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.

Enhancing flood susceptibility modeling using multi-temporal SAR images, CHIRPS data, and hybrid machine learning algorithms.

Flood susceptibility maps are useful tool for planners and emergency management professionals in the early warning and mitigation stages of floods. In this study, Sentinel-1 dB radar images, which provide Synthetic-Aperture Radar (SAR) data were used to delineate flooded and non-flooded locations. 12 input parameters, including elevation, lithology, drainage density, rainfall, Normalized Difference Vegetation Index (NDVI), curvature, ground slope, Stream Power Index (SPI), Topographic Wetness Index (TWI), soil, Land Use Land Cover (LULC), and distance from the river, were selected for model development. The importance of each input parameter on flood occurrences was assessed via the Mutual Information (MI) technique. Several machine learning models, including Radial Basis Function (RBF), and three hybrid models of Bagging (BA-RBF), Random Committee (RC-RBF), and Random Subspace (RSS-RBF), were developed to delineate flood susceptibility areas at Goorganrood watershed, Iran. The performance of each model was evaluated using several error indicators, including correlation coefficient (r), Nash Sutcliffe Efficiency (NSE), Mean Absolute Error (MSE), Root Mean Square Error (RMSE), and the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC). The results showed that the hybrid techniques enhanced the modeling performance of the standalone model, and generally, all hybrid models are more accurate than the standalone model. Although all developed models have performed well, RC-RBF outperforms all of them (AUC = 0.997), followed by BA-RBF (AUC = 0.996), RSS-RBF (AUC = 0.992), and RBF (AUC = 0.975). Generally, about 12 % of the study area has high and very high susceptibility to future flood occurrences.

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.

Assessment of intrinsic aquifer vulnerability at continental scale through a critical application of the drastic framework: The case of South America.

An assessment of the intrinsic aquifer vulnerability of South America is presented. The outcomes represent the potential sensitivity of natural aquifers to leaching of dissolved compounds from the land surface. The study, developed at continental scale but retaining regionally a high resolution, is based on a critical application of the DRASTIC method. The biggest challenge in performing such a study in South America was the scattered and irregular nature of environmental datasets. Accordingly, the most updated information on soil, land use, geology, hydrogeology, and climate at continental, national, and regional scale were selected from international and local databases. To avoid spatial discrepancy and inconsistency, data were integrated, harmonized, and accurately cross-checked, using local professional knowledge where information was missing. The method was applied in a GIS environment to allow spatial analysis of raw data along with the overlaying and rating of maps. The application of the DRASTIC method allows to classify South America into five vulnerability classes, from very low to very high, and shows an overall medium to low vulnerability at continental scale. The Amazon region, coastal aquifers, colluvial Andean valleys, and alluvial aquifers of main rivers were the areas classified as highly vulnerable. Moreover, countries with the largest areas with high aquifer vulnerability were those characterized by extended regions of rainforest. In addition, a single parameter sensitivity analysis showed depth to water table to be the most significant factor, while a cross-validation using existing vulnerability assessments and observed concentrations of compounds in groundwater confirmed the reliability of the proposed assessment, even at regional scale. Overall, although additional field surveys and detailed works at local level are needed to develop effective water management plans, the present DRASTIC map represents an essential common ground towards a more sustainable land-use and water management in the whole territory of South America.

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.

New hybrid-based approach for improving the accuracy of coastal aquifer vulnerability assessment maps.

Due to excessive exploitation, groundwater resources of coastal regions are exposed to seawater intrusion. Therefore, vulnerability assessments are essential for the quantitative and qualitative management of these resources. The GALDIT model is the most widely used approach for coastal aquifer vulnerability assessment, but suffers from subjectivity of the identification of rates and weights. This study aimes at developing a new hybrid framework for improving the accuracy of coastal aquifer vulnerability assessment using various statistical, metaheuristic, and Multi-Attribute Decision Making (MADM) methods to improve the GALDIT model. The Gharesoo-Gorgan Rood coastal aquifer in northern Iran is used as study site. In order to meet this aim, the Differential Evolution (DE) and Biogeography-Based Optimization (BBO) metaheuristic algorithms were employed to optimize the GALDIT weights. In addition, a novel MADM method, named Step-wise Weight Assessment Ratio Analysis (SWARA), and the bivariate statistical method called statistical index (SI) were used to modify the GALDIT ratings. Finally, correlation coefficients between the maps obtained from each method and Total Dissolved Solid (TDS) as an indicator of seawater intrusion were computed to evaluate the models' prediction power. Correlation coefficients of 0.72, 0.75, 0.76 and 0.78 were obtained for the GALDITSWARA-BBO, GALDITSI-BBO, GALDITSWARA-DE and GALDITSI-DE models, respectively. The results from the GALDITSI-DE model outperformed all other models at improving the accuracy of the vulnerability assessment. Moreover, the statistical-metaheuristic method yielded more accurate results than SWARA-metaheuristic hybrid models. The vulnerability map of the studied region indicates that the northwestern and western areas are very highly vulnerable. According to GALDITSI-DE model, 42%, 17%, 18% and 22% of the aquifer areas respectively have a low, medium, high and very high vulnerability to seawater intrusion. The research findings could be applied by regional authorities to manage and protect groundwater resources.

