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Simulation‐optimization methods are commonly used in groundwater pollution source identification. Traditional simulation‐optimization methods require multiple calls to the numerical model, which leads to a considerable computational burden. Surrogate models based on machine learning can replace numerical models while maintaining accuracy. Previous studies have focused on the fitting accuracy of surrogate models, this study emphasizes the importance of the precision of surrogate models for the inversion process. We use the analytic hierarchy process to integrate ConvLSTM, convolutional neural network, and BiLSTM to improve the precision of the surrogate model. GMS is used to construct numerical models of two hypothetical cases and a practical case. Compared with the best results of the single deep learning methods, the integrated surrogate model improves the precision of the solution of the two hypothetical cases by 90% and 26%, respectively. In addition, the accuracy of the pollution source information obtained by incorporating the integrated surrogate model into the optimization model is higher than that obtained by ConvLSTM as the surrogate model. The inversion results of 7 metaheuristic optimization algorithms are compared through two hypothetical cases, and then the optimization algorithm with higher accuracy is applied to the solution of the practical case. To obtain more accurate results, we reobtain a batch of training data by resampling and training the integrated surrogate model. The results show that constructing an integrated surrogate model and selecting an optimization algorithm can improve the solution accuracy of the simulation‐optimization method. This research provides a new perspective for the construction of simulation‐optimization methods.

In water-stressed regions of the world, managed aquifer recharge (MAR), the process of intentionally recharging depleted aquifers, is an essential tool for combating groundwater depletion. Many groundwater-dependent regions, including the Central Valley in California, USA, are underlain by thick unsaturated zones (ca. 10 to 40 m thick), nested within complex valley-fill deposits that can hinder or facilitate recharge. Within the saturated zone, interconnected deposits of coarse-grained material (sands and gravel) can act as preferential recharge pathways, while fine-textured facies (silts and clays) accommodate the majority of the long-term increase in aquifer storage. However, this relationship is more complex within the vadose zone. Coarse facies can act as capillary barriers that restrict flow, and contrasts in matric potential can draw water from coarse-grained flow paths into fine-grained, low-permeability zones. To determine the impact of unsaturated-zone stratigraphic heterogeneity on MAR effectiveness, we simulate recharge at a Central Valley almond orchard surveyed with a towed transient electromagnetic system. First, we identified three outcomes of interest for MAR sites: infiltration rate at the surface, residence time of water in the root zone and saturated-zone recharge efficiency, which is defined as the increase in saturated-zone storage induced by MAR. Next, we developed a geostatistical approach for parameterizing a 3D variably saturated groundwater flow model using geophysical data. We use the resulting workflow to evaluate the three outcomes of interest and perform Monte Carlo simulations to quantify their uncertainty as a function of model input parameters and spatial uncertainty. Model results show that coarse-grained facies accommodate rapid infiltration rates and that contiguous blocks of fine-grained sediments within the root zone are >20 % likely to remain saturated longer than almond trees can tolerate. Simulations also reveal that capillary-driven flow draws recharge water into unsaturated, fine-grained sediments, limiting saturated-zone recharge efficiency. Two years after inundation, fine-grained facies within the vadose zone retain an average of 37 % of recharge water across all simulations, where it is inaccessible to either plants or pumping wells. Global sensitivity analyses demonstrate that each outcome of interest is most sensitive to parameters that describe the fine facies, implying that future work to reduce MAR uncertainty should focus on characterizing fine-grained sediments.

Although groundwater and surface water are often treated as individual water compartments in hydrological cycle studies, they essentially originate from one source. Such a split approach restricts the optimal usages of these water resources in several water management applications. The present study aims to shed light on the complex interaction of surface–groundwater interactions in terms of groundwater recharge from drainage network towards the adjacent aquafer and conversely, groundwater discharge from the aquifer towards the drainage network in the Gharehsoo River Basin (GRB), with the enclosed Ardabil aquifer, located in northwest Iran. To that end, the Soil and Water Assessment Tool (SWAT), as the surface hydrological model was fully coupled with the latest version of the Modular Three-Dimensional Finite-Difference Groundwater Flow (MODFLOW-NWT) (Newton–Raphson Technique to improve the solutions of unconfined groundwater-flow problems). The total study period, i.e. 1978–2012 was split into two intervals for calibration (1988–2012) and validation (1978–1987). To facilitate and expedite the calibration of the coupled model, first we calibrated SWAT and MODFLOW-NWT independently against the observed streamflow and groundwater head time series, respectively. Afterwards, we recalibrated the coupled model SWAT-MODFLOW. To link these two models, the surface and sub-surface water flow components are exchanged between the Disaggregated Hydrological Response Units (DHRUs) of SWAT with the MODFLOW-NWT’ grid cells. In addition, three more flow components are sequentially exchanged: the deep percolation from SWAT to MODFLOW-NWT, baseflow/groundwater discharge from MODFLOW-NWT to SWAT, and the river heads from SWAT to MODFLOW-NWT. The results of the application show that the coupled model satisfactorily, quantified by R2 ≥ 0.5, simulates streamflow and particularly, groundwater heads. In fact, both observations and simulations indicate that, owing to an ongoing overexploitation of the aquifer, heads have been decreased steadily over the studied period which has led to a parallel decline of the groundwater storage. Moreover, the analysis of the stream–aquifer exchange flows indicates that groundwater discharge towards the stream-network (effluent conditions) is orders of magnitude higher than the opposite process (influent conditions). In addition, findings reveal that many of the tributaries across the GRB have shifted from a perennial regime to ephemeral/intermittent system over the past decades. The provided and well-tested coupled model would be a viable asset to assess a wide range of plausible scenarios to identify most effective and practical water resource management schemes to recover the severely depleted surface water and groundwater resources of the GRB.

