives water use strategies of self‐organized shrubs for adapting to drought remains poorly understood. To address this gap, soil moisture, morphological and root traits, leaf‐level physiological traits, and stable isotope (δ2H, δ18O, and δ13C) of scattered and clumped Vitex negundo were observed during 2022–2024 in the semi‐arid Loess Plateau. The scattered shrubs primarily utilized middle and deep soil water (62.3 ± 7.8%), associated with isolated canopies that promote infiltration into deep soil layers. In contrast, clumped shrubs relied on shallow and middle soil water (82.0 ± 6.5%), linked to aggregated canopies and shallow roots. Scattered shrubs showed tight stomatal regulation with high midday leaf water potential and intrinsic water use efficiency (iWUE) during dry season, whereas clumped shrubs exhibited relaxed stomatal regulation, with declines in midday leaf water potential and iWUE. In rainy season, scattered shrubs constrained gas exchange, while clumped shrubs enhanced stomatal conductance and photosynthesis. These findings indicate that scattered shrubs adopt an active deep‐water acquisition with conservative water utilization strategy, enhancing drought resistance, while clumped shrubs exhibit an opportunistic shallow‐water acquisition with acquisitive water utilization strategy, improving water capture efficiency for drought adaptation. This study reveals the ecohydrological processes of self‐organized vegetation in drylands and provides basis for vegetation allocation in ecological restoration.
Abstract Riverbanks have laterally sloping banks and are favorable environments for aquatic vegetation. A vegetated sloping bank alters the lateral profile of streamwise velocity across the entire channel. However, the impacts of flow, vegetation and side sloping banks on the velocity profile are unclear, and predicting the velocity profile on a vegetated sloping bank is challenging. In this study, rigid cylinders were used to mimic bank vegetation, and laboratory experiments were performed to clarify the mechanisms through which the lateral profile of depth‐averaged velocity changes in a trapezoidal channel with a vegetated sloping bank. The side slope and vegetation are the two main factors controlling the velocities in the bare bed and vegetated regions, varying the lateral profiles of depth‐averaged velocity across the entire channel. By using the flow continuity equation and momentum equation, a depth‐averaged dimensionless equation was developed for a vegetated trapezoidal channel. The new governing equation could not be solved analytically in the vegetated sloping bank region, so a difference solution method was used. The velocity prediction results were consistent with the measurements. If a new channel has a geometry condition (the ratio of channel width to flow depth and the bank slope) and vegetation density similar with a calibrated channel, the new method combining the difference solution method could be applied to predict the velocity profile across the new channel. Overall, this study provides an innovative approach to solving computational models that cannot be solved analytically in natural river environments, which provides technical support for river restoration.
Abstract Groundwater‐dependent ecosystems (GDEs) provide critical ecosystem services in arid and semiarid regions and help sustain ecosystems during droughts in humid areas. Accurate mapping of GDEs is essential for improving water resource management and ecosystem conservation. The distribution of GDEs remains largely unknown across contiguous China. In this study, remote sensing data, soil data, and modeled groundwater depth (GWD) data are integrated to map GDEs across both arid and humid regions of contiguous China. The mapping framework utilizes long‐term remote sensing data combined with drought‐related climatic data to characterize vegetation responses under selected hydroclimatic conditions. These conditions are selected to highlight the differences in vegetation responses between GDEs and non‐GDEs. By integrating vegetation temporal dynamics with hydroclimatic indicators, this approach enables systematic mapping of GDEs. The results indicate that high‐, moderate‐, and low‐potential GDEs account for 1.6%, 7.7%, and 22.1% of the mapped area, respectively. Assuming a vegetation rooting depth within 10 m, the GDE map shows a commission error of 13.6% against observed GWD. The reliability of the GDE map is further supported by vegetation responses to drought and the characteristic topographic settings of GDEs.