Developing a SINTACS-based method to map groundwater multi-pollutant vulnerability using evolutionary algorithms.

In this study, the modified SINTACS method, a rating-based groundwater vulnerability approach, was applied to data from the Campanian Plain, southern Italy, to identify groundwater vulnerable areas accurately. To mitigate the subjectivity of SINTACS rating and weighting schemes, a modified SINTACS model was formulated by optimizing parameter ratings using the Wilcoxon rank-sum test, and the weight scores using the evolutionary algorithms including artificial bee colony (ABC) and genetic algorithm (GA) methods. The validity of the models was verified by analyzing the correlation coefficient between the vulnerability index and nitrate (NO3) and sulfate (SO4) concentrations found in the groundwater. The correlation coefficients between the pollutant concentrations and the relevant vulnerability index increased significantly from - 0.35 to 0.43 for NO3 and from - 0.28 to 0.33 for SO4 after modifying the ratings and weights of typical SINTACS. Besides, a multi-pollutant vulnerability map considering both NO3 and SO4 pollutants was produced by amalgamating the best calibrated vulnerability maps based on the obtained correlation values (i.e., the Wilcoxon-ABC-based SINTACS vulnerability map for NO3 and the Wilcoxon-GA-based SINTACS vulnerability map for SO4). The resultant multi-pollutant vulnerability map coincided significantly with a land use map of the study area, where anthropogenic activities represented the main sources of pollution.

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.

Improving prediction of water quality indices using novel hybrid machine-learning algorithms.

River water quality assessment is one of the most important tasks to enhance water resources management plans. A water quality index (WQI) considers several water quality variables simultaneously. Traditionally WQI calculations consume time and are often fraught with errors during derivations of sub-indices. In this study, 4 standalone (random forest (RF), M5P, random tree (RT), and reduced error pruning tree (REPT)) and 12 hybrid data-mining algorithms (combinations of standalones with bagging (BA), CV parameter selection (CVPS) and randomizable filtered classification (RFC)) were used to create Iran WQI (IRWQIsc) predictions. Six years (2012 to 2018) of monthly data from two water quality monitoring stations within the Talar catchment were compiled. Using Pearson correlation coefficients, 10 different input combinations were constructed. The data were divided into two groups (ratio 70:30) for model building (training dataset) and model validation (testing dataset) using a 10-fold cross-validation technique. The models were evaluated using several statistical and visual evaluation metrics. Result show that fecal coliform (FC) and total solids (TS) had the greatest and least effect on the prediction of IRWQIsc. The best input combinations varied among the algorithms; generally variables with very low correlations displayed weaker performance. Hybrid algorithms improved the prediction power of several of the standalone models, but not all. Hybrid BA-RT outperformed the other models (R2 = 0.941, RMSE = 2.71, MAE = 1.87, NSE = 0.941, PBIAS = 0.500). PBIAS indicated that all algorithms, with the exceptions of RT, BA-RT and CVPS-REPT, overestimated WQI values.

Enhancing nitrate and strontium concentration prediction in groundwater by using new data mining algorithm.

