Groundwater Flow Model Research Articles

Groundwater dynamics simulation in Hodna (Algeria): aquifer modeling approach

Groundwater modeling using MODFLOW and GIS tools is crucial for understanding the complexities of groundwater dynamics in Algeria's semi-arid and arid climates. This study focuses on the quantitative assessment of the phreatic aquifer within the Hodna aquifer system over the period of 1973-2005. The study employs MODFLOW and GIS tools to model groundwater flow. Calibration and validation processes, using a trial and error technique, refine the models to understand water table behaviors and drawdown within the Hodna basin. Calibration efforts yield insights into groundwater dynamics, particularly significant drawdowns observed in cities like M’sila and Boussaada. Parameters such as hydraulic heads, piezometric levels, drainage, and hydraulic conductivity values are extracted from MODFLOW simulations. Analysis reveals a decline in groundwater volume and consistent drops in borehole water levels over the past 32 years, highlighting the challenges of comprehending recharge resources and the impact of groundwater depletion in the region. The study underscores the importance of calibrated groundwater models in managing and mitigating groundwater depletion in Algeria's Hodna basin. It elucidates predominant groundwater flow patterns towards Chott and emphasizes the need for comprehensive understanding and management strategies to sustainably manage groundwater resources in semi-arid and arid regions.

Innovative management strategies for groundwater logging in Aswan city and maximization of its benefits using modeling techniques

Groundwater levels vary from region to another and sometimes in different zones in the same country due to different boundary conditions and extraction rates. Therefore, understanding intricate aquifer systems and predicting how they will react to hydrological changes require the use of groundwater models. In Egypt, the groundwater levels in the Nile Delta aquifer decrease causing problems to the delta ecosystem while it is rising in Aswan area due to the presence of Nasser Lake causing several damages to the city’s buildings and infrastructures. In order to maximize its benefits and lessen the harm brought on by inadequate groundwater management in the city of Aswan, the height of the groundwater level in that city was examined, appraised, and groundwater management scenarios were established in this study. To achieve the objectives of the study, a simulation of Aswan aquifer’s groundwater system is built based on a quasi-three-dimensional transient groundwater flow model using MODFLOW. The model was calibrated and verified. Four management scenarios are tested. The fifth scenario, in this scenario, the four scenarios combined together at the same time and with the same conditions and ratios were proposed to be implemented. The results of the proposal to implement the four scenarios together showed that the rates of decline in groundwater levels in the last stage will be 12.44%. The study results reveal that a better understanding of the simulated long-term average spatial distribution of water balance components is useful for managing and planning the available water resources in the Aswan aquifer.

Validating a Calibrated Model of a Groundwater Pump-And-Treat System Using Robust Multiple Regression

Validation of groundwater models is relatively challenging due to the need to reserve scarce water level data for calibration targets. In addition, traditional statistical validation metrics are unintuitive for non-technical audiences and do not directly identify model behaviors that require further refinement. We developed a novel model validation method that analyzes rate change events at pump-and-treat wells and statistically compares the water level responses at nearby monitoring wells between the data and model. The method takes advantage of events that occur alongside ambient pumping, unlike parameter estimation techniques that require independent drawdown or recovery events. The ability of the model to match well connections that are evident (or not evident) in the observations is characterized statistically, leading to four decision scenarios: model matches the observed connection (1) or lack thereof (2), model exhibits a connection that is not observed (3), or model over- or understates the observed connection (4). The method is applied to an FEHM-based groundwater flow and transport model that is shown to match 84.5% of the well connections analyzed. The method provides novel perspectives on the influence of calibration targets on the flow field and suggests that although the overall effect of drawdown targets was to improve the model, the choice of target well pairs creates flow pathways that may be inconsequential during normal operational conditions. The model adequately matches the flow over short spatial scales (<800 m) and over-represents the flow over greater distances (300–1200 m), suggesting the need for “null” drawdown targets in subsequent rounds of calibration.

