Physical properties of soils are ubiquitously heterogeneous. This spatial variability has a profound, yet still partially understood, impact on conservative transport. Moreover, molecular diffusion is often a disregarded process that can have an important counter-intuitive effect on transport: diffusion can prevent non-Fickian tailing by mobilizing mass otherwise trapped in low velocity zones. Here, we focus on macroscopically homogeneous soils presenting small scale heterogeneity, as described by the Miller-Miller theory. We then analyze the dynamic control of soil heterogeneity, advection and diffusion on conservative transport. We focus especially on the importance of diffusion and of its tortuosity-dependent spatial variability on the overall transport. Our results confirm previous finding that relatively high Péclet number systems are highly sensitive to the degree of heterogeneity, which promotes non-Fickian transport. Also, diffusion appears to have a profound impact on transport, depending on both the degree of heterogeneity and the Péclet number. For a high Péclet number and a very heterogeneous system, diffusion leads to the counter-intuitive decrease of non-Fickian spreading described previously. This is not observed for a low Péclet number due to the impact of the spatial variability in the diffusion coefficient, which appears to be a significant controlling factor of transport by promoting or preventing the accumulation of mass in low velocity zones. Globally, this work (1) highlights the complex, synergistic effect of soil heterogeneity, advective fluxes and diffusion on transport and (2), alerts on potential upscaling challenges when the spatial variability of such key processes cannot be properly described.

Summer streamflow predictions are critical for managing water resources, however warming-induced shifts from snow to rain regimes impact low flow predictive models. Additionally, reductions in snowpack drive earlier peak flows and lower summer flows across the western United States increasing reliance on groundwater for maintaining summer streamflow. However, it remains poorly understood how groundwater contributions vary interannually. We quantify recession limb groundwater (RLGW), defined as the proportional groundwater contribution to the stream during the period between peak stream flow and low flow, to predict summer low flows across three diverse western US watersheds. We ask: 1) how do snow and rain dynamics influence interannual variations of RLGW contributions and summer low flows?; and 2) which watershed attributes impact the effectiveness of RLGW as a predictor of summer low flows? Linear models reveal that RLGW is a strong predictor of low flows across all sites, and drastically improves low flow prediction compared to snow metrics at a rain-dominated site. Results suggest that strength of RLGW control on summer low flows may be mediated by subsurface storage. Subsurface storage can be divided into dynamic (i.e., variability saturated) and deep (i.e., permanently saturated) components, and we hypothesize that interannual variability in dynamic storage contribution to streamflow drives RLGW variability. In systems with a higher proportion of dynamic storage, RLGW is a better predictor of summer low flow because the stream is more responsive to dynamic storage contributions compared to deep-storage-dominated systems. Overall, including RLGW improved low flow prediction across diverse watersheds.

Understanding the distribution of soil moisture is notoriously difficult in topographically complex regions that are subject to both large-scale climate gradients and fine-scale effects of terrain, vegetation, and soil structure. Remote sensing approaches capture large-scale moisture patterns but are limited in spatial and temporal resolution, while commercial field sensors remain too expensive to deploy intensively over large spatial extents. Here, we demonstrate the use of low-cost (<20 USD) custom sensors to create a large monitoring network of surficial (0–15 cm depth) volumetric soil moisture content (VMC) across Great Smoky Mountains National Park (GSMNP) (NC, TN, USA). In laboratory tests, temperature-calibrated VMC values approached the accuracy of commercial probes. We deployed over 80 sensors across multiple watersheds, topographic positions, and a 1,800-m elevation gradient, and created hierarchical models to understand associations of VMC with spatial (30-m resolution) and temporal (daily) variables related to water supply and demand. Elevation had the strongest association with VMC, with a fivefold increase across the gradient reflecting 1.5-fold changes in both (increased) precipitation and (decreased) evapotranspiration; slope angle was a strong mediating factor. Common proxies for moisture including topographic convergence index were not associated with VMC, likely due to limited contributions of surface drainage to local water balance. Our model predicted daily VMC of a set of validation sensors with a root mean square error of 4.8%, which may be improved by site-specific field calibration. Our study indicates that spatially extensive, field-based soil moisture networks are practical, accurate, and an important component of regional environmental monitoring.