Abstract In many watersheds, urbanization and agriculture have altered stream hydrology and morphology, increasing flow variability, and sediment transport imbalances. Beaver dam analogs (BDAs) offer a process‐based alternative to conventional gray or hard‐engineered stormwater infrastructure to slow flow, promote sediment deposition, and enhance channel‐floodplain connectivity, but their hydrologic and geomorphic performance in stormwater‐driven, mixed land use watersheds is not well understood. This study describes the geomorphic and hydrologic responses 1 year after installing eight BDAs in an ephemeral, stormwater‐driven stream within a mixed‐land use watershed (68% agricultural, 28% developed, 4% forested) in Selinsgrove, Pennsylvania. Water‐level loggers showed significantly longer retention time in upstream BDA pools than downstream pools (p < 0.01), suggesting that BDAs moderate flow during storm events along the downstream gradient. Cross‐sectional surveys showed net sediment deposition in upstream BDAs 1–5 and net erosion at BDAs 6–8, with the most upstream BDAs accumulating up to 10–20 cm of vertical sediment. Substrate analyses revealed a shift from coarse to finer materials, with median grain size declining from 27 to 14 mm (p < 0.0001) and fine earth content (<2 mm) increasing by 6.7% post‐installation (p < 0.05), consistent with reduced stream energy and enhanced deposition upstream of BDAs. Together, these results document how BDAs alter hydrologic retention and sediment transport in a flashy, human‐impacted stream, suggesting that process‐based restoration responses commonly reported in perennial systems can also emerge under episodic, storm‐driven flow conditions.
Abstract Calibration of physics‐based hydrodynamic models of lake water temperature often faces challenges including limited observational data and parameter equifinality, that is, many parameterizations yield similar goodness‐of‐fit to observations. This study presents a framework to investigate how the number and location of observation depths used in calibration influence temperature prediction accuracy and parameter equifinality. We leverage a computationally efficient one‐dimensional physics‐based model (GLM; General Lake Model), and apply probabilistic parameter estimation within a Generalized Likelihood Uncertainty Estimation framework. Using 7 years of hourly water temperature data from Lake Mendota (WI, USA), we quantify parameter equifinality and assess predictive performance using multiple metrics across single‐depth, dual‐depth, and full‐profile observation. Results show that single‐depth deep‐water observations consistently outperform shallower depths and sometimes even full‐profile observations, significantly reducing equifinality and achieving accurate match throughout the water column. Contrary to expectations, full‐profile data do not consistently yield superior predictive performance across the water column. Instead, they dilute the influence of deep‐water observations on parameter selection. Dual‐depth scenarios show that single‐depth surface mixed layer observations can be vastly improved by adding a second observation depth. These findings provide practical examples for optimizing observational strategies and advancing lake hydrodynamic modeling. In particular, this work supports the management of drinking water reservoirs by informing the strategic selection of fewer, well‐placed deep‐water observations. In doing so, it focuses model development toward critical depths—for example, for drinking water reservoirs that commonly rely on deep‐layer intakes.
Abstract Previous studies have suggested that the linear superposition principle fails for thin‐layer overland flows, yet direct experimental evidence remains scarce. This work adopts high‐resolution particle image velocimetry (HR‐PIV) to measure the 2D flow fields of grain resistance and form resistance in thin‐layer overland flows. Experiments cover four bed roughnesses (grain resistance), three cylinder sizes (form resistance), and five flow discharges. Key results: (a) Streamwise velocity vertical profiles fit the Keulegan log law well ( R 2 > 0.88); the cross‐sectional mean flow velocity drops with larger roughness and cylinder diameter. (b) Flow resistance coefficient rises with roughness and cylinder size but falls with Reynolds number. (c) The wilcoxon test reveals that the summed increments of individual grain and form resistance are significantly lower than the measured total resistance increment ( p < 0.001), disproving linear superposition; the discrepancy enlarges as flow depth declines. A dimensionless product model ( R 2 = 0.86) is proposed and yields reliable predictions. (d) Coexisting resistances produce far larger high Reynolds stress regions and Reynolds stress than their separate superposition; the linear fitting slope of measured versus superimposed Reynolds stress increments equals only 0.701, demonstrating that linear superposition underestimates micro‐turbulence and explaining the mechanism of additional resistance. This study provides direct hydrodynamic evidence to revisit resistance superposition for overland flow and supports theoretical calculations in soil and water conservation.