Groundwater Model for Karst and Pelitic Aquifer Systems from a Semi-Arid Region Under Climate Change Scenarios: A Case Study in the Vieira River Watershed, Brazil

Water scarcity is a global issue, especially in semi-arid and arid regions where precipitation is irregularly distributed over time and space. Predicting groundwater flow in heterogeneous karst terrains, which are essential water sources, presents a significant challenge. This article integrates geology, hydrology, and water monitoring to develop a pioneering conceptual and numerical model of groundwater flow in the Montes Claros Region (Vieira River Watershed, Brazil). This model was evaluated under various climate change scenarios, considering changes in rainfall, groundwater consumption, and population growth over the current century. The results indicate that a decline in water table levels is inevitable, primarily driven by population growth and high pumping rates rather than rainfall fluctuations. This underscores the urgent need for improved monitoring, model upgrading, and more importantly, targeted water resource management for Montes Claros.

Efficient Model Calibration Using Submodels

AbstractGroundwater models tend to become increasingly detailed to accommodate increasing data availability and higher accuracy demands from stakeholders. As runtimes increase almost quadratically with the number of model cells, this makes the models ever more computationally demanding. This high computational demand introduces challenges for the history‐matching (calibration) process as this is an algorithmic process that needs hundreds or thousands of model‐runs to obtain the model sensitivities needed to estimate parameters. Model runs may take hours or days to complete which in fact, is often a reason to discard the history‐matching all together. As a solution, we present a practical approach to use sub‐modeling in combination with parallelization for automatic history‐matching. Therefore a large model is subdivided into smaller models to carry out the sensitivity simulations. With a realistic case the method is elaborated, after which the method is demonstrated in the history‐matching of the transient Dutch National Groundwater Flow model. In this manner the model, which consisted of over 12 million model cells, could be optimized using 416 sub‐models and altogether 2,188 parameters in 1 week. This would take years to complete in a conventional way.

Modelling hazards impacting the flow regime in the Hranice Karst due to the proposed Skalička Dam

Abstract. This study examines the hydrogeological hazard associated with the construction of the proposed Skalička Dam in the vicinity of the Hranice Karst. Prompted by the catastrophic regional floods in 1997 and 2010, the design of the dam aims to mitigate floods along the Bečva River downstream of the reservoir. However, concerns have been raised regarding the potential disturbance of the natural groundwater regime in the Hranice Karst and the source of mineral waters for the Teplice spa. This is due in particular to the dam's location in an area with limestone outcrops potentially susceptible to surface-water infiltration. Previous studies have also highlighted the strong correlation between the water level in the Bečva River and the water level in karst formations such as the Hranice Abyss, Zbrašov Aragonite Caves, and other caves in the locality. To address these concerns, a nonlinear reservoir-pipe groundwater flow model was employed to simulate the behaviour of the Hranice Karst aquifer and specifically the effects of the dam reservoir's impoundment. The study concluded that the lateral variant of the dam would have a practically negligible impact on the karst water system, with the rise in water level being only a few centimetres. The through-flow variant was found to have a more significant potential impact on water levels and the outflow of mineral water to the spa, with a piezometric rise of about 1 m and an increase in the karst water discharge to the Bečva River of more than 50 %. Based on these results, recommendations for further investigations concerning the design of the dam and its eventual construction have been formulated to reduce geological uncertainties and to minimize the potential impact of the hydraulic scheme on the hydrogeology of the karstic system.