This study investigates how environmental flows (e-flows) can be designed as dynamic operating policies to optimize long-term economic and ecosystem performance in reservoir systems. The main goal is to provide e-flow solutions that contribute to better preparedness and flexibility of hydro-systems to face multiyear stress periods, reducing the impact of water crises. The methodology framework combines a fish-flow model with a multi-objective evolutionary algorithm to construct multiple environmental water demand curves and capture the opportunity cost of different levels of ecosystem preservation. The water demand curves applied to a stochastic dynamic hydro-economic model then derive dynamic e-flow policies that balance immediate and future water use tradeoffs. The approach, termed dynamically adaptive environmental flows (DAE-flows), is demonstrated on the Paraná River Basin, Brazil, a large-scale hydropower system. Results show that the approach can adjust e-flows (coordinated with other hydro-system releases) over the time horizon, sacrificing them at certain times at the expense of some ecosystem loss, but improving long-term ecosystem functioning. A long-term approach to adaptation also yields better results for the environment without imposing a hard constraint to hydropower during droughts. Even under a drier climate change scenario, this allowed maintenance and improvement of environmental performance in most years, so during severe droughts the water could still be reallocated to hydropower but at a lesser cost to the environment.

In probabilistic flood modeling, uncertainty manifests in frequency of occurrence, or histograms, for quantities of interest, including the Flood Extent and hazard rating (HR). Such modeling at the field-scale requires the identification of a more efficient alternative to the Standard Monte Carlo (SMC) method that can reproduce comparable output probability distributions with a relatively reduced sample size, including detailed histograms of quantities of interest. Latin hypercube sampling (LHS) is the most evaluated alternative for fluvial floods but yields no considerable sample size reduction. Potentially better alternatives include adaptive stratified sampling (ASS), Quasi Monte Carlo (QMC) and Haar-wavelet expansion (HWE), which are yet unevaluated for probabilistic flood modeling. To fulfill this gap, LHS, ASS, QMC, and HWE are compared to quantify sample size reduction to reproduce output detailed histograms—for Flood Extent, and average and maximum HR—while keeping the difference below 10% to the reference SMC prediction. The comparison is done for two test cases with two (i.e., inflow discharge and Manning's coefficient) and three (i.e., further including the ground elevation) input random variables, and a real case with five input random variables. With two input random variables, all four alternatives yield sample size reductions, with QMC and HWE considerably outperforming the others; with three and more input random variables, HWE becomes inflexible and LHS underperforms. Still, QMC is a better choice than ASS to boost sample size reduction for the real case and shall be preferred in probabilistic flood modeling. Accompanying research codes are openly available online.

Bayesian model selection (BMS) and Bayesian model justifiability analysis (BMJ) provide a statistically rigorous framework for comparing competing models through the use of Bayesian model evidence (BME). However, a BME-based analysis has two main limitations: (a) it does not account for a model's posterior predictive performance after using the data for calibration and (b) it leads to biased results when comparing models that use different subsets of the observations for calibration. To address these limitations, we propose augmenting BMS and BMJ analyses with additional information-theoretic measures: expected log-predictive density (ELPD), relative entropy (RE) and information entropy (IE). Exploring the connection between Bayesian inference and information theory, we explicitly link BME and ELPD together with RE and IE to highlight the information flow in BMS and BMJ analyses. We show how to compute and interpret these scores alongside BME, and apply the framework to a controlled 2D groundwater setup featuring five models, one of which uses a subset of the data for calibration. Our results show how the information-theoretic scores complement BME by providing a more complete picture concerning the Bayesian updating process. Additionally, we demonstrate how both RE and IE can be used to objectively compare models that feature different data sets for calibration. Overall, the introduced Bayesian information-theoretic framework can lead to a better-informed decision by incorporating a model's post-calibration predictive performance, by allowing to work with different subsets of the data and by considering the usefulness of the data in the Bayesian updating process.