Groundwater resources constitute the main source of clean fresh water for domestic use and it is essential for food production in the agricultural sector. Groundwater has a vital role for water supply in the Campanian Plain in Italy and hence a future sustainability of the resource is essential for the region. In the current paper novel data mining algorithms including Gaussian Process (GP) were used in a large groundwater quality database to predict nitrate (contaminant) and strontium (potential future increasing) concentrations in groundwater. The results were compared with M5P, random forest (RF) and random tree (RT) algorithms as a benchmark to test the robustness of the modeling process. The dataset includes 246 groundwater quality samples originating from different wells, municipals and agricultural. It was divided for the modeling process into two subgroups by using the 10-fold cross validation technique including 173 samples for model building (training dataset) and 73 samples for model validation (testing dataset). Different water quality variables including T, pH, EC, HCO3-, F-, Cl-, SO42-, Na+, K+, Mg2+, and Ca2+ have been used as an input to the models. At first stage, different input combinations have been constructed based on correlation coefficient and thus the optimal combination was chosen for the modeling phase. Different quantitative criteria alongside with visual comparison approach have been used for evaluating the modeling capability. Results revealed that to obtain reliable results also variables with low correlation should be considered as an input to the models together with those variables showing high correlation coefficients. According to the model evaluation criteria, GP algorithm outperforms all the other models in predicting both nitrate and strontium concentrations followed by RF, M5P and RT, respectively. Result also revealed that model's structure together with the accuracy and structure of the data can have a relevant impact on the model's results.

A novel hybrid method of specific vulnerability to anthropogenic pollution using multivariate statistical and regression analyses.

Groundwater resources are the main supply of freshwater for human activities. However, in the last fifty years aquifers have become more susceptible to chemical pollution due to human activities. The concept of groundwater vulnerability constitutes a worldwide accepted tool for water protection and planning. However, the existing methods and modified versions do not account for all the hydrogeochemical processes that drive anthropogenic pollution. The hydrogeochemical processes occurring within an aquifer can be determined using multivariate statistical analysis. In this study a specific vulnerability method named SVAP (Specific Vulnerability to Anthropogenic Pollution) is proposed. The index is based on seven quantitative parameters: depth to groundwater, recharge, nitrate losses, hydraulic resistance of the vadose zone, aquifer thickness, hydraulic conductivity of the aquifer, and slope. Weights of anthropogenic factors were determined by factor analysis and used to validate the SVAP methodology. The parameters' classification was selected according to the highest Pearson's correlation coefficient with factor weights and then grouped via a linear combination. The new index was applied in two watersheds: the Florina basin (Greece) and the Garigliano River basin (Italy), both of which possess complex hydrogeochemical regimes. The main hydrogeochemical processes acting in the study areas were identified via factor analysis, which revealed that the anthropogenic pollution in both sites was due mainly to chemical fertilizers and manure. Verification of the SVAP method produced correlation coefficients with nitrate concentrations of 0.75 and 0.62 in Florina and Garigliano, respectively. The proposed SVAP method is more reliable and flexible than standard vulnerability assessment methods and can be easily adapted for complex aquifers.

Water allocation and governance in multi-stakeholder environments: Insight from Axios Delta, Greece.

This article deals with stakeholders' interactions and institutional capacity influencing water resource management where competitive demands co-exist. For the case study area of Axios Delta, Northern Greece, a water deficit in the agricultural sector, an unmet environmental flow and a reduced capacity for urban supply during drought conditions are observed. An egocentric network analysis based on desk-study and a series of semi-structured stakeholder interviews reveals how weak stakeholder ties lead to ineffective multilevel governance and, as a result, low water efficiency practices. There is a lack of understanding of other users' priorities as well as of the risks related to climate change and/or seasonal variability. This is reflected in the flat rate abstraction licence for agricultural purposes which reduces environmental flow to below acceptable standards. There is no transboundary cooperation between Greece and the Republic of North Macedonia which hinders an integrated management approach. A limited exchange of information to support an evidence-based allocation plan is observed. Suitable interventions identified through a DPSIR approach are evaluated in a multi-criteria analysis considering cost effectiveness, delivered benefits as well as ease of implementation. Suitable technical practices include the development of a local and catchment-scale monitoring network for surface water and groundwater, climate-adaptive agriculture and treated-water reclamation. Updated management policies involve the institutional prioritisation of environmental flow through an adaptive allocation plan as well as the strengthening of transboundary cooperation. This research shows how the coordination of aggregated diverging interests in multilevel multi-stakeholder environments appears to be key in supporting positive water budgets in an uncertain climate future.

GALDIT-SUSI a modified method to account for surface water bodies in the assessment of aquifer vulnerability to seawater intrusion.