Abstract Soil moisture is an essential climate variable that controls the exchange of water and energy between land and atmosphere, directly impacting land surface feedbacks. Satellite‐derived soil moisture provides valuable large‐scale information, but long‐term applications are limited by sensor lifespans, retrieval noise, and systematic biases. Earth system reanalyses rely on data assimilation to merge these observations with model dynamics. The mentioned limitations of the observations can degrade assimilation performance by propagating artifacts into model states. Here, we present a European daily soil moisture reanalysis for 2003–2023 that explicitly addresses these limitations by pre‐processing satellite observations prior to assimilation. We assimilate a newly developed Deep Learning (DL)‐enhanced AMSR‐E/2 soil moisture data set that provides a seamless daily record from 2003 to 2023 by reducing retrieval artifacts while preserving spatiotemporal consistency. Residual systematic biases are mitigated through climatological bias correction, and observation uncertainty is characterized using spatially distributed uncertainty estimates derived from Triple Collocation Analysis. The processed observations are subsequently assimilated into the encore Community Land Model (eCLM). This study evaluates whether DL‐enhanced observations improve reanalysis skill when assimilated into a continental‐scale ensemble land surface DA system. Evaluation against independent in situ measurements shows that assimilation of the DL‐enhanced AMSR‐E/2 reduces PBIAS from 28.68% to 3.28% and RMSE from 0.100 to 0.082 relative to open‐loop simulation, and outperforms assimilation of the original AMSR‐E/2 (PBIAS 15.13%, RMSE 0.094 ). Representing observation uncertainty as a spatially distributed field rather than a uniform value further improved temporal correlation at 66% of validation stations in a dedicated 3‐year experiment (2016–2018). Improvements are consistent across most in situ networks, with the largest gains observed in semi‐arid and transitional climate zones. At the same time, seasonal benefits are most pronounced during spring, autumn, and summer, where model dry‐down and vegetation dynamics are better represented. These improvements result in better preservation of hydrological signals and more physically consistent soil moisture dynamics. Our results demonstrate that observation pre‐processing is essential for effective soil moisture data assimilation, providing a reliable pathway for improved Earth system reanalyses.
Abstract Forest disturbance effects on peak flows have been debated for decades. Paired‐catchment analysis (PCA) has been criticized in relation to the long‐term stability of pre‐harvest regressions and the inability of chronological pairing to characterize changes in event frequency. Non‐stationary frequency analysis (NFA) avoids some concerns with PCA. However, a fundamental problem is that the concept of return period is ambiguous under non‐stationary conditions. This study re‐examined peak flow response to forestry at Camp Creek, a snow‐dominated 34‐km2 catchment in British Columbia, using five approaches: (a) chronologically paired PCA using adjacent Greata Creek as a control; (b) frequency‐paired PCA using maintenance‐of‐variance regression to fit the pre‐harvest relationship; (c) time‐series regression to relate Camp Creek annual maximum flows to annual peak snowpack water equivalent (SWE) and equivalent clearcut area (ECA); (d) NFA using SWE and ECA as covariates; (e) the design‐life‐level (DLL) approach. While all approaches reveal aspects of forestry effects on peak flows, only DLL provides a conceptually meaningful probabilistic approach to risk analysis under non‐stationary forest cover. However, the DLL‐based approach involves the application of NFA using ECA as a covariate. ECA may not provide a robust indicator of the effects of forest disturbance on peak flows in snow‐dominated catchments, particularly for situations in which logging occurs progressively over extended periods of time or where disturbance also occurs due to fire, insect infestation or disease. Further research should focus on developing alternative indices of forestry effects on peak flow, and on testing process‐based models using rigorous evaluation procedures.
Abstract Tile drainage is widely adopted in the U.S. Midwest to manage excess soil water and sustain agricultural productivity, yet its impact on key water budget components—particularly evapotranspiration (ET)—remains insufficiently understood under varying drainage designs and climate conditions. We enhanced the PFLOTRAN model by developing an ET module and validated it against HYDRUS simulations. Using the calibrated model, we analyzed tile drainage effects on hydrological dynamics at an Iowa corn site during a critical vegetative‐stage period and assessed the influence of tile depth and spacing. Simulations showed that tile drainage produced distinct spatiotemporal soil moisture patterns, with topsoil moisture aligned with the pipe layout. Compared to an undrained scenario, the tile drainage system at the experimental site increased ET by approximately 16% and significantly altered surface–subsurface water partitioning. Design experiments further showed that compared to the baseline depth (1.2 m), shallower drains (0.76 m) reduced tile flow by about 78%, decreased ET by approximately 10%, and substantially increased surface runoff. In contrast, tile spacing has a limited impact on ET (<2%) across the tested range (10.8–27 m) and exerts a secondary influence relative to tile depth, with consistently smaller variations across other water budget components. Using these findings, we proposed a framework to evaluate drainage design using ET deficit (Potential ET–ET) and economic cost, identifying 1.2 m depth and ≥18 m spacing as optimal at the study site. These findings contribute to a site‐adaptable digital twin framework for tile drainage design, offering a tool to optimize water management.