Dissolved oxygen (DO) is critical for aquatic ecosystems, however, few studies have focused on the long-term DO dynamics in hyporheic zones, which are a function of both transport (hydrologic exchange between river and hyporheic zone) and uptake by biogeochemical reactions or respiration. We explore the dynamics of temperature and DO at 10, 20, and 35 cm depth beneath the East River, Colorado, from July–October 2017 (relatively normal water year) and April to October 2018 (comparatively low flow year), enabled by distinctive, in-situ, high frequency (Δt = 5 min) sensors that provided continuous time-series from the undisturbed study site over 14 months. We expect that hyporheic DO, which has a regular daily fluctuation pattern, is supplied by the surface water (at all times we estimate downwelling) and that diurnal hyporheic DO temporal patterns should be aligned with diurnal hyporheic temperature patterns. However, this was not found to be the case. Hyporheic DO becomes depleted briefly at 20 and 35 cm depths in 2017, and at all three hyporheic depths for extended periods in 2018. Whereas diurnal temperature fluctuations have consistent timings of maxima and minima, hyporheic DO rarely has as regular a pattern, and daily ranges are inconsistent. Rainfall events caused some of these changes to diurnal hyporheic DO patterns without repeatable effects. Antecedent snowpack conditions influence streamflow dynamics and therefore hyporheic DO dynamics in this alpine river. These results also point to the strong and variable influence of hyporheic microbial communities regulating hyporheic DO.

Fluid flow in fracture porosity in the Earth’s crust is in general accompanied by crystallization or dissolution depending on the state of saturation. The evolution of the microstructure in turn affects the transport and mechanical properties of the rock, but the understanding of this coupled system is incomplete. Here, we aim to simulate spatio-temporal observations of laboratory experiments at the grain scale (using potash alumn), where crystals grow in a fracture during reactive flow, and show a varying growth rate along the fracture due to saturation differences. We use a multiphase-field modeling approach, where reactive fluid flow and crystal growth is computed and couple the chemical driving force for grain growth to the local saturation state of the fluid. The supersaturation of the fluid is characterized by a concentration field which is advected by fluid flow and in turn affects the crystal growth with anisotropic growth kinetics. The simulations exhibit good agreement with the experimental results, providing the basis for upscaling our results to larger scale computations of combined multi-physical processes in fractured porous media for applications as groundwater protection, geothermal and hydrocarbon reservoir prediction, water recovery, or storing H2 or CO2 in the subsurface.

The hydrological rhythm of floodplain wetlands affected by natural climate variability and human activities is the driving factor affecting the distribution of herbivorous geese habitats. It has always been challenging to determine its long-term variation, due to the lack of data on species distribution and broad wetland observation. In this study, the key normalized difference vegetation index (NDVI) threshold range suitable for herbivorous geese, greater white-fronted geese (Anser albifrons, GWG) and bean geese (Anser fabalis, BG), was discovered by combining GPS tracking data with Landsat satellite data. The suitable habitat area and distribution variation characteristics of GWG and BG from 1982 to 2020 were extracted based on the threshold range. The results showed that the suitable habitat area had a declining trend after 2003, that the distribution had moved toward the lake's center, and that its average elevation had decreased by around 1 m, particularly during the arrival period. The change of herbivorous geese habitat is caused by the continuous drying of Poyang Lake. The study can offer a rational scientific foundation for wetland waterbird protection and watershed hydrological management.

Morphological evolution of river deltas depends to a large extent on river discharges, which are usually highly unsteady due to natural hydrological cycles and anthropogenic regulations. However, it is unclear that how and to what extent the discharge fluctuations influence the delta morphology. In this study, we focus on the morphological response of the Yellow River Delta to the Water-Sediment Regulation Schemes, which generate impulsive floods and deliver high sediment load within a short time. Tracking the fate of fluvial sediment released by 10 historical events reveals that 51.3%, 19.7%, and 17.8% of fluvial sediment are deposited in the delta front, the subaerial delta and the prodelta, respectively. Hypopycnal and hyperpycnal flows occur alternately during different phases of the regulation schemes, and the latter contribute to 32% of the deposition volume with 10% of the duration. To explore the effects of river discharge schematizations on delta evolution, numerical experiments are conducted to compare models forced by unsteady and constant discharges, with the latter being a common practice in delta morphology modeling. We show that the constant-discharge simplification fails to capture the highly-depositional hyperpycnal flows, leading to dramatic underestimation of river mouth depositions. In addition, we demonstrate that including the 3D flow-sediment interactions are critical in reproducing the correct plume structures, bed surface sediment compositions and deposition patterns. Our study highlights the importance of proper river discharge schematizations and full consideration of flow-sediment interactions in modeling deltas affected by episodic river floods and high suspended sediment concentrations.