The salinization of coastal aquifers is one of the major environmental issue worldwide. Overexploitation is the most common reason of salinization, since it generates a piezometric inversion, which in turn leads to groundwater flow from the coast towards inland. This also occurs in water bodies connected to the sea like lagoons, rivers, torrents and wetlands. In this study, a modification of the GALDIT method including "SUperficial Seawater Intrusion (SUSI)" is proposed. Six new parameters were added to the classical ones. The analytical hierarchy process and the sensitivity analysis were performed for weights definition and validation of the proposed GALDIT-SUSI method. Two study areas, with different characteristics were chosen for the application of both methods: the coastal area of Epanomi (Greece) and the Po River lowland (Italy). The application of the standard GALDIT in both sites showed a poor discrimination of the vulnerability to seawater intrusion, confining it only in proximity to the coastline. Conversely, GALDIT-SUSI divided the two sites in five classes of vulnerability ranging from very low to very high, stressing the higher vulnerability of lagoons and wetland for Epanomi and lagoons and rivers for the Po River lowland. GALDIT-SUSI is easy to apply and versatile, since it can be adapted to the specific hydrogeological setting of the area of interest. Moreover, GALDIT-SUSI can be further improved to deal with other salinization mechanisms.

A comparison study of DRASTIC methods with various objective methods for groundwater vulnerability assessment.

Groundwater vulnerability assessment is a measure of potential groundwater contamination for areas of interest. The main objective of this study is to modify original DRASTIC model using four objective methods, Weights-of-Evidence (WOE), Shannon Entropy (SE), Logistic Model Tree (LMT), and Bootstrap Aggregating (BA) to create a map of groundwater vulnerability for the Sari-Behshahr plain, Iran. The study also investigated impact of addition of eight additional factors (distance to fault, fault density, distance to river, river density, land-use, soil order, geological time scale, and altitude) to improve groundwater vulnerability assessment. A total of 109 nitrate concentration data points were used for modeling and validation purposes. The efficacy of the four methods was evaluated quantitatively using the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC). AUC value for original DRASTIC model without any modification of weights and rates was 0.50. Modification of weights and rates resulted in better performance with AUC values of 0.64, 0.65, 0.75, and 0.81 for BA, SE, LMT, and WOE methods, respectively. This indicates that performance of WOE is the best in assessing groundwater vulnerability for DRASTIC model with 7 factors. The results also show more improvement in predictability of the WOE model by introducing 8 additional factors to the DRASTIC as AUC value increased to 0.91. The most effective contributing factor for ground water vulnerability in the study area is the net recharge. The least effective factors are the impact of vadose zone and hydraulic conductivity.

Management and research strategies of karst aquifers in Greece: Literature overview and exemplification based on hydrodynamic modelling and vulnerability assessment of a strategic karst aquifer.

Karst aquifers are valuable water resources in terms of quantity and quality, hence, their protection and rational management is of utmost importance to sustain water supply. An overview of research articles regarding Karst aquifers in Greece was obtained revealing that progressively the initial simple statistical analysis was replaced from advanced tools but rarely coupled. Additionally, a combined approach including the concept of groundwater vulnerability and pollution risk in conjunction with statistical and hydrodynamic analysis was performed in the complex karst aquifer of Damasi-Titanos in Thessaly Central Greece. The karst aquifer discharges via three springs and it is in dynamic interaction with one of the two rivers that cross the system. The water demands of the area are mainly met with groundwater from the karst aquifer rendering its protection fundamental priority for the sustainability of the area. The hydrodynamic analysis of the karst system was performed by pairing statistical techniques and KarstMod. The analysis revealed a high correlation between the springs that highlights the karstification maturity of the aquifer. Additionally, spring discharge is mainly controlled by the percolation of River Titarisios rather than precipitation. Following the hydrodynamic analysis, the PaPRIKa method was applied and validated using sensitivity analysis in order to assess the intrinsic vulnerability. The vulnerability and hazard maps were combined to produce the pollution risk map of the karst aquifer. The majority of the karst aquifer is characterized by high to very high vulnerability as well as pollution risk. The case study and the obtained overview revealed that a holistic approach can provide mutually supported results increasing their reliability. In this base, a four-step road map including hydrogeological observation, statistical analysis, modelling and vulnerability assessment is suggested in order to obtain the sustainable exploration and integrated management of karst aquifers in Greece.

Environmentally available hexavalent chromium in soils and sediments impacted by dispersed fly ash in Sarigkiol basin (Northern Greece).