Best Management Practices to Mitigate Contamination of Karstic Aquifers from Emergency Fire-Control Runoff

We propose best management practices to increase karstic aquifer drinking water supplies’ resilience to potential hazardous material impacts where first responders and public safety officials suspect emergency firefighting runoff has entered the subsurface in the aquifer recharge zone. Karstic aquifers are unique because of their direct openings to the land’s surface, which allows contaminants in runoff or other surface waters to rapidly enter the subsurface and impact aquifer water quality. In the United States, 20% of the land surface is karst, and about a third of the groundwater used for drinking comes from karstic aquifers. Proposed best management practices emphasize on-site, real-time evaluation of the transport and fate of HAZMAT that may enter the subsurface and focus on water quality sampling, runoff and groundwater flow modeling, nontoxic dye tracing, and related studies for use in planning before and during an event and after emergency response has ended. We recommend best management practices for evidence-based screening of high-risk sites to facilitate placement of hazardous material sampling devices and emergency responder deployment. The best management practices, tools, curricula, and training described combine with earlier work by the authors to provide a comprehensive menu of actions to (1) help first responders prevent or limit flushing of hazardous material into a karstic aquifer; (2) help emergency management officials understand the consequences of contamination and issue data-driven geographically focused health and safety risk communications; and (3) help provide health and safety officials with relevant science- and data-based information that can help mitigate human and environmental health risks.

A Systematic Review of the Current State of Numerical Groundwater Modeling in American Countries: Challenges and Future Research

In arid and semi-arid regions, groundwater is often the only available water source. However, overexploitation and pollution have led to a decrease in groundwater quantity and quality. Therefore, the proper management of groundwater resources is essential to promote sustainable development. Numerical simulation models (NSMs) have emerged as a valuable tool to address these challenges due to their ability to accurately and efficiently model groundwater systems. This study provides a comprehensive systematic review to evaluate the current knowledge on using numerical groundwater flow models for planning and water resource management in countries in the American region. A total of 166 research articles were published between the years 2000 and 2024. We analyzed and summarized details such as the study regions, numerical simulation methods and applied software, performance metrics, modeling units, modeling limitations, and prediction scenarios. In addition, we discuss alternatives to address the constraints and difficulties and suggest recommendations for future research. The continued research, improvement, and development of numerical groundwater models are essential to ensure the sustainability of groundwater resources.

Enhancing groundwater management with GRACE-based groundwater estimates from GLDAS-2.2: a case study of the Almonte-Marismas aquifer, Spain

AbstractThe Almonte-Marismas aquifer, southwestern Spain, is a critical ecohydrogeological system that features extensive groundwater monitoring. This study investigates the utility of gravity recovery and climate experiment (GRACE) satellite data, specifically obtained from the global land data assimilation system (GLDAS) version 2.2, for assessing groundwater storage variations in the Almonte-Marismas aquifer. The presented research emphasizes the practical application of readily available GLDAS products that do not require data preprocessing. The study validates the GLDAS-2.2-based ready-to-use groundwater storage (GWS) time series by correlating it with precipitation and piezometric information, highlighting its effectiveness in medium-scale aquifers. The results reveal a strong agreement between GLDAS-2.2-derived GWS anomalies and in-situ measurements, confirming GLDAS-2.2’s potential for assessing aquifer depletion. The study discusses the consistency of seasonal variations in groundwater levels and GLDAS-2.2 data, emphasizing their close alignment with precipitation and pumping activities. Importantly, the study introduces GLDAS-2.2-derived volumetric groundwater storage (VGWS) as a valuable calibration parameter for numerical groundwater flow models, enhancing their accuracy over time. Moreover, the analysis reveals disparities in annual recharge values between GLDAS-2.2-derived data and the soil-water mass balance. These variations suggest the importance of additional inputs to precipitation, possibly related to subsurface or lateral connections. Overall, this study contributes to the ongoing discourse on the practical applications of GLDAS-2.2-derived GWS data in groundwater management, offering insights into its effectiveness in diverse hydrogeological settings, particularly in areas that lack monitoring infrastructure.

