Coupling SWAT and DPSIR models for groundwater management in Mediterranean catchments.

Groundwater is an essential natural resource and has a significant role in human and environmental health as well as in the economy. Management of subsurface storage remains an important option to meet the combined demands of humans and ecosystems. The increasing need to find multi-purpose solutions to address water scarcity is a global challenge. Thus, the interactions leading to surface runoff and groundwater recharge have received particular attention over the last decades. Additionally, new methods are developed to incorporate the spatial-temporal variation of recharge in groundwater modeling. In this study, groundwater recharge was spatiotemporally quantified using the Soil and Water Assessment Tool (SWAT) in the Upper Volturno-Calore hydrological basin in Italy and the results were compared with other two basins in Greece (Anthemountas and Mouriki). SWAT model was applied in actual and future projections (2022-2040) using the Representative Concentration Pathway (RCP) 4.5 emissions scenario to evaluate changes in precipitation and assess the future hydrologic conditions, along with, the Driving Force-Pressure-State-Impact-Response (DPSIR) framework that was applied in all the basins as a low-cost analysis of integrated physical, social, natural, and economic factors. According to the results, no significant variations in runoff are predicted in the Upper Volturno-Calore basin for the period 2020-2040 while the potential evapotranspiration percentage varies from 50.1% to 74.3% and infiltration around 5%. The limited primary data constitutes the main pressure in all sites and exaggerates the uncertainty of future projections.

Modeling groundwater and surface water interaction: An overview of current status and future challenges.

The interaction between surface water and groundwater constitutes a critical process to understand the quantitative and qualitative regime of dependent hydrosystems. A multi-scale approach combining cross-disciplinary techniques can considerably reduce uncertainties and provide an optimal understanding of groundwater and surface water exchanges. The simulation process constitutes the most effective tool for such analysis; however, its implementation requires a variety of data, a detailed analysis of the hydrosystem, and time to finalize a reliable solution. The results of the simulation process contribute to the raising of awareness for water protection and the application of better management strategies. Knowledge of models' parameters has great importance to ensure reliable results in the modeling process. In this study, a literature overview of modeling applications in groundwater - surface water interaction is provided. In this context, a comprehensive and holistic approach to groundwater and surface water simulation codes is here presented; results, case studies, and future challenges are also discussed. The main finding of the analysis highlights uncertainties and gaps in the modeling process due to the lack of high frequency and depth dependent field measurements. In many studies, authors underestimate the importance of the hydrogeological regime, and the discretization of hydraulic parameters is often lumped in a simplified manner. The modeling ethics in terms of data transparency and openness should be widely considered to improve the modeling results. The current study contributes to overcome common weaknesses of model applications, fulfils gaps in the existing literature, and highlights the importance of the modeling process in planning sustainable management of water resources.

A novel hybrid of support vector regression and metaheuristic algorithms for groundwater spring potential mapping.

Limited groundwater resources and their overexploitation have become major challenges for sustainable development worldwide. In this study, an innovative hybrid approach was proposed to generate a groundwater spring potential map (GSPM) from the Sarab plain located in Lorestan Province, Iran, which includes the new best-worst method (BWM), stepwise weight assessment ratio analysis (SWARA), support vector machine learning method (SVR), Harris hawk optimization (HHO), and bat algorithms (BA). The first step involved the inventory of a map prepared to contain 610 spring locations. Randomly, 70% of the spring points were selected as training data, and the remaining 30% were selected for validation. Based on the review of the literature and available data, thirteen factors were generated as independent variables. The BWM and SWARA methods were used to identify correlations between the occurrence of springs and factors. Finally, using SVR-BA and SVR-HHO hybrid models, potential maps of groundwater springs were generated and then evaluated with receiver operating characteristic (ROC) and several statistical evaluators such as sensitivity, specificity, accuracy, and kappa index. Validation of the training data set showed that the success rates for the SWARA-SVR-BA, SWARA-SVR-HHO, BWM-SVR-BA, and BWM-SVR-HHO models were 92.6%, 93.7%, 95.9%, and 96.4%, respectively. The results revealed that with a small difference, BWM-SVR-HHO performed better in training compared to other models. Evaluation of the prediction rate showed that the values of the area under the ROC curve for SWARA-SVR-BA, SWARA-SVR-HHO, BWM-SVR-HHO, and BWM-SVR-BA were 91.7%, 92.4%, 93.3%, and 94.7%, respectively. According to the results, although all models had excellent performance with more than 90% accuracy, BWM-SVR-BA was more accurate in predicting. The hybrid models presented in this study can be used as an accurate and effective methodology to improve the results of spatial modeling of the probability of groundwater occurrence.