Hexavalent chromium is one of the most toxic and carcinogenic species known and can be released into the environment from several sources. In Sarigkiol basin (N Greece) the presence of Cr(VI) in soil, sediments and groundwater may originate from both natural (ophiolitic rocks and their weathering products) and anthropogenic (dispersed fly ash produced from lignite power plants) sources. In this study, the distribution of contents and origin of environmentally available Cr(VI) in soils, sediments, regoliths and fly ash of Sarigkiol basin is presented. Detailed geochemical and mineralogical studies were performed on soil samples (up to 1 m) and regoliths, while leaching tests were also applied to fresh and old fly ash samples. Leachable chromium from soil and sediment samples generally increased with depth and the highest concentrations were observed near to the power plant of Agios Dimitrios. The speciation of chromium in leachates revealed that Cr(VI) concentrations accounted for more than 96% of total Cr. Leaching tests of regoliths established that the natural contribution of Cr(VI) is up to 14 µg kg-1. Therefore, the measurement of higher concentrations (up to 80 µg kg-1) of environmentally available Cr(VI) in soils and sediments can be attributed to the impact/presence of dispersed fly ash in the soils and sediments of the same area. This was also supported by the low correlation recorded between environmentally available chromium and Cr-bearing minerals (mainly serpentine and talc). The influenced zone is located in the eastern part of the basin near the local power plant and surrounds an open conveyor belt that transfers fly ash to an open temporary storage pit. This zone overlies an unconfined porous aquifer thus explaining the elevated concentrations of Cr(VI) in groundwater (up to 120 µg L-1) previously reported in this area.

Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution.

Floods are among Earth's most common natural hazards, and they cause major economic losses and seriously affect peoples' lives and health. This paper addresses the development of a flood susceptibility assessment that uses intelligent techniques and GIS. An adaptive neuro-fuzzy inference system (ANFIS) was coupled with a genetic algorithm and differential evolution for flood spatial modelling. The model considers thirteen hydrologic, morphologic and lithologic parameters for the flood susceptibility assessment, and Hengfeng County in China was chosen for the application of the model due to data availability and the 195 total flood events. The flood locations were randomly divided into two subsets, namely, training (70% of the total) and testing (30%). The Step-wise Weight Assessment Ratio Analysis (SWARA) approach was used to assess the relation between the floods and influencing parameters. Subsequently, two data mining techniques were combined with the ANFIS model, including the ANFIS-Genetic Algorithm and the ANFIS-Differential Evolution, to be used for flood spatial modelling and zonation. The flood susceptibility maps were produced, and their robustness was checked using the Receiver Operating Characteristic (ROC) curve. The results showed that the area under the curve (AUC) for all models was >0.80. The highest AUC value was for the ANFIS-DE model (0.852), followed by ANFIS-GA (0.849). According to the RMSE and MSE methods, the ANFIS-DE hybrid model is more suitable for flood susceptibility mapping in the study area. The proposed method is adaptable and can easily be applied in other sites for flood management and prevention.

A fuzzy multicriteria categorization of the GALDIT method to assess seawater intrusion vulnerability of coastal aquifers.

Groundwater constitutes the primary source of fresh water for >1.2 billion people living in coastal zones. However, the threat of seawater intrusion is widespread in coastal aquifers mainly due to overexploitation of groundwater. In the present study, a modified fuzzy multicriteria categorization into non-ordered categories method was developed in order to modify the standard GALDIT method and assess seawater intrusion vulnerability in a coastal aquifer of northern Greece. The method is based on six parameters: groundwater occurrence, aquifer hydraulic conductivity, groundwater level, distance from the shore, impact of the existing status of seawater intrusion, and aquifer thickness. Initially, the original method was applied and revealed a zone of high vulnerability running parallel to the coastline and covering an area of 8.6km2. The modified GALDIT-F method achieved higher discretization of vulnerability zones which is essential to build a rational management plan to prevent seawater intrusion. The GALDIT-F approach also distinguished an area of the aquifer that is influenced by geothermal fluids. In total, twenty-five categories were produced corresponding to different vulnerability degrees according to the initial method (High, Moderate, Low) as well as the area influenced by geothermal fluids. Finally, a road map was developed in order to adapt management strategies to GALDIT-F categories and prevent and mitigate seawater intrusion. The proposed management strategies of the coastal aquifer include managed aquifer recharge (MAR) implementation, reallocation of existing wells, optimization of pumping rates during the hydrological year, and a detailed monitoring plan.