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.

Soil erosion and sedimentation problems remain a major water quality concern for making watershed management policies in the Mississippi River Basin (MRB). It is unclear whether the observed decreasing trend of stream suspended sediment loading to the mouth of the MRB over the last eight decades truly reflects a decline in upland soil erosion in this large basin. Here, we improved a distributed regional land surface model, the Dynamic Land Ecosystem Model, to evaluate how climate and land use changes have impacted soil erosion and sediment yield over the entire MRB during the past century. Model results indicate that total sediment yield significantly increased during 1980-2018, despite no significant increase in annual precipitation and runoff. The increased soil erosion and sediment yield are mainly driven by intensified extreme precipitation. Spatially, we found notable intensified extreme precipitation events in the cropland-dominated Midwest region, resulting in a substantial increase in soil erosion and sediment yield. Land use change played a critical role in determining sediment yield from the 1910s to the 1930s, thereafter, climate variability increasingly became the dominant driver of soil erosion, which peaked in the 2010s. This study highlights the increasing influences of extreme climate in affecting soil erosion and sedimentation, thus, water quality. Therefore, existing forest and cropland Best Management Practices (BMPs) should be revisited to confront the impacts of climate change on water quality in the MRB.

Many of the world-rivers are often ungauged or poorly gauged due to inadequate streamflow monitoring networks. Even with the limited available monitoring stations, only a few of them monitor both stage and discharge data. But when there is a need for estimation of discharges at a gauging station, where only stage data is monitored, one may employ a reverse routing technique using the stage data monitored at a nearby downstream gauging station. This study develops a novel single-parameter reverse-stage routing (RSR) model based on the second-order approximate water surface gradient governing equation to estimate stage and discharge at any scantily-gauged upstream river section using the known stage information available at the downstream section. A novel criterion is also developed for checking the applicability of the RSR model. Reverse routing experiments, carried out by the RSR model using different hypothetical downstream stage hydrographs in a number of hypothetical trapezoidal and rectangular channel reaches to reproduce the benchmark upstream stage and discharge hydrographs, demonstrate the good performance of the RSR model with the Nash–Sutcliffe Efficiency >98%, absolute volume conservation error <1.5%, and an absolute error in peak ≤1%. Subsequently, the RSR model was tested for three real-river case studies in India and Italy with good reproduction performances, and along with the development of the corresponding normal rating curves at the upstream river sections. The study results reveal that the parsimonious RSR model has good potential for solving reverse routing problems with stable numerical solutions for rivers under ungauged and scantily-gauged scenarios.

Filtering methods have been widely used to improve the forecast performances of hydrological models. Although extensive research has been carried out on filtering methods in the hydrology literature, researchers have not treated the noise statistics of filtering methods in much detail. Wrongly specified noise statistics can lead to degraded state estimates and even filter divergence. A powerful approach for state estimation in the presence of unknown noise statistics is the interacting multiple model method (IMM), which solves the noise specification problem by combining the strengths of multiple filters with different noise statistics. The IMM method has received little to no attention in the hydrologic context. This paper proposes a novel approach (IMM-CKF) for updating the states of hydrological models in the presence of unknown noise statistics by combining the cubature Kalman filter (CKF) with the IMM. The CKF is a popular nonlinear filter that has received little attention in the hydrology literature. The method is tested using a lumped hydrological model. The results of the synthetic case suggest the IMM-CKF can yield an accurate estimate for the switch as well as the changed states when there are abrupt changes in the noise statistics of true noises, even if sub-filter noise statistics are not equal to the real ones. The real case results (a forecast experiment) show that the IMM-CKF has successfully combined the strengths of different sub-filters and associated noise statistics. The IMM-CKF can be useful for hydrological forecasting, especially when useful information about the true noise statistics is unavailable.

Streamflow prediction in ungauged basins (PUB) is challenging, and Long Short-Term Memory (LSTM) is widely used to for such predictions, owing to its excellent migration performance. Traditional LSTM forced by meteorological data and catchment attribute data barely highlight the optimum data integration strategy for LSTM and its migration from data-rich basins to ungauged ones. In this study, we experimented with 1,897 global catchments and found that LSTM-corrected Global Hydrological Models (GHMs) outperformed uncorrected GHMs, improving the median Nash-Sutcliff efficiency (NSE) from 0.03 to 0.66. Notably, there was a large gap between traditional LSTM modeling in ungauged basins and autoregressive modeling in data-rich basins, and GHM-forced LSTM were an effective way to close this gap in ungauged basins. The spatial heterogeneity of the performance of GHM-forced LSTM was mainly influenced by three metrics (dryness, the leaf area index and latitude), which described the hydrological similarity among catchments. Weaker hydrological similarity among continental catchments results in larger variability in GHM-forced LSTM, with the best performance in Siberia (NSE, 0.54) and the worst in North America (NSE, 0.10). However, the migration performance of GHM-forced LSTM was significantly improved (NSE, 0.63) in ungauged basins when hydrological similarity was considered. This study stressed the advantages of GHM-forced LSTM and due significance should be attached to hydrological similarities among catchments to improve hydrological prediction in ungauged catchments.