Bayesian inference of coupled groundwater flow and radiogenic helium-4 production and transport at the catchment scale

Hydrogeological numerical models are essential for assessing radioactive waste disposal by understanding groundwater flow systems. These models typically rely on hydraulic head data, with other state variables often underutilized in model inversions. In Flanders' Neogene aquifer, where safety studies for Boom Clay are ongoing, existing models face uncertainties due to dependence on hydraulic heads alone. This study aims to: i) develop a conceptual model of radiogenic Helium-4 (4Herad) production and transport to reproduce observed concentrations; ii) evaluate the usefulness of 4Herad observations as an additional state variable for inverse conditioning; and iii) assess how including 4Herad affects model calibration and uncertainty reduction. We address these objectives by incorporating radiogenic Helium-4 (4Herad) as an unconventional state variable in model inversion to tackle parameter non-uniqueness. Utilizing a Bayesian approach, we employ two 3D models: a groundwater flow (GWF) model conditioned by hydraulic heads and a coupled GWF model with 4Herad production and transport, conditioned by both hydraulic heads and 4Herad concentrations. We hypothesize that integrating 4Herad will improve model accuracy and reduce uncertainties compared to using hydraulic head data alone. Findings indicate that major 4Herad sources include crustal fluxes (~57 %) and in situ Boom Clay production (~25 %). The coupled inference narrows the posterior distributions of parameters, especially for hydraulic conductivity (e.g., Mol, Diest, Berchem formations, and the Rauw Fault) and recharge, showing that 4Herad observations effectively reduce uncertainty beyond hydraulic head data. Including 4Herad improves model reliability and prediction accuracy, highlighting its importance for refining groundwater flow parameters and derived fluxes. To the best of our knowledge, this study marks the first-ever attempt at harnessing 4Herad quantitative insights into flow and transport parameters following a Bayesian approach at a catchment scale, setting a precedent for future research and emphasizing the innovative nature of this work. This study showcases a state-of-the-art approach in hydrogeological modelling, demonstrating the effectiveness of integrating 4Herad with hydraulic head observations to produce more reliable model predictions. By reducing uncertainties in crucial groundwater fluxes, this approach offers significant potential for improving geological disposal safety studies. This pioneering work advocates for the continued use of 4Herad in hydrogeological modelling, emphasizing its innovative contribution to future research and safety studies. Plain language summaryUnderstanding how groundwater flows is crucial for safely storing radioactive waste. Traditionally, models used to study groundwater rely mostly on measurements of water pressure, while other valuable data often go unused. In the Neogene aquifer in Flanders, where researchers are studying the Boom Clay formation for potential waste storage, relying only on water pressure has created uncertainties in the models. This study explores a new approach by using a different type of measurement: radiogenic Helium-4 (4Herad). The goals of the study are threefold: first, to build a model that accurately represents how 4Herad is produced and moves through the groundwater; second, to test whether including 4Herad as an additional measurement improves the model compared to using only water pressure data; and third, to see how using 4Herad affects the accuracy and reliability of the model. The study uses two types of models: one that relies only on water pressure data and another that includes both water pressure and 4Herad measurements. The idea is that 4Herad could provide additional insights and help reduce uncertainties in the model. The findings show that 4Herad comes mainly from two sources: the surrounding crust and the Boom Clay itself. Including 4Herad in the model helps narrow down uncertainties, particularly regarding how water moves through different layers and how much water is being replenished. This research is the first to use 4Herad in this way, using a formal statistical method to improve groundwater models. The results suggest that 4Herad is a valuable addition for making more accurate predictions and improving safety assessments for radioactive waste storage.

