Integration of Geological, Geochemical Modelling and Hydrodynamic Condition for Understanding the Geometry and Flow Pattern of the Aquifer System, Southern Nyírség–Hajdúság, Hungary
Yetzabbel G. Flores,
Mohamed Hamdy Eid,
Péter Szűcs
et al.
Abstract:Geological heterogeneity impacts groundwater flow patterns, necessitating a detailed hydrogeological framework for conceptualization process of aquifer systems. This research developed a new conceptual model of detailed geologic geometry by integrating 133 well-logs, 366 hydrodynamic data and 118 water samples. As new results, systematic 3D log correlation detected four distinct hydrostratigraphic units in the Southern Nyírség–Hajdúság Groundwater Body (East Hungary). The primary aquifer was identified as an i… Show more
“…The calculated CAI-1 and CAI-2 show that the majority of groundwater samples exhibit positive values (Fig. 4e), which may highlight the impact of cation exchange between bivalent cations (Ca 2+ and Mg 2+ ) in the host rock and monovalent cations (Na + and K + ) in the groundwater (Al-Mashreki et al, 2023;Eid et al, 2024bEid et al, , 2024aFlores et al, 2023;Wu et al, 2015)…”
Section: Ion Exchange Processesmentioning
confidence: 94%
“…The characteristics of irrigation water are critical in determining the structural and functional attributes of soil, affecting aspects like its ability to allow water flow, its filtration efficiency, and the movement of air within its matrix, all due to the water's specific chemical makeup (Ghodbane et al, 2022;Flores et al, 2023;Eid et al, 2023;(Gad et al, 2023b;Gad and El Osta, 2020;Eid et al, 2022;Gad and El Osta, 2020;Ibrahim et al, 2023). The levels of sodium ions present hold particular importance, as high concentrations can alter soil's natural filtration process by causing calcium and magnesium ions to be replaced via absorption.…”
The crucial assessment of water quality in the Mornag Plain, Tunisia, is fundamental for reservoir management, ensuring suitability for consumption, and preserving environmental integrity. Employing a diverse range of methodologies, such as water quality indices (WQIs), statistical analyses, geographic information systems (GIS), and decision tree (DT) model, provided a nuanced understanding of the compositional attributes of groundwater designated for irrigation. Detected categories of water, for instance Na-Cl, Ca-Mg-SO4, and a combination of Ca-Mg-Cl/SO4, displayed unique chemical signatures. These patterns were shaped by diverse processes including interactions between water and rock, the breakdown of dolomite, the concentration of minerals through evaporation, the swapping of ions, and human impact. Evaluating groundwater's suitability for irrigation purposes by employing measures like Na%, SAR, SSP, and MH, demonstrated that a significant portion of the samples conformed to approved norms. However, the analysis revealing 65.6% of the IWQI and every instance of PS falling within the spectrum of high to severe constraints, as well as ranging from detrimental to unsatisfactory classes, underscores the obstacles in sustaining superior irrigation water standards. The predictive model, DT, demonstrated robust results in forecasting all water quality indices. The DT-HyC-9 model stood out as the top performer in prediction accuracy, demonstrating a robust correlation with prime factors affecting IWQI, as shown by substantial R2 metrics in both the training and evaluation phases. Likewise, the DT-HyC-3 approach showcased remarkable precision in forecasting IWQI, highlighting the critical role of three essential factors. These results provide crucial guidance for policymakers engaged in managing water quality, laying the groundwork for the sustainable management of water resources within the Mornag Plain. The integration of advanced methodologies and predictive models enhances the understanding of groundwater dynamics, facilitating informed decision-making for the region's water sustainability.
“…The calculated CAI-1 and CAI-2 show that the majority of groundwater samples exhibit positive values (Fig. 4e), which may highlight the impact of cation exchange between bivalent cations (Ca 2+ and Mg 2+ ) in the host rock and monovalent cations (Na + and K + ) in the groundwater (Al-Mashreki et al, 2023;Eid et al, 2024bEid et al, , 2024aFlores et al, 2023;Wu et al, 2015)…”
Section: Ion Exchange Processesmentioning
confidence: 94%
“…The characteristics of irrigation water are critical in determining the structural and functional attributes of soil, affecting aspects like its ability to allow water flow, its filtration efficiency, and the movement of air within its matrix, all due to the water's specific chemical makeup (Ghodbane et al, 2022;Flores et al, 2023;Eid et al, 2023;(Gad et al, 2023b;Gad and El Osta, 2020;Eid et al, 2022;Gad and El Osta, 2020;Ibrahim et al, 2023). The levels of sodium ions present hold particular importance, as high concentrations can alter soil's natural filtration process by causing calcium and magnesium ions to be replaced via absorption.…”
The crucial assessment of water quality in the Mornag Plain, Tunisia, is fundamental for reservoir management, ensuring suitability for consumption, and preserving environmental integrity. Employing a diverse range of methodologies, such as water quality indices (WQIs), statistical analyses, geographic information systems (GIS), and decision tree (DT) model, provided a nuanced understanding of the compositional attributes of groundwater designated for irrigation. Detected categories of water, for instance Na-Cl, Ca-Mg-SO4, and a combination of Ca-Mg-Cl/SO4, displayed unique chemical signatures. These patterns were shaped by diverse processes including interactions between water and rock, the breakdown of dolomite, the concentration of minerals through evaporation, the swapping of ions, and human impact. Evaluating groundwater's suitability for irrigation purposes by employing measures like Na%, SAR, SSP, and MH, demonstrated that a significant portion of the samples conformed to approved norms. However, the analysis revealing 65.6% of the IWQI and every instance of PS falling within the spectrum of high to severe constraints, as well as ranging from detrimental to unsatisfactory classes, underscores the obstacles in sustaining superior irrigation water standards. The predictive model, DT, demonstrated robust results in forecasting all water quality indices. The DT-HyC-9 model stood out as the top performer in prediction accuracy, demonstrating a robust correlation with prime factors affecting IWQI, as shown by substantial R2 metrics in both the training and evaluation phases. Likewise, the DT-HyC-3 approach showcased remarkable precision in forecasting IWQI, highlighting the critical role of three essential factors. These results provide crucial guidance for policymakers engaged in managing water quality, laying the groundwork for the sustainable management of water resources within the Mornag Plain. The integration of advanced methodologies and predictive models enhances the understanding of groundwater dynamics, facilitating informed decision-making for the region's water sustainability.