Model Predictive Control (MPC) of Urban Drainage Systems (UDS) has been established as a cost-effective method to reduce pollution. However, the operation of large UDS (containing over 20 actuators) can only be optimized by oversimplifying the UDS dynamics, potentially leading to a decrease in performance and reduction in users’ trust, thus inhibiting widespread implementation of MPC procedures. A Heuristic And Predictive Policy (HAPPy) was set up, relying on the dynamic selection of the actuators with the highest impact on the UDS functioning and optimizing those in real-time. The remaining actuators follow a pre-set heuristic procedure. The HAPPy procedure was applied to two separate UDS in Rotterdam with the control objective being the minimization of overflow volume in each of the two cases. Results obtained show that the level of impact of the actuators on the UDS functioning changes during an event and can be predicted using a Random Forest algorithm. These predictions can be used to provide near-global optimal actuator settings resulting in the performance of the HAPPy procedure that is comparable to a full-MPC control and outperforming heuristic control procedures. The number of actuators selected to obtain near-global optimal settings depends on the UDS and rainfall characteristics showing an asymptotic RTC performance as the number of actuators increases. The HAPPy procedure showed different RTC dynamics for medium and large rainfall events, with the former showing a higher level of controllability than the latter. For medium events, a relatively small number of actuators suffices to achieve the potential performance improvement.

Flood retention lakes (RLs) are widely employed in rural-urban catchments with low impacts on the natural environment. However, insights are lacking regarding the control of climate conditions on RLs’ performances and how they are affected by different geographic configurations. This study applies a 2D hydrodynamic model to perform catchment-scale performance assessment of RLs beyond the scope of analytical and hydrological models. We conduct extensive numerical experiments of rainstorm-induced flooding in a rural-urban catchment with a constructed RL and blueprinted ones upstream. Results demonstrate an L-shaped band of satisfactory performance of the current RL in the frequency-duration diagram, which coincides with short return periods ( 4 hours), or short durations (< 3 hours) and moderate to long return periods (5 - 50 years); such L-shaped pattern is also valid for additional RLs and their combinations. With the increase in event size, the first two modes of RL performance (out of four) correspond to effective flood mitigation. When working jointly, RLs with series configurations are more effective in reducing the mainstream flood peaks, while parallel connections provide a greater spatial extent of flood hazard mitigation. For both series and parallel configurations, the upstream-weighted settings tend to outperform downstream-weighted ones under more extreme events; the decentralized arrangement in the urban area yields more benefits in spatial flood hazard mitigation compared to the centralized case. The study highlights the critical role of rainstorm severity (with possible spatiotemporal variabilities) in controlling RL performances despite various configurations and hydraulic settings.

The wetting properties of pore walls have a strong effect on multiphase flow through porous media. However, the fluid flow behavior in porous materials with both complex pore structures and non-uniform wettability are still unclear. Here, we performed unsteady-state quasi-static oil- and water-flooding experiments to study multiphase flow in two sister heterogeneous sandstones with variable wettability conditions (i.e., one natively water-wet and one chemically treated to be mixed-wet). The pore-scale fluid distributions during this process were imaged by laboratory-based X-ray micro-computed tomography (micro-CT). In the mixed-wet case, we observed pore filling events where the fluid interface appeared to be at quasi-equilibrium at every position along the pore body (13% by volume), in contrast to capillary instabilities typically associated with slow drainage or imbibition. These events corresponded to slow displacements previously observed in unsteady-state experiments, explaining the wide range of displacement time scales in mixed-wet samples. Our new data allowed us to quantify the fluid saturations below the image resolution, indicating that slow events were caused by the presence of microporosity and the wetting heterogeneity. Finally, we investigated the sensitivity of the multi-phase flow properties to the slow filling events using a state-of-the-art multi-scale pore network model. This indicated that pores where such events took place contributed up to 19% of the sample's total absolute permeability, but that the impact on the relative permeability may be smaller. Our study sheds new light on poorly understood multiphase fluid dynamics in complex rocks, of interest to for example, groundwater remediation and subsurface CO2 storage.

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.

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.

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.

The soil freezing characteristic curve (SFCC) represents the constitutive relationship between sub-zero temperature and unfrozen water content in soil. It governs the hydrologic and mechanical behaviors associated with freezing soil. Numerous studies have investigated the mechanisms of soil water freezing and attempted to predict SFCC using soil water characteristic curve (SWCC) and Clapeyron equation. However, limited attention has been given to the physical disparities between adsorbed and capillary water during freezing, including variations in pressures and water-ice interfaces. In this study, we present a novel theoretical model for predicting SFCC. The model determines the freezing point by calculating the chemical potential of soil water and ice with their respective pressures, thus capturing the distinctions in freezing behaviors between adsorbed and capillary water. All model parameters possess clear physical interpretations, and the model solely relies on the SWCC as input. The validity of the proposed model was confirmed through experimental measurements involving the water phase diagram, SWCCs, and the corresponding SFCCs of sandy, silty, and clayey soils. The model exhibits strong capabilities in predicting SFCC regardless of the soil type and outperforms the conventional method in predicting the SFCC of soil with high adsorbed water content. Model analyses were performed to investigate the effects of individual pore size, soil type, and initial water content on the freezing process, revealing the distinct contributions of adsorption and capillarity in soil water freezing. This study elucidates the mechanisms underlying soil water freezing, offering a theoretical framework for the analysis and prediction of frozen soil behaviors.