Extending GLUE With Multilevel Methods to Accelerate Statistical Inversion of Hydrological Models

AbstractInverse problems aim at determining model parameters that produce observed data to subsequently understand, predict or manage hydrological or other environmental systems. While statistical inversion is especially popular, its sampling‐based nature often inhibits its application to computationally costly models, which has compromised the use of the Generalized Likelihood Uncertainty Estimation (GLUE) methodology, for example, for spatially distributed (partial) differential equation based models. In this study we introduce multilevel GLUE (MLGLUE), which alleviates the computational burden of statistical inversion by utilizing a hierarchy of model resolutions. Inspired by multilevel Monte Carlo, most parameter samples are evaluated on lower levels with computationally cheap low‐resolution models and only samples associated with a likelihood above a certain threshold are subsequently passed to higher levels with costly high‐resolution models for evaluation. Inferences are made at the level of the highest‐resolution model but substantial computational savings are achieved by discarding samples with low likelihood already on levels with low resolution and low computational cost. Two example inverse problems, using a rainfall‐runoff model and groundwater flow model, demonstrate the substantially increased computational efficiency of MLGLUE compared to GLUE as well as the similarity of inversion results. Findings are furthermore compared to inversion results from Markov‐chain Monte Carlo (MCMC) and multilevel delayed acceptance MCMC, a corresponding multilevel variant, to compare the effects of the multilevel extension. All examples demonstrate the wide‐range suitability of the approach and include guidelines for practical applications.

Customized Statistically Downscaled CMIP5 and CMIP6 Projections: Application in the Edwards Aquifer Region in South‐Central Texas

AbstractClimate projections are being used for decision‐making related to climate mitigation and adaptation and as inputs for impacts modeling related to climate change. The plethora of available projections presents end users with the challenge of how to select climate projections, known as the “practitioner's dilemma.” In addition, if an end‐user determines that existing projections cannot be used, then they face the additional challenge of producing climate projections for their region that are useful for their needs. We present a methodology with novel features to address the “practitioner's dilemma” for generating downscaled climate projections for specific applications. We use the Edwards Aquifer region (EAR) in south‐central Texas to demonstrate a process to select a subset of global climate models from both the CMIP5 and CMIP6 ensembles, followed by downscaling and verification of the accuracy of downscaled data against historical data. The results show that average precipitation changes range from a decrease of 10.4 mm to an increase of 25.6 mm, average temperature increases from 2.0°C to 4.3°C, and the number of days exceeding 37.8°C (100°F) increase by 35–70 days annually by the end of century. The findings enhance our understanding of the potential impacts of climate change on the EAR, essential for developing effective regional management strategies. Additionally, the results provide valuable scenario‐based projected data to be used for groundwater and spring flow modeling and present a clearly documented example addressing the “practitioner's dilemma” in the EAR.

Quantifying the Role of Calibration Strategies on Surface‐Subsurface Hydrologic Model Performance

ABSTRACTDistributed, coupled surface‐groundwater hydrologic models are high‐dimensional, given the necessity to reflect the spatially diverse nature of complex hydrologic processes. Furthermore, inverse/inference problems involving these high‐dimensional models are naturally ill‐posed, given the limited information content of state observations that are typically assimilated. Many inversion/inference algorithms do not cope well with high dimensionality, leaving the practitioner to make subjective choices related to uncertain model inputs. The objective of this study is to evaluate the impact of these subjective calibration choices within a formal sensitivity analysis, uncertainty analysis, and parameters estimation (SA‐UA‐PE) framework on model testing for a surface‐subsurface hydrologic model. In doing so, we address the concepts of ‘over‐parameterisation’ and ‘under‐parameterisation’. We completed a series of numerical experiments, testing several otherwise subjective aspects of the calibration process: (1) the number (5, 10, 15, 20) and type (soil, aquifer, land surface, channel) of calibration parameters selected); (2) the type of state observations assimilated (streamflow, groundwater head); and (3) the length of testing period (1 to 14 years), using monthly streamflow and groundwater head as testing data. The experiments were completed for models of the Winnebago River watershed (Minnesota, Iowa), (significant tile drainage) and the Nanticoke River watershed (Delaware, Maryland (significant groundwater‐channel interactions). The selected hydrologic model is SWAT+, using the gwflow module for physically based groundwater storage and flow modelling, and simulations are run for the 2000–2015 period. Through this process, we found that increasing the number of parameters from 5 to 15 improves the representation of streamflow, principally through an improvement of groundwater storage representation and baseflow generation, but minimal improvement when increasing to 20 parameters. Therefore, the SA‐UA‐PE process can be optimised based on an ideal number of parameters that yield adequate results while maintaining a lower computational burden. The method presented here can be used for any watershed, using integrated surface‐subsurface hydrologic models.