Abstract Understanding water fluxes and mixing dynamics in the unsaturated zone is crucial for ecohydrological studies. Yet, observations of soil moisture and soil water isotopes remain limited in spatial extent and temporal resolution, restricting modeling studies on the critical zone. A data‐driven Artificial Intelligence (AI) approach was applied in a first attempt to simulate daily soil moisture and soil water isotopes (δ2H, δ18O) across soil profiles of a mixed land use catchment using parsimonious climate and vegetation predictors. A sequential model combined a Long Short‐Term Memory network for soil moisture and a Random Forest model for soil water isotope simulation, using LSTM‐predicted soil moisture as input. Training used ca. 2 years of daily soil moisture and one year of monthly soil water isotopes from different depths (0–100 cm) under seven land uses in the Demnitzer Millcreek catchment (66 km2), NE Germany. The AI model captured seasonal and depth‐dependent hydrological patterns, outperforming a process‐based model previously applied in the catchment. Soil moisture simulations showed Kling‐Gupta Efficiency of 0.75–0.92 (RMSE: 1.81%–4.50%), while isotopes simulations achieved 0.80–0.88 (RMSE: 4.3–5.8‰ for δ2H, 0.6–0.9‰ for δ18O). However, the performance of AI model depended on the study period and catchment, showing overestimation in drier test periods and underestimation in wetter validation periods. Also, in this evapotranspiration‐dominated site, the model relied heavily on indirect climate predictors (e.g., relative humidity), derived from direct drivers (e.g., precipitation), reducing performance in short‐term hydrological responses. Our sequential model has potential as stand‐alone simulations or as a surrogate for process‐based models.
Abstract Hydrological connectivity regulates river–sea exchange by controlling freshwater delivery, salinity intrusion, sediment transport, and biogeochemical fluxes that sustain deltaic ecosystems. In distributary river systems, connectivity is mediated by multiple alternative downstream pathways, yet most assessments rely on network‐averaged indices and overlook cumulative regulation along interacting routes. Here, we develop a pathway‐inclusive, probabilistic framework to quantify river‐sea hydrological connectivity in distributary networks by retaining all feasible pathways and cumulative low‐head barrier effects. This formulation distinguishes structural redundancy from functional connectivity by accounting for the unequal contributions of different pathways to river–sea exchange. We apply the framework to the Guangdong–Hong Kong–Macao Greater Bay Area using multi‐decadal river network and barrier data sets from the 1980s to the 2010s. Total river network length increased from 7.9 × 10 6 m to 8.8 × 10 6 m, while sluice number increased more than sixfold and density twentyfold. Despite network expansion, functional river–sea connectivity declined by approximately 40% across maximum, average, and minimum connectivity metrics. The sharpest decline in average connectivity occurred between the 1990s and 2000s, indicating a system‐wide transition toward reduced pathway effectiveness. Connectivity losses were most pronounced along high‐capacity pathways that historically supported disproportionate exchange, whereas increased pathway number and small‐channel construction failed to offset network‐scale degradation. Spatially, connectivity declined most strongly upstream, despite barrier clustering near estuarine outlets, revealing non‐local, system‐wide regulatory effects. These results demonstrate that explicitly evaluating all feasible downstream pathways is necessary to diagnose connectivity loss, identify critical routes for river–sea exchange, and avoid misleading inferences based on network‐averaged indices.
