A comprehensive review of groundwater vulnerability assessment using index-based, modelling, and coupling methods

Mapping key areas to protect high-value and high-vulnerability groundwater from pollution load: Method for management

Severe groundwater pollution has necessitated prioritizing the prevention and control of groundwater pollution (PCGP). The fundamental strategy of PCGP involves identifying priority areas. Vulnerability assessment, such as DRASTIC, and its extension, pollution risk assessment, have been developed to guide PCGP. However, managers find it struggling to implement these results in PCGP due to a lack of consideration for practical management demands. This study establishes a management-oriented method to map key areas for groundwater protection and PCGP, considering water sources, pollution source load, vulnerability, and function value, to facilitate management implementation. The key area includes the protection area aimed at protecting water sources and the control area focused on preventing and controlling pollution load in high-value and high-vulnerability groundwater. The effectiveness and practicality of this method are demonstrated through a case study in a large district reliant on groundwater, enabling the key area and corresponding suggestions to directly guide local management. This method offers a practical tool for PCGP worldwide and is expected to guide the sustainable development plan.

Pollution risk evaluation of groundwater wells based on stochastic and deterministic simulation of aquifer lithology

Groundwater pollution risk evaluation is an important basis for developing groundwater protection measures and management strategies, and its accuracy directly affects the effectiveness of protection measures. The heterogeneity of the aquifer significantly affects the transport process of pollutants, increasing the uncertainty of pollutant risk assessment. However, in the actual site, borehole data that reveal aquifer heterogeneity are costly, and only a limited number of borehole data are available, which cannot accurately describe the heterogeneity of the aquifer, thus limiting the accuracy of groundwater pollution risk assessment. In order to overcome the above problems, this paper proposes a groundwater pollution risk assessment framework based on the stochastic and deterministic simulation of aquifer lithology. Based on the statistical characteristics of the change of lithology type in the actual borehole, the framework uses Markov chain to generate some sets of random lithology field and transforms them into heterogeneity parameter field, so as to realize the stochastic assessment of the pollution risk of groundwater resource wells. Furthermore, combined with the pumping test data, the parameter field that is most suitable for the actual situation is selected to evaluate the pollution risk deterministically. Finally, the stochastic and deterministic results are combined to comprehensively evaluate the pollution risk of groundwater resource wells. Through a case study in a river valley plain, the feasibility of the above framework is verified, and good application effects are achieved. This study provides a feasible method for accurately assessing groundwater pollution risk, which is helpful to reduce the impact of uncertain factors on pollution risk assessment, and thus provides a more reliable basis for groundwater management and decision-making.

Integrated assessment of groundwater vulnerability in arid areas combining classical vulnerability index and AHP model

Groundwater is the main source of water for agriculture, industry, and families in arid areas. At present, there is an urgent need to protect groundwater due to human activities. In this study, the Qingshui River Basin was selected as the study area. Based on the DRASTIC model, the DRASTIC-Land use type (DRASTICL) model and the analytic hierarchy process-DRASTICL (AHP-DRASTICL) model were constructed by optimizing the indicators and weights. And the three models were applied to calculate the groundwater vulnerability index (GVI), and the groundwater vulnerability map (GVM) was drawn. The validation results of Spearman correlation coefficient show that the DRASTICL model and the AHP-DRASTICL model have higher correlation, which indicates that the optimized model is more accurate. Among them, the AHP-DRASTICL model has the highest correlation coefficient (ρ = 0.92), which is more in line with the actual situation. The results of this study can provide scientific guidance for the protection and utilization of groundwater in the Qingshui River Basin. And it is of guiding significance for the study of groundwater vulnerability, especially for groundwater management in arid and semi-arid areas.

A machine learning framework for spatio-temporal vulnerability mapping of groundwaters to nitrate in a data scarce region in Lenjanat Plain, Iran

The temporal aspect of groundwater vulnerability to contaminants such as nitrate is often overlooked, assuming vulnerability has a static nature. This study bridges this gap by employing machine learning with Detecting Breakpoints and Estimating Segments in Trend (DBEST) algorithm to reveal the underlying relationship between nitrate, water table, vegetation cover, and precipitation time series, that are related to agricultural activities and groundwater demand in a semi-arid region. The contamination probability of Lenjanat Plain has been mapped by comparing random forest (RF), support vector machine (SVM), and K-nearest-neighbors (KNN) models, fed with 32 input variables (dem-derived factors, physiography, distance and density maps, time series data). Also, imbalanced learning and feature selection techniques were investigated as supplementary methods, adding up to four scenarios. Results showed that the RF model, integrated with forward sequential feature selection (SFS) and SMOTE-Tomek resampling method, outperformed the other models (F1-score: 0.94, MCC: 0.83). The SFS techniques outperformed other feature selection methods in enhancing the accuracy of the models with the cost of computational expenses, and the cost-sensitive function proved more efficient in tackling imbalanced data issues than the other investigated methods. The DBEST method identified significant breakpoints within each time series dataset, revealing a clear association between agricultural practices along the Zayandehrood River and substantial nitrate contamination within the Lenjanat region. Additionally, the groundwater vulnerability maps created using