Water scarcity is a growing concern globally, with climate change and increasing population exacerbating the issue. Here, we introduce a new framework for assessing water availability in 708 Brazilian catchments that considers the effect of CO2 concentrations on potential evapotranspiration, uses CMIP6 bias-corrected climate change simulations, and presumes an open water balance assumption, while considering the human-aspect by incorporating water demand projections. We note an average reduction of water security in 81% of the analyzed catchments by 2100. Among these catchments, 37% presented a reduction of future water availability, while 63% undergo a worse scenario due to an increase in human water use, which highlights the role of the human aspect in water security assessment. Our study shows important aspects for both advancing future water availability studies and for drawing a picture of the impacts of changes in climate and water use on Brazilian future water security that may be useful for water resources management practices and advancing hydrologic studies.

Agriculture is the world's largest consumer of water resources, and accurate measurement of crop water footprint (CWF) can provide a scientific basis for evaluating the water use characteristics of agricultural production and guiding water management. The measurement of regional CWF requires large amounts of ground data, and remote sensing provides an effective means to scientifically measure the CWF on a large scale. In this study, we developed an effective means to estimate regional maize CWF based on global evapotranspiration (MOD16 and Global Land Data Assimilation System (GLDAS)-Noah) products in China. And we also calculated the CWF based on the FAO Penman-Monteith equation (FAO-PM) on the site scale in China. We assessed the accuracy of these methods by comparing them against eight eddy-covariance based flux tower measurements. Links and differences behind the results of the three water footprint calculations were analyzed in terms of the basic principles and characteristics of the calculations. The results showed that the CWF had a similar distribution based on the MOD16 and GLDAS-Noah, reflecting the influence of regional soil moisture on the CWF. Computational rationale analysis of the three quantitative methods showed that the ETc-based method from the FAO-PM model was similar to the MOD16- and GLDAS-Noah-based CWF in the wetter areas, two remote sensing-based methods considered the water constraints during soil evaporation and water dissipation during dry and wet canopy conditions, and were closer to reality than the ETc-based methods in measuring the CWF in arid and semi-arid regions.

In the montane western United States, where the majority of downstream water resources are derived from snowmelt, a warming climate threatens the timing and amount of future water availability. It is expected that the fraction of precipitation falling as snow will continue decreasing and the timing of snowmelt will continue shifting earlier in the year with unknown impacts on partitioning between evapotranspiration and streamflow. To assess this, we employ a Snow Storage Index (SSI) to represent the annual temporal phase difference between daily precipitation and daily modeled surface water inputs (SWI, the sum of rainfall and snowmelt), weighted by the respective amounts. We coupled the SSI metric with a Budyko-based framework to determine the effect of snow water storage on relative hydrologic partitioning across snow-influenced watersheds in the western U.S.

Mixing fronts at the interface of opposing flows are compressed at a constant rate. The resulting exponential stretching of fluid elements leads to enhanced chemical gradients and biogeochemical processes. This process is similar as what occurs in the pore space of 3D chaotic flows. However, it is so far not known how such fluid compression controls the amplitude of mixing and reaction rates in porous media. Here we derive analytical predictions for the mixing width, the maximum reaction rate and the reaction intensity in compressed mixing fronts as a function of the Péclet and Damköhler numbers. We developed an experimental setup providing pore scale measurements of mixing and reaction rates in mixing fronts at the interface of converging flows. The theory accurately predicts the scaling of mixing and reaction with the Péclet number both in porous micromodels and simple Hele-Shaw cells. Additionally, we found that the presence of pore scale heterogeneities in the porous micromodels enhances reaction rates by a factor of 4 compared to the Hele-Shaw cells. Using numerical simulations of pore scale velocity fields, we attributed this phenomenon to the enhancement of pore-scale compression due to the presence of grains in accelerating flows. These findings provide new insights into the dynamics of mixing-induced reactions in porous media.