Abstract Vapor pressure deficit (VPD), a key indicator of atmospheric dryness, has markedly increased across most global land areas due to progressive warming. Although site‐scale studies show that increasing VPD may enhance plant transpiration (Tran) and influence water and energy fluxes between land and atmosphere, this relationship has not been thoroughly investigated on the global scale. Here, we apply a binning approach to quantify the contribution of VPD to global Tran by ruling out the effect of soil moisture. To this aim, we integrate satellite‐based observations, reanalysis data sets, and simulations from sixteen Coupled Model Intercomparison Project Phase 6 (CMIP6) models into a coherent framework. VPD shows a broadly positive influence on Tran over most parts of the global land consistently across all data sets. Exceptions occur in tropical rainforests where reanalysis data and CMIP6 model outputs suggest a negative VPD‐Tran interplay. Attribution analysis suggests that increasing leaf area index reduces the positive effect of VPD on Tran in reanalysis data sets and CMIP6 models compared to satellite‐based observations. This discrepancy may reflect differences in the representation of the effective canopy‐level response to high atmospheric water demand, potentially involving stomatal regulation, plant hydraulics, and vegetation structure. Such differences may contribute to an underestimated transpiration response to VPD. These findings highlight the need to better represent physiological processes, hydraulic processes, and vegetation structural characteristics in process‐based models to capture vegetation responses to increasing water stress.
Abstract Significant changes in flow‐sediment dynamics and channel evolution have occurred in the Lower Yellow River owing to upstream damming. Notably, the post‐dam stage has witnessed an unexpected increase in movable bed roughness and more frequent dune development, driven by reduced sediment load, coarsened bed material, and altered hydraulic conditions. However, existing formulas for movable bed roughness, mostly calibrated using pre‐dam or flume data, fail to capture these new trends. A new formula is developed to calculate movable bed roughness, considering the effects of flow condition and bedforms, based on a new criterion for flow regime partition. The spatiotemporal variations in bedforms and the contributions of different factors were discussed. Results indicate that: (a) Froude number and relative water depth are key factors of movable bed roughness. The formula shows high accuracy, with the determination coefficients under different flow regimes larger than 0.70. (b) Lower flow regime is more likely to develop after the reservoir operation, especially in the braided reach under low discharges. The frequency of lower flow regime (embodied with ripples and dunes) under the low discharge generally increased by 1.3 times during the post‐dam stage. But there is a lag in the peak frequency of lower flow regime between the braided and transitional reach. (c) Although a slight increase existed in the contributions of relative water depth to movable bed roughness during the post‐dam stage, the flow condition, was still a dominant factor, with the contributions of Froude number exceeding 60% during both pre‐ and post‐ dam stages.
Abstract Reported values of average annual peak snow water equivalent (SWE) in the mountains of the western United States differ by more than a factor of five, from not enough water to fill Lake Mead, the largest reservoir in the region, to enough water to fill Lake Mead five times. These products are not created equal. Many vastly underestimate snow water storage, and guidelines regarding which products are more accurate are crucial for using these products in any scientific analysis. Here, we review existing products and compare them to each other and to SWE derived from 26 aerial LiDAR acquisitions in Colorado, California, and Washington mountains of the western U.S. Products that match well with observations include (a) resolution of 9 km or finer, (b) reliance on observations of either fractional snow‐covered area or in situ SWE measurements, and (c) a well‐calibrated model framework that includes both quality precipitation estimates and accurate snowmelt. Many of the energy balance models reviewed had too much melt, likely from too much incoming radiation. Over all three regions, for the 14 flights that occurred close to peak SWE (March–April), the Western U.S. SWE Reanalysis (WUS‐SR) is least biased in reproducing near‐peak SWE (bias of 1.6%) and also best represented the spatial distribution of SWE (smallest mean absolute error). SNODAS and both of the University of Arizona products provide reasonable estimates (biases of 7.8%–10.2%). At the basin scale, ERA5‐Land also has small bias (−17.5%), but due to its coarser resolution, less accurate spatial representations.