The accurate prediction of groundwater contamination is challenging due to uncertainties arising from the inherent heterogeneity of aquifers, inadequate site characterization, and limitations in conceptual mathematical models. These factors can result in an underestimation of contaminant concentrations. For effective contaminant prevention and control, it is important to estimate the probability of exceeding the allowed threshold for contaminant concentrations, known as the failure probability of groundwater contamination. Computing small failure probabilities using classical Monte Carlo simulation (MCS) requires computing a large number of samplers to converge to a stationary target value, which is time‐consuming. To address this, in this paper, we develop a novel approach for calculating small failure probabilities, known as subset simulation (SS) coupled with preconditioned Crank‐Nicolson Markov chain Monte Carlo (pCN‐SS), which combines subset simulation with preconditioned Crank‐Nicolson Markov chain Monte Carlo (pCN‐MCMC) to promote computational efficiency. We have tested the performance of the proposed algorithm in both a mathematical example and a numerical case study of groundwater contamination. The results demonstrate that pCN‐SS provides improved accuracy and efficiency for evaluating small failure probabilities for high‐dimensional groundwater contamination, specifically for hydraulic conductivity as a source of uncertainty. Compared to classical MCS and traditional SS, pCN‐SS requires fewer model evaluations but produces stable and accurate results. Key Points The pCN‐SS is proposed to accurately estimate the small failure probability in high‐dimensional groundwater contaminant transport problems The results demonstrate that the pCN‐SS can offer both accuracy and efficiency in estimating small failure probabilities The pCN‐SS surpasses traditional subset simulation in accurately estimating small failure probabilities

Managed aquifer recharge (MAR) is an effective approach to mitigate groundwater decline and spring depletion in karst systems impacted by excessive exploitation. However, the hydrogeological complexity of karst aquifers makes groundwater quantity and quality highly sensitive to human activities, posing challenges for MAR implementation. This study develops an integrated multi-method framework – combining isotopic analysis, flow monitoring, tracer tests, and numerical modeling – to evaluate the effects of MAR and groundwater extraction on karst aquifer dynamics, with a case study in the Baotu Spring system (Jinan, China). To enhance the accuracy of recharge rate quantification, an enhanced isotope mixing model that reduces uncertainties in estimating groundwater recharge ratios from multiple sources was developed, and the MAR rate settings were refined by establishing a quantitative relationship between effective MAR rates and water release rates through river flow monitoring. To improve the solute transport simulations' reliability, we conducted field tracer tests to constrain the effective porosity of the karst aquifer – a parameter typically poorly constrained in such systems. Furthermore, we validated the applicability of the equivalent porous media (EPM) model through rigorous hydrodynamic analysis, using field-measured fracture apertures to calculate Reynolds numbers and verify laminar flow conditions. The results demonstrate that surface water contributes > 80 % of recharge near MAR implementation zones, with MAR efficiency decreasing beyond critical river discharge thresholds. The karst aquifer exhibits laminar flow (effective porosity = 1.08 × 10−4), confirming the validity of the EPM approach. Modeling reveals that MAR significantly raises water tables, though efficiency varies by different MAR sources, and MAR-induced sulfate concentrations must be maintained below 56.5, 197.8, and 339.1 mg L−1 to meet China's Class I, II, and III groundwater standards, respectively. These findings provide practical guidelines for MAR implementation in temperate semi-arid fissured karst systems.

Surface flow and subsurface flow constitute a naturally linked hydrologic continuum that has not traditionally been simulated in an integrated fashion. Recognizing the interactions between these systems has encouraged the development of integrated hydrologic models (IHMs) capable of treating surface and subsurface systems as a single integrated resource. IHMs are dynamically evolving with improvements in technology, and the extent of their current capabilities are often only known to the developers and not general users. This article provides an overview of the core functionality, capability, applications, and ongoing development of one open-source IHM, ParFlow. ParFlow is a parallel, integrated, hydrologic model that simulates surface and subsurface flows. ParFlow solves the Richards equation for three-dimensional variably saturated groundwater flow and the two-dimensional kinematic wave approximation of the shallow water equations for overland flow. The model employs a conservative centered finite-difference scheme and a conservative finite-volume method for subsurface flow and transport, respectively. ParFlow uses multigrid-preconditioned Krylov and Newton–Krylov methods to solve the linear and nonlinear systems within each time step of the flow simulations. The code has demonstrated very efficient parallel solution capabilities. ParFlow has been coupled to geochemical reaction, land surface (e.g., the Common Land Model), and atmospheric models to study the interactions among the subsurface, land surface, and atmosphere systems across different spatial scales. This overview focuses on the current capabilities of the code, the core simulation engine, and the primary couplings of the subsurface model to other codes, taking a high-level perspective.