Wildfires naturally occur in many landscapes, however they are undergoing rapid regime shifts. Despite the emphasis in the literature on the most severe hydrological responses to wildfire, there remains a knowledge gap on the thresholds of wildfire (i.e., burned area/drainage area ratio, BAR) required to initiate hydrological responses. We investigated hydrological changes in the Russian River Watershed (RRW) in California, a coastal, Mediterranean, drought-prone, wildfire-adapted ecosystem, following ten wildfires that burned 30% of the watershed. Our findings suggest that sub-watersheds of the RRW have not burned beyond an intrinsic, unknown, threshold required to initiate change. Using paired watersheds, we examined spatiotemporal patterns of pre-and-post wildfire hydrology with a rainfall-runoff hydrological model. Even though these successive wildfires burned 1%–50% of each sub-watershed (1%–30% at moderate/high severity), we found little evidence of wildfire-related shifts in hydrology. As a function of BAR, wildfire imposed limited effects on runoff ratios (runoff/precipitation) and runoff residuals (observations—model simulations). Our findings that post-wildfire runoff enhancements asymptote beyond 30% burn indicate that when a watershed is burned beyond a certain threshold, the magnitude of the hydrologic response no longer increases. Drought and storm conditions explained much of the variability observed in streamflow, whereas wildfire explained only moderate variability in streamflow even when wildfire accounted for >45% BAR. While the BAR in the RRW was sufficiently beyond previously reported minimum disturbance thresholds (>20% burned forest), the lack of hydrological response is attributed to buffering effects of wildfire adaptation and drought factors that are unique to Mediterranean ecoregions.

To better understand the changes in the atmospheric evaporative demand (AED) in the context of global warming, the anomaly contribution analysis is presented to estimate mid-long term contributions of meteorological factors to the AED. The Pearson correlation coefficient (RP) between the total contribution (ψ) of meteorological factors and the relative variation (ϕ) of the AED, and the Sen's trend slope (βS) of (ϕ, ψ) scatters are used to evaluate the applicability of the method. The smaller the values of |1 − RP| and |1 − βS|, the more applicable the method is. To validate the method, the reference crop evapotranspiration is employed as a proxy for the AED. The multi-year contribution analysis is used as a comparison approach, which can only investigate the dominant meteorological factors of the AED in long term. Moreover, the Huaihe River basin of China is taken as a case study. Results show that (a) the values of |1 − RP| and |1 − βS| in mid-long term are less than 0.1 in most cases when applying the anomaly contribution analysis, and the mid-long term contribution processes of meteorological factors to the AED are clearly demonstrated; and (b) the wind speed and sunshine hours are the two most dominant factors (the total absolute contribution exceeds 60%) in long term, but they are not always the dominant factors in mid-long term (e.g., wind speed in summer, and sunshine hours in winter). Therefore, the anomaly contribution analysis is a reasonable and effective method, which can help to gain insights into the changes in the AED.

Observations from a granitic watershed within a Mediterranean climate reveal the hydrologic and critical zone functioning of a perennial stream headwater and its upslope contributing area within a meadow system in the Sierra Nevada, California. Chemical analysis (diagnostic tools of mixing models, end member mixing analysis, tritium, etc.) and physical data (stream stage, piezometers, soil water, snowpack, etc.) indicate there are two primary pathways of water input into a headwater stream sourced from a mountain meadow. One input is a shallower and younger subsurface pathway with water that resembles snowpack chemistry, and the other a deeper and older subsurface pathway with water that reflects the chemistry of the groundwater derived from the contributing hillslopes. Multi-year observations reveal that regardless of snowpack amount, during the period of peak hillslope infiltration, shallow and deep pathways in the hillslope behave similarly to initiate headwater streams. However, during summer dry periods, similarities in active pathways within the meadow center are not maintained between high and low snowpack years. With less snow, perennial groundwater discharge within the meadow center is eliminated, becoming only a seasonal source at the meadow's outlet. At the meadow's edge, geophysically observed downslope thinning in saprolite thickness creates reduced lateral transmissivity and initiation points for headwater streams via enhanced groundwater discharge of upslope water. Combined, these findings suggest how loss of snowpack and critical zone structure can together mediate hydrologic function in a wet meadow system in a Mediterranean climate. Creating new understanding about the stability of hydraulic functioning in headwater wet-meadow systems under a changing climate.