Abstract Recession analysis is a powerful tool for determining the hydraulic properties of a riparian aquifer. The basis of the method relies on analytical solutions of the nonlinear Boussinesq equation in horizontal aquifers. In the present work, the nonlinear Boussinesq equation for groundwater flow in sloping aquifers is tackled analytically via a refinement of its linear solution in a perturbation series framework. The analytical solution is composed of three parts: the representative depth α, the linear solution, and the correction term that is derived from the perturbation expansion. It can handle arbitrary initial water table profile, time‐varying stream level at the downstream end, as well as non‐uniform and non‐steady recharge and seepage distributions. The solution is evaluated for specific drainage conditions to obtain explicit equations for the outflow discharge rates and volume for both finite and semi‐infinite aquifers. The approximate solutions reproduce the original nonlinear behavior with high accuracy due to the introduction and proper quantification of the depth parameter α. The linear semi‐infinite solution is used as the basis for introducing a recession flow model and formulating a surrogate model that replicates the original nonlinear behavior. An additional model that is based on the quasi‐steady approach is also presented and constitutes a special case of the surrogate model. Explicit equations relating the discharge rate to the outflow volume are obtained for easy implementation in recession flow analysis. Application of the recession models to practical cases demonstrate their effectiveness in the estimation of the basin‐scale hydraulic conductivity and drainable porosity.
Abstract Urbanization is increasing at a global scale. Rapid urbanization is often accompanied by elevated nutrients, putting downstream habitats at risk of eutrophication and harmful algal blooms. Despite these critical consequences, most urban research compares urban systems to pristine environments or focuses solely on nutrient concentrations. This limits our understanding of how nutrient export varies spatially and temporally within a broader urban area. Here, we quantified nutrient loads at a high spatiotemporal resolution to determine what factors controlled nutrient export across an urban landscape. We collected biweekly nutrient samples (nitrate [NO3−] and soluble reactive phosphorus [SRP]) and streamflow at 20 urban stream sites across Fayetteville, Arkansas. We also conducted opportunistic storm sampling to understand how storms control nutrient loss from urban systems. We found that each of our watersheds had low spatial stability, indicating that the site with the highest nutrient loads varied over time in each watershed. We also found that both NO3− and SRP loads had a positive relationship with subwatershed impervious cover (NO3−: p < 0.0001, R2 = 0.72; SRP: p < 0.0001, R2 = 0.79) and a negative relationship with subwatershed canopy cover (NO3−: p < 0.001, R2 = 0.56; SRP: p < 0.05, R2 = 0.22), regardless of season. In contrast, riparian landscape characteristics were less predictive of nutrient export. During storms, relationships between load and landscape characteristics were highly variable and storm dependent. Our research suggests that conservation of greenspace at the watershed scale is needed in order to preserve water quality in urban areas.
Abstract Capillary pressure–saturation relationships are central to modeling multiphase flow in porous media, yet conventional formulations often neglect how pore‐scale flow organization and interfacial geometry evolve during displacement. Here, we investigate curvature‐derived capillary pressure during immiscible two‐phase displacement using microfluidic experiments and phase‐field simulations across ranges of wettability and viscosity ratio under low‐capillary‐number conditions. Microfluidic experiments provide benchmarks for invasion morphology and specific fluid–fluid interfacial area, while simulations provide access to interfacial curvature, capillary pressure, and pathway localization. The simulations reproduce the experimental magnitude, breakthrough interfacial area, and interfacial‐area generation efficiency of the Awn–Sinv relationship, supporting their use for constitutive analysis. We introduce a front‐localized flow‐focusing index to quantify whether invasion is distributed across many pores or concentrated into a few active pathways. This index is positively correlated with the magnitude of the local capillary‐pressure curve slope, showing that stronger pathway localization is associated with steeper capillary‐pressure changes. Representative pore‐scale events show that burst‐like filling broadens the curvature distribution and promotes abrupt pressure changes, whereas cooperative advance maintains a narrower curvature distribution and smoother pressure evolution. By fitting the coupled effective saturation, capillary pressure, and interfacial‐area data for viscously unfavorable and neutral‐to‐favorable regimes, we obtain two regime‐specific Se–Pc–Awn surfaces. These results show that a single surface does not adequately represent both displacement regimes. Instead, interfacial area acts as a regime‐dependent geometric mediator between saturation and capillarity, while flow focusing diagnoses the pore‐scale organization that controls the form of the capillary‐pressure relation. This framework provides a physically grounded basis for improving constitutive models of multiphase flow in porous media.