Are regional groundwater models suitable for simulating wetlands, rivers and intermittence? The example of the French AquiFR platform

Predicting and managing water resources at regional scale under different climate and socio-economic scenarios is crucial to support drinking water supply and other sectors. At the same time, protecting rivers and wetlands from pollutions and droughts is essential and must include groundwater given its contribution to surface water. Yet, assessing temporal and spatial variability of groundwater contributions to surface water is constrained due to limited observations. This study aims to quantify the spatio-temporal distribution of groundwater discharge (hereafter called “GW discharge”) zones, i.e. the groundwater flow to rivers and wetlands, estimated by calibrated regional groundwater flow models (French AquiFR platform). We compare simulation results with two types of surface observations: (i) the spatial distribution of surface water (BD TOPO, RAMSAR and Natura 2000 database) and (ii) an innovative datasets, the river intermittence observed in headwaters (ONDE network). Results show that simulated GW discharge zones are consistent with the observed location of rivers and wetlands. Time variations in GW discharge are well correlated with the intermittence observed at 396 of the 515 selected stations. Of these, groundwater model continues to feed surface water upstream of the station for ∼75 % of observed river drying up events, which may be consistent with a small alluvial flow. The groundwater withdrawals are shown to have a strong impact on the GW discharge and thus on the river intermittence.

Robust estimation of hydrogeological parameters from wireline logs usingsemi-supervised deep neural networks assisted with global optimization-based regression methods

Understanding the distribution of hydrogeological properties of the aquifers is crucial for sustainable groundwater resource development. This research explores the application of deep autoencoder neural networks (AE-NN), assisted with global optimization methods for estimating hydrogeological parameters in the Quaternary aquifer system in the Debrecen area, Hungary. Traditional methods for estimating aquifer parameters typically depend on field experiments and laboratory analyses, which are both costly and time-consuming, and often fail to account for the heterogeneity of groundwater formations. In this study, deep AE-NN models are trained to extract latent space (LS) representations that capture key features from the available well logs, including spontaneous potential (SP), natural gamma ray (NGR), shallow resistivity (RS), and deep resistivity (RD). The LS log is then correlated with shale volume and hydraulic conductivity, as determined by the Larionov and Csókás methods, respectively. Regression analysis revealed a Gaussian relationship between the LS log and shale volume and a negative nonlinear relationship with hydraulic conductivity. Global optimization methods, including simulated annealing (SA) and particle swarm optimization (PSO), were used to refine the regression parameters, enhancing the predictive capabilities of the models. The results demonstrated that AE-NN assisted with global optimization methods can be effectively used to estimate shale volume and hydraulic conductivity, proposing a novel and independent approach for estimating hydrogeological parameters critical to groundwater flow and contaminant transport modeling.

Comment on “Modeling groundwater flow with random hydraulic conductivity using radial basis function partition of unity method” by Shile et al. (2024)

The goal of this article is to explain some striking discrepancies between the Monte Carlo inferences for flow in heterogeneous aquifers presented in (Shile et al., 2024) and reliable and verifiable results previously published. Comparisons with statistical inferences done within the same numerical setup and using the same benchmark codes demonstrate that quantifiable aspects, such as a too small departure from the linear theory and the decrease of the standard deviations with the increase of the aquifer heterogeneity, cannot be produced with the tools used by the authors.

