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Geochemical modeling is an important tool in environmental studies, and in the areas of subsurface and surface hydrology, pedology, water resources management, mining geology, geothermal resources, hydrocarbon geology, and related areas dealing with the exploration and extraction of natural resources. The book fills a gap in the literature through its discussion of geochemical modeling, which simulates the chemical and physical processes affecting the distribution of chemical species in liquid, gas, and solid phases. Geochemical modeling applies to a diversity of subsurface environments, from the vadose zone close to the Earth's surface, down to deep-seated geothermal reservoirs.The book is addressed to students, teachers, other professionals, and to the institutions involved in water, geothermal and hydrocarbon resources, mining, and environmental management. The book should prove useful to undergraduate and graduate students, postgraduates, professional geologists and geophysicists, engineers, environmental scientists, soil scientists, hydrochemists, and others interested in water and geochemistry.

\"Arid and semi-arid regions face major challenges in the management of scarce freshwater resources under pressures of population, economic development, climate change, pollution and over-abstraction. Groundwater is commonly the most important water resource in these areas. Groundwater models are widely used globally to understand groundwater systems and to guide decisions on management. However, the hydrology of arid and semi-arid areas is very different from that of humid regions, and there is little guidance on the special challenges of groundwater modelling for these areas. This book brings together the experience of internationally-leading experts to fill a gap in the scientific and technical literature. It introduces state-of-the-art methods for modelling groundwater resources, illustrated with a wide-ranging set of illustrative examples from around the world. The book is valuable for researchers, practitioners in developed and developing countries, and graduate students in hydrology, hydrogeology, water resources management, environmental engineering and geography\"-- Provided by publisher.

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

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.

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

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.

Coal mining has resulted in pollution of groundwater in mining areas, which poses a risk to human health. However, understanding of groundwater evolution in mining areas and the associated implications remains insufficient. This study collected 13 groundwater samples from an abandoned coal mine in southwestern China. Ionic ratio analysis, hydrochemical simulation, health risk assessment, and entropy-weighted water quality index (EWQI) were applied to characterize the groundwater quality and the associated risks to human health. Monte Carlo analysis was used to quantify the uncertainty in the health risk assessment. The results indicated that the groundwater samples are of the HCO 3 -Ca and SO 4 -Ca water chemistry types. Water–rock interaction and mining activities were the main processes regulating groundwater hydrochemistry. Acid mine drainage was mainly responsible for elevated groundwater sulfate in the study area. EWQI ranged from 13 to 515 (mean of 111), and 75% of the samples fell within classes 1 and 2, meeting the World Health Organization (WHO) drinking water standards. Health risk assessment indicated adults to be more at risk from groundwater ingestion than children, with this result confirmed by uncertainty analysis. This study comprehensively examined groundwater evolution and its potential impacts through the example of a typical mining area. The results provides valuable insights into the identification of factors affecting groundwater, the evolution of hydrochemical processes, and the sustainable development of groundwater resources in mining areas globally. 

A newly developed concept called ‘groundwater footprint’ is used to reveal the degree of sustainable use of global aquifers by calculating the area relative to the extractive demands; globally, this footprint exceeds aquifer area by a factor of about 3.5, and excess withdrawal is centred on just a few agriculturally important aquifers. Striking a balance on groundwater usage In many parts of the world, groundwater is being extracted for agricultural use and human consumption at a greater rate than the Earth's natural systems can replace it. Tom Gleeson and colleagues estimate the true scale of the problem using a newly developed concept called the 'groundwater footprint' — defined as the area required to sustain groundwater use and groundwater-dependent ecosystem services. The authors find that globally, the groundwater footprint exceeds the aquifer area by a factor of about 3.5. Overexploitation centres predominantly on a few agriculturally important aquifers in arid or semiarid climates, especially in Asia and North America. The groundwater footprint could serve as a useful framework for analysing the global groundwater depletion data sets emerging from NASA's GRACE satellites. Groundwater is a life-sustaining resource that supplies water to billions of people, plays a central part in irrigated agriculture and influences the health of many ecosystems 1 , 2 . Most assessments of global water resources have focused on surface water 3 , 4 , 5 , 6 , but unsustainable depletion of groundwater has recently been documented on both regional 7 , 8 and global scales 9 , 10 , 11 . It remains unclear how the rate of global groundwater depletion compares to the rate of natural renewal and the supply needed to support ecosystems. Here we define the groundwater footprint (the area required to sustain groundwater use and groundwater-dependent ecosystem services) and show that humans are overexploiting groundwater in many large aquifers that are critical to agriculture, especially in Asia and North America. We estimate that the size of the global groundwater footprint is currently about 3.5 times the actual area of aquifers and that about 1.7 billion people live in areas where groundwater resources and/or groundwater-dependent ecosystems are under threat. That said, 80 per cent of aquifers have a groundwater footprint that is less than their area, meaning that the net global value is driven by a few heavily overexploited aquifers. The groundwater footprint is the first tool suitable for consistently evaluating the use, renewal and ecosystem requirements of groundwater at an aquifer scale. It can be combined with the water footprint and virtual water calculations 12 , 13 , 14 , and be used to assess the potential for increasing agricultural yields with renewable groundwaterref 15 . The method could be modified to evaluate other resources with renewal rates that are slow and spatially heterogeneous, such as fisheries, forestry or soil.