A changing climate and often unregulated water extractions have exposed over 2 billion people to water stress worldwide. While water managers have explored a portfolio of options to reduce this stress, supply augmentation through reuse of treated municipal wastewater is becoming increasingly attractive. Wastewater treatment plants protect water quality and prevent sewage from contaminating waterways. Increasingly, this resource is utilized for numerous human (e.g., irrigation, drinking water, groundwater recharge) and conservation (e.g., stream and river recharge) needs in water stressed regions. To understand the role treated municipal wastewater plays in impacting conservation objectives we identified the intersection of wastewater treatment plant locations and occurrences of threatened and endangered (T&E) species in California and compared the permitted contribution of effluent to baseflow quantities of the receiving waterbody to assess the degree to which changes in effluent could affect instream waterbodies. We found a positive correlation between the presence of treatment plants and T&E species in California watersheds—a quarter of species have 100% of their range in watersheds with at least one treatment plant. This correlation is greatest for species associated with terraces and riparian habitat, followed by aquatic emergent vegetation and habitat. One-third of watersheds in our analysis can receive most of their cumulative watershed baseflow from effluent and are characterized by dense urbanization or agriculture. Our analysis demonstrates that the fates of T&E species and effluent are interconnected in ways important for water policy, suggesting that species conservation goals should be considered when making decisions about effluent reuse.

Groundwater is an important water source for evaporation, especially during dry conditions. Despite this recognition, plant access to groundwater is often neglected in global evaporation models. This study proposes a new, conceptual approach to incorporate plant access to groundwater in existing global evaporation models, and analyses the groundwater contribution to evaporation globally. To this end, the Global Land Evaporation Amsterdam Model (GLEAM) is used. The new GLEAM-Hydro model relies on the linear reservoir assumption for modelling groundwater flow, and introduces a transpiration partitioning approach to estimate groundwater contributions. Model estimates are validated globally against field observations of evaporation, soil moisture, discharge and groundwater level for the time period 2015–2021, and compared to a regional groundwater model. Representing groundwater access influences evaporation in 22% of the continental surface. Globally averaged, evaporation increases by 2.5 mm year−1 (0.5% of terrestrial evaporation), but locally, evaporation can increase up to 245.2 mm year−1 (149.7%). The groundwater contribution to transpiration is highest for tall vegetation under dry conditions due to more frequent groundwater access. The temporal dynamics of the simulated evaporation improve across 75% of the stations where groundwater is a relevant water source. The skill of the model for variables such as soil moisture and runoff remains similar to GLEAM v3. The proposed approach enables a more realistic process representation of evaporation under water-limited conditions in satellite-data driven models such as GLEAM, and sets the ground to assimilate satellite gravimetry data in the future.

The coincidence and superposition of flood processes from different rivers and regions tend to form compound flood events, determined by spatial relationship between diverse flood processes that cannot be accurately depicted and evaluated by existing dependence analysis methods. A framework, integrating multi-dimensional vine copula model and dependence evaluation system, was developed with a testing-oriented application to explore underlying dependence between two kinds of extreme runoff series (peak discharge and flood volume) extracted from the identified compound flood events in the upper reaches of the Yangtze River. Multi-dimensional regular vine (R-vine) copula models were established to depict the complex and diverse dependence, and corresponding vine structure was specified by the vine structure array that can reflect the sequence of tributaries flowing into the main stream and the spatial locations of different hydrometric stations. Dependence magnitude and association status were calculated and compared according to the optimal R-vine copula models and information theory. Comparison with existing methods demonstrated that dependence evaluation system could reflect nonlinear and local dependence characteristics and eliminate the effect of extreme runoff series from other hydrometric station on the dependence. The association status between different extreme runoff series of the upper Yangtze River and its tributaries were diverse in view of the impact of tributary flood inflow. The proposed framework can be regarded as an effective way for dependence modeling and evaluation of compound floods, thus providing a scientific reference for the risk analysis of water resources systems.