Abstract For much of the coastal United States, water quality continues to worsen due to excess nitrogen (N) from agricultural production and urban development. Restoration of freshwater wetlands, especially those connected to streams (i.e., fluvial wetlands), is a promising management option for curtailing watershed inputs of N to coastal embayments, with regional models assuming 77% retention of N by natural wetlands. In this study, we combine modeling, field measurements, and mass‐balance analysis to quantify N inputs and retention in a 56‐ha, 7‐year‐old restored fluvial wetland in southeastern Massachusetts, United States. Rapid watershed land‐use change, including development of ∼30% of forested land between 2001 and 2019, contributed to wastewater and turfgrass fertilizers accounting for the majority (80%) of watershed inputs of N to the restored wetland compared with the atmosphere (13%), a landfill (7%), and agricultural fertilizers (<1%). Field measurements showed that in‐stream processes retained 28% of surface water and groundwater inputs of nitrate (NO3−), which was slightly lower than N retention for restored wetlands and about half the N retention in natural wetlands. NO3− retention of 1,029 kg N yr−1 was not strongly correlated with stream residence time or NO3− inputs, but rather to stream temperature, pointing to environmental effects on in‐stream processes such as uptake and denitrification. Although NO3− retention may increase as the wetland matures, the current N removal rate suggests that regional models may overestimate N removal in restored wetlands and that wetland restoration is an unlikely substitute for centralized sewering or other forms of enhanced wastewater management.
Abstract Although overland flow strongly governs soil erosion and sediment transport, its response to desiccation cracking induced by drying‐wetting cycles is not fully understood. This study addresses this gap through laboratory rainfall simulations on a slope model subjected to three drying‐wetting cycles. Soil moisture and suction were monitored at shallow depth, crack patterns were quantified through image processing, and runoff and sediment yields were measured during rainfall events. Infrared thermography was applied to capture flow velocity and spatial organization. Results show that drying‐wetting cycles accelerated moisture loss, steepened suction gradients, and promoted the initiation and propagation of cracks. Crack ratio and total crack length increased markedly during the first two cycles but stabilized in the third, indicating that the slope surface had reached a state of structural equilibrium. Runoff generation was influenced by both antecedent moisture and crack development: higher initial moisture reduced infiltration capacity and produced rapid, high‐magnitude runoff, while progressive cracking enhanced infiltration and weakened runoff response in later cycles. Sediment yield closely followed runoff intensity, peaking in the second cycle due to the influence of reduced infiltration and structural weakening. Infrared thermography showed that leading‐edge velocity declined during rainfall while the continuity‐derived equivalent runoff depth increased, reflecting different responses of local tracer‐front propagation and integrated runoff generation to evolving flow connectivity and effective hydraulic resistance. Across cycles, leading‐edge velocity rose initially and then declined, consistent with the transition from high antecedent moisture and structural weakening to stabilized crack networks. Spatial indices further confirmed this shift, indicating progressive increases in surface roughness, enhanced flow dispersion, and shortened effective flow paths.
Abstract Drywells are vadose‐zone wells constructed for infiltration and recharge, typically positioned far above the water table. They provide for efficient infiltration, with high infiltration rates per land area, because the infiltration process is below the land surface. The drywell infiltration process is highly transient, with extremely high infiltration rates initially when the surrounding medium is dry, and monotonically declining rates as it wets and the hydraulic gradient decreases. The process is governed by the infiltration rate, the drywell geometry (depth, radius, casing), and the hydraulic properties of the adjacent subsurface media. This study develops a model for characterizing the drywell infiltration capacity (DIC)—the maximum infiltration rate at any time—as a unique function relating infiltration rate to cumulative infiltration. The model depends solely on the drywell's geometry and the hydraulic properties of the medium. An analytical solution is derived with the Green‐Ampt approach to describe the transient nature of infiltration and to provide a direct method to delineate the DIC function. Numerical experiments are conducted for a range of scenarios and properties, which confirm that, while the filling time of the drywell itself varies among infiltration rates, all cases converge to a single characteristic DIC curve once the well is full. Each drywell configuration, with its unique properties and subsurface settings, exhibits its characteristic DIC function. The proposed model and methodology provides a parsimonious framework for infiltration planning and real‐time management by enabling prediction of infiltration rates directly from cumulative infiltrated volume, offering a practical and robust approach for stormwater infiltration and groundwater‐recharge applications.