The origin of Uranium in groundwater of the eastern Halkidiki region, northern Greece.

Uranium (U) pollution in groundwater has become a serious problem worldwide. Even in low concentrations, U has both radiological and toxicological impacts on human health. In this study an integrated hydrogeological approach was applied to conceptualize an aquifer system, and determine the origin of U detected in the aquifer of the eastern Halkidiki region in northern Greece. Data from measurements of groundwater level and hydrochemical and stable isotope analyses of groundwater samples were applied to perform geochemical modeling and multivariate statistical analysis. The modeling and statistical analysis identified three hydrogeochemical groups within the studied hydro-system, and U(VI) as the dominant U species. The first group is linked to the deeper aquifer which is characterized by water-rock interactions with weathering products of granodiorite. In this group the dominant U species is uranyl phosphate and U concentration is 3.7 µg/L. The upper aquifer corresponds to the second hydrogeochemical group where U concentrations are mainly influenced by high concentrations of nitrogen species (NO3- and NO2-). Factor analysis further discriminated the upper aquifer into a saline coastal zone and an inland zone impacted by agricultural activities. The third hydrogeochemical group presents the highest concentration of U (up to 15 µg/L) in groundwater and corresponds to the internal aquifer system. The U within this system is triggered by the presence of Mn2+, while the long residence time of the groundwater contributes synergistically to the hydrogeochemical process. Manganese triggers U oxidation in parallel with Fe2+ precipitation that acts as a regulator of U concentration. Groundwater depletion of the upper aquifers promotes the up-coning of geothermal fluids from fault zones leading to increased concentrations of U in the mid-depth aquifers.

Predictive modeling of selected trace elements in groundwater using hybrid algorithms of iterative classifier optimizer.

Trace element (TE) pollution in groundwater resources is one of the major concerns in both developing and developed countries as it can directly affect human health. Arsenic (As), Barium (Ba), and Rubidium (Rb) can be considered as TEs naturally present in groundwater due to water-rock interactions in Campania Plain (CP) aquifers, in South Italy. Their concentration could be predicted via some readily available input variables using an algorithm like the iterative classifier optimizer (ICO) for regression, and novel hybrid algorithms with additive regression (AR-ICO), attribute selected classifier (ASC-ICO) and bagging (BA-ICO). In this regard, 244 groundwater samples were collected from water wells within the CP and analyzed with respect to the electrical conductivity, pH, major ions and selected TEs. To develop the models, the available dataset was divided randomly into two subsets for model training (70% of the dataset) and evaluation (30% of the dataset), respectively. Based on the correlation coefficient (r), different input variables combinations were constructed to find the most effective one. Each model's performance was evaluated using common statistical and visual metrics. Results indicated that the prediction of As and Ba concentrations strongly depends on HCO3-, while Na+ is the most effective variable on Rb prediction. Also, the findings showed that the most powerful predictive models were those that used all the available input variables. According to models' performance evaluation metrics, the hybrid ASC-ICO outperformed other hybrid (BA- and AR-ICO) and standalone (ICO) algorithms to predict As and Ba concentrations, while both hybrid ASC- and BA-ICO models had higher accuracy and lower error than other algorithms for Rb prediction.

Origin, implications and management strategies for nitrate pollution in surface and ground waters of Anthemountas basin based on a δ15N-NO3- and δ18O-NO3- isotope approach.

Nitrate pollution of surface and groundwater resources is a major worldwide environmental problem. In this study nitrogen isotopes of water, soil, fertilizer and manure were analyzed to determine the pollution sources of nitrate in the groundwater and surface waters of Anthemountas basin. The SIAR model and multivariate statistical analysis were used to determine and quantify the contribution of different NO3̄ sources in groundwater and surface water. Additionally, a detailed literature overview was carried out to identify the origin of nitrate pollution in surface and ground waters based on ΝΟ3- isotopes. The Piper diagram identified the dominant water types as Mg-Ca-HCO3 and Ca-Mg-HCO3. Nitrate concentrations reached 162.0 mg/L in groundwater and 39.0 mg/L in surface waters. The main source of nitrate in groundwater was mainly nitrified ammonium-based synthetic urea and less nitrate-based synthetic fertilizers. The correlation of SIAR results with other trace elements revealed a negative correlation between hexavalent chromium and a) nitrate-based synthetic fertilizers, and b) nitrification of urea synthetic fertilizers. However, a positive correlation was observed between hexavalent chromium and anthropogenic organic matter. The literature overview provided the basis to design a novel management protocol for nitrate pollution that includes three steps: a) fundamental research, b) management tools, c) monitoring and preservation actions. However, an integrated management protocol for nitrate pollution requires a deeper understanding of the hydro-system and the full participation of local farmers and stakeholders.