Where has hydrogeologic science been, and where is it going? Research trends in hydrogeology publishing over the past 60 years

AbstractQuantifying historical research trends in the field of hydrogeology is not only generally informative for hydrogeologists but is essential for fostering interdisciplinary collaboration and assessing the relationship between academic study and societal interests in hydrogeologic issues. To address this, a topic model was applied to over 37,000 academic abstracts published in over 20 journals between 1963–2022 in the field of hydrogeology to study the evolution of topic trends through time. Model results were fed into the popular large-language model ChatGPT to assign topic names, representing an unsupervised method. The results indicate that, historically, popular topics related to methodological development and analytical and numerical models analytical and numerical methods in groundwater flow modeling and well hydraulics have given way to topics related to more increasingly complex models (groundwater monitoring and uncertainty estimation and groundwater modeling calibration and simulation) as data and computational capability becomes increasingly available. An insight into the period of boom-and-bust in contaminant hydrogeology is reflected by a shift in focus from topics related to assessment and characterization of contaminant sources toward topics related to degradation and remediation methods. Topics of emerging prevalence (sustainable groundwater resource management, catchment hydrology and runoff processes) in the current period reflect an increasing focus on treating the surface-water/groundwater system as a single system. In addition, results suggest that topic distribution within the field of hydrogeology has become more varied as time has progressed.

Conceptual Modflow Model Application with Boundary Condition Analysis

In this study, groundwater flow modeling of the aquifer in the Porsuk Basin, located in the Eskisehir province of Turkey, was conducted using the MODFLOW modeling method and the Groundwater Modeling System (GMS) software interface. The modeling was performed using the finite difference method under the assumption of steady flow, incorporating well data, aquifer boundaries, topographic elevations, riverbed data, and hydraulic parameters from the 1971 water year. This study provided insights into changes in groundwater over time and yielded a comprehensive water budget. By creating a three-dimensional solid model of the Porsuk Basin aquifer, general information about the region's hydrogeology was obtained. The results are expected to play a significant role in identifying potential drilling areas for groundwater extraction. Since the modeling utilizes objects from geographic information systems, it will enhance visualization of the regional structure for researchers, allowing them to observe the topographic features of the area in three dimensions. Additionally, it is anticipated that the groundwater conceptual model will inform drilling studies and serve as a foundation for research on groundwater transport and pollution.

Evidence of Groundwater Seepage and Mixing at the Vicinity of a Knickpoint in a Mountain Stream

AbstractStreamflow generation and biochemical hotspots are significantly influenced by groundwater contributions distributed along the drainage network. However, identifying the geomorphic landscape features that drive groundwater‐surface water interactions remains challenging. In this study, we investigate the role of knickpoints in controlling these interactions in a mountainous stream in Switzerland. We employ a combination of synoptic sampling of environmental tracers, endmember mixing calculations, and groundwater flow simulations. Our findings reveal substantial groundwater seepage concentrated near the knickpoint of the main river stem. Using parsimonious groundwater flow modeling, we validate the hypothesis that the topographical shape of the knickpoint enhances local groundwater discharge rates. We quantify that approximately 20% of the total catchment streamflow originates from around the knickpoint. These results indicate that knickpoints are significant hotspots for groundwater seepage and physicochemical mixing, providing a clear method for identifying major localized sources of streamflow generation.

Prediction of groundwater level using artificial neural network as an alternative approach: a comparison assessment with numerical groundwater flow model

ABSTRACT Over-exploitation of groundwater in arid and semi-arid regions requires an understanding of dynamics for effective management strategies. This study uses an artificial neural network (ANN) to predict groundwater levels using initial water level, pumping, and hydrological and meteorological input variables. A sensitivity analysis assessed predictors’ impact on accurate groundwater level prediction. The accuracy of predictions by the ANN model was examined through different performance evaluation criteria and the result achieved is satisfactory as compared to reasonable statistical indicator values. A comparison of results simulated by the ANN model and numerical groundwater flow model (MODFLOW) was carried out and it was found that the prediction of the ANN model was consistently superior to that of the numerical model. Therefore, data-driven models such as ANN can provide better predictions of groundwater level that will aid in groundwater resource management.