When simulating groundwater flow in unconfined and convertible aquifers using a groundwater model with the block‐centered finite‐difference approach, such as MODFLOW, it frequently encounters drying and rewetting of cells. Although many drying and rewetting simulation methods have been proposed in the past, balancing simulation accuracy and convergence capability all at once is difficult. MODFLOW‐2005, which has second‐order accuracy, employs a trial‐and‐error method, but it suffers from computational instability when large quantities of grid cells are dried. MODFLOW‐NWT adopts the upstream‐weighting approach and Newton iteration method to ensure the stability of the drying and rewetting simulations. However, the upstream‐weighting approach has only first‐order accuracy, and the Newton iteration method is complex to implement because it necessitates the establishment of an additional Jacobian matrix. The methods employed by MODFLOW‐NWT are also available in MODFLOW 6, therefore it inherits both the strengths and weaknesses of MODFLOW‐NWT. In this study, a new method, Picard iteration‐based always active cell (PAAC), is proposed. Similar to MODFLOW‐NWT, the PAAC method also uses dry cells as active cells. The PAAC method, however, does not use the upstream‐weighting approach and has second‐order accuracy. Moreover, it ensures good convergence stability even under the Picard iteration method. In addition to discussing the algorithm, five cases were used to comprehensively compare the simulation effects of the PAAC method with MODFLOW‐2005 and MODFLOW‐NWT, including an analytical solution, repeated drying‐rewetting of multi‐layer grids, pumping well problem, perched aquifer problem and a nearly dry single‐layer grid, which verified the practicability of the PACC method. Key Points A new physically‐based method to simulating the drying‐rewetting problems of groundwater model, Picard iteration‐based always active cell The new method performed robust convergence even with the Picard iteration method and a general PCG solver The new method achieved second‐order accuracy

Groundwater plays a critical role in maintaining streamflow during low-flow periods. However, accurately quantifying groundwater flow still remains a modeling challenge. Prolonged low-flow or drought conditions necessitate long-term simulations, further increasing the complexity of achieving reliable results. To address these issues, a novel modeling framework (HYD module in LoFloDes) that integrates a one-dimensional (1D) river module with two-dimensional (2D) groundwater module via bidirectional coupling, enabling robust and accurate simulations of both groundwater and river dynamics throughout their interactions, especially over extended periods, was developed. The HYD module was applied to the Rur River, calibrated using gridded groundwater data, groundwater and river gauge data from 2002 to 2005 and validated from 1991 to 2020. During validation periods, the simulated river and groundwater levels generally reproduced observed trends, although suboptimal performance at certain gauges is attributed to unmodeled local anthropogenic influences. Comparative simulations demonstrated that the incorporation of groundwater–river interactions markedly enhanced model performance, especially at the downstream Stah gauge, where the coefficient of determination (R2) increased from 0.83 without interaction to 0.9 with interaction. Consistent with spatio-temporal patterns of this interaction, simulated groundwater contributions increased from upstream to downstream and were elevated during low-flow months. These findings underscore the important role of groundwater contributions in local river dynamics along the Rur River reach. The successful application of the HYD module demonstrates its capacity for long-term simulations of coupled groundwater–surface water systems and underscores its potential as a valuable tool for integrated river and groundwater resources management.

The Nubian Sandstone Aquifer (NSA) has been the main source of water in the western desert of Egypt since 1985. This aquifer is subject to excessive groundwater withdrawal in the region, causing declines in potentiometric heads and a deterioration of groundwater quality. Consequently, investigations were undertaken to provide baseline water quality information for groundwater management. Water quality data from wells in the study area were manipulated using a Geographic Information System (GIS), statistical analyses (SPSS), graphical simulations (such as Piper and Gibbs diagrams), water quality index (WQI) and a simple geochemical model (NETPATH). This assessment and the use of hydrochemical indices indicated that chemical reactions between the aquifer matrix and groundwater are likely to be the main factors controlling the chemical composition of groundwater in the area. Also, the results of WQI showed that the majority of the collected water points (about 89%) were unsuitable for potable use due to elevated iron concentrations. The NETPATH modeling results suggest that dedolomitization, silicate weathering and dissolution of halite processes are the main geochemical processes that influence changes in the chemical composition along groundwater flow-paths.