“…The region’s climate can be characterized as predominantly continental, with annual mean temperatures ranging from 10° to 11 °C. The annual precipitation varies from 550 to 600 mm, and potential evapotranspiration ranges between 600 and 700 mm/year 44 . Figure 1 Geographic map created with ArcGIS Desktop v. 10.8 45 , showing the location of the study area within the Eastern part of Hungary.…”
Section: Study Areamentioning
confidence: 99%
“…Recently, Flores et al 44 conducted an extensive regional-level hydrostratigraphical investigation, concentrating on the upper section of the Nagyalföld aquifer. Their findings revealed that the key hydrostratigraphic components in their study encompass the Pre-Quaternary and Quaternary sequences (Fig.…”
This research presents an unsupervised learning approach for interpreting well-log data to characterize the hydrostratigraphical units within the Quaternary aquifer system in Debrecen area, Eastern Hungary. The study applied factor analysis (FA) to extract factor logs from spontaneous potential (SP), natural gamma ray (NGR), and resistivity (RS) logs and correlate it to the petrophysical and hydrogeological parameters of shale volume and hydraulic conductivity. This research indicated a significant exponential relationship between the shale volume and the scaled first factor derived through factor analysis. As a result, a universal FA-based equation for shale volume estimation is derived that shows a close agreement with the deterministic shale volume estimation. Furthermore, the first scaled factor is correlated to the decimal logarithm of hydraulic conductivity estimated with the Csókás method. Csókás method is modified from the Kozeny-Carman equation that continuously estimates the hydraulic conductivity. FA and Csókás method-based estimations showed high similarity with a correlation coefficient of 0.84. The use of factor analysis provided a new strategy for geophysical well-logs interpretation that bridges the gap between traditional and data-driven machine learning techniques. This approach is beneficial in characterizing heterogeneous aquifer systems for successful groundwater resource development.
“…Statistical analysis and imitative approaches, such as Piper diagrams, Gibbs diagrams, Chadha diagrams, and the ionic ratio are powerful and useful tools for evaluating the chemical characteristics of groundwater and the underlying factors [19][20][21][22]. Geochemical models are valuable in computing chemical reactions occurring in groundwater systems; comprehensive processes like solid dissolution and precipitation; ion exchange; and sorption by clay minerals [23][24][25][26][27][28]. Statistical correlation analysis, which establishes connections between various physicochemical factors, holds promise for advancing groundwater quality management.…”
In the Zeroud basin, a diverse array of methodologies were employed to assess, simulate, and predict the quality of groundwater intended for irrigation. These methodologies included the irrigation water quality indices (IWQIs); intricate statistical analysis involving multiple variables, supported with GIS techniques; an artificial neural network (ANN) model; and an XGBoost regression model. Extensive physicochemical examinations were performed on groundwater samples to elucidate their compositional attributes. The results showed that the abundance order of ions was Na+ > Ca2+ > Mg2+ > K+ and SO42− > HCO3− > Cl−. The groundwater facies reflected Ca-Mg-SO4, Na-Cl, and mixed Ca-Mg-Cl/SO4 water types. A cluster analysis (CA) and principal component analysis (PCA), along with ionic ratios, detected three different water characteristics. The mechanisms controlling water chemistry revealed water–rock interaction, dolomite dissolution, evaporation, and ion exchange. The assessment of groundwater quality for agriculture with respect IWQIs, such as the irrigation water quality index (IWQI), sodium adsorption ratio (SAR), sodium percentage (Na%), soluble sodium percentage (SSP), potential salinity (PS), and residual sodium carbonate (RSC), revealed that the domination of the water samples was valuable for agriculture. However, the IWQI and PS fell between high-to-severe restrictions and injurious-to-unsatisfactory. The ANN and XGBoost regression models showed robust results for predicting IWQIs. For example, ANN-HyC-9 emerged as the most precise forecasting framework according to its outcomes, as it showcased the most robust link between prime attributes and IWQI. The nine attributes of this model hold immense significance in IWQI prediction. The R2 values for its training and testing data stood at 0.999 (RMSE = 0.375) and 0.823 (RMSE = 3.168), respectively. These findings indicate that XGB-HyC-3 emerged as the most accurate forecasting model, displaying a stronger connection between IWQI and its exceptional characteristics. When predicting IWQI, approximately three of the model’s attributes played a pivotal role. Notably, the model yielded R2 values of 0.999 (RMSE = 0.001) and 0.913 (RMSE = 2.217) for the training and testing datasets, respectively. Overall, these results offer significant details for decision-makers in managing water quality and can support the long-term use of water resources.
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