Spatial structures of natural variables are often very complex due to the different physical chemical or biological processes which contributed to the emergence of the fields. These structures often show non-Gaussian spatial dependence. Unfortunately, there are only a limited number of approaches that can explicitly consider non-Gaussian behavior. In this contribution, a very flexible way of defining non-Gaussian spatial dependence is presented. The approach is based on a kind of continuous deformation of fields with different Gaussian spatial dependence. Theoretical examples illustrate the methodology for a wide variety of non-Gaussian structures. A real-life example of groundwater quality parameters shows the practical applicability of the geostatistical model.

Contradictory interpretations of transient storage modeling (TSM) results of past studies hamper the understanding of how hydrologic conditions control solute transport in streams. To address this issue, we conduct 30 instantaneous tracer experiments in the Weierbach stream, Luxembourg. Using an iterative modeling approach, we calibrate TSM parameters and assess their identifiability across various hydrologic conditions. Near-stream groundwater monitoring wells and LIDAR scans of the streambed are used to evaluate the area of the hyporheic zone and of the submerged sediments for each experiment. Our findings show that increasing discharge enhances parameters interaction requiring more samples of TSM parameters to obtain identifiable results. Our results also indicate that transient storage at the study site is influenced by in-stream and hyporheic exchange processes during low discharge, likely due to the hyporheic zone's large extent and the relatively low water level compared to the size of slate fragments on the streambed. However, as discharge increases, in-stream storage zones become part of the advective channel and the lower localized stream water losses to the adjacent groundwater suggests a decrease of the hyporheic exchange on transient storage. The results obtained were utilized to generate a hydrograph for the study site illustrating the dynamic evolution of in-stream and hyporheic storage with varying discharge, providing insights into the expected influence of different transient storage processes prior to tracer experiments. Overall, our study enhances the understanding of the role of the hyporheic area and in-stream storage zones in transient storage and helps estimate TSM parameters more accurately.

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.

High latitude mountain environments are experiencing disproportionately adverse effects from climate change. The Gulf of Alaska (GoA) region is an embodiment of this change, particularly concerning a shifting hydrologic balance. Even so, the magnitude and contribution of fresh submarine groundwater discharge (fresh SGD) remains virtually unexplored within the region, though it has gained increasing attention globally due to its chemical significance and influence on coastal ecosystems. Here we provide the first regional estimates of fresh SGD to the GoA using two established water balance approaches. This is an effective way to distinguish the contribution of terrestrially derived fresh SGD, rather than the more commonly quantified total SGD which includes discharge that is driven by marine forces such as sea-level oscillations and density gradients. We compare the approaches and assess their capabilities in computing the magnitude of fresh SGD over a large regional scale. Mean annual fresh SGD flux ranges between 26.5 and 86.8 km3 yr−1 to the GoA, equivalent to 3.5%–11.4% of the total freshwater discharge. Contributions are highest in the Southeastern panhandle and lowest in the Cook Inlet basin, with the highest area normalized contribution occurring in the Prince William Sound. Fresh SGD exhibits high spatial and temporal variability throughout the region. Although freshwater discharge to the GoA is investigated considerably, the importance of fresh SGD has, thus far, been overlooked.

The proper numerical representation of physical processes in mechanistic hydrological models is essential to produce robust predictions. A common problem with numerical schemes in hydrological models is that multiple concurrent fluxes are calculated sequentially. Although the importance of errors introduced by inappropriate numerical schemes is well recognized in the literature, many hydrological models calculate concurrent fluxes sequentially. Here, two versions of the HYPE model are used to investigate the limitations of sequential calculations. A fourth order Gear-Nordsieck solution of the continuous state-space formulation of HYPE (I-HYPE) is developed to provide a robust solution, and a fixed-step implicit Euler scheme (IE-HYPE) is implemented to provide a computationally efficient and robust approximation of the I-HYPE simulations. In contrast to I-HYPE, results show that the original HYPE and the sequential calculation implemented in the continuous state-space formulation of HYPE (SQ-HYPE) typically simulate no interflow when soil moisture levels exceed the field capacity. The discrepancy between SQ-HYPE and I-HYPE grows with the size of the computation time step, and this implies a compromised representation of flow paths by sequential schemes. IE-HYPE provides responses comparable with I-HYPE for both daily and hourly time steps. IE-HYPE and SQ-HYPE are compared in terms of their groundwater representation, parameter identifiability, and predictive skills for two catchments. The sequential models have larger groundwater contributions to flow than IE-HYPE because the splitting errors in SQ-HYPE limit the interflow flux. IE-HYPE estimates of the groundwater flux are more consistent with literature values of groundwater contributions to flow for the basins studied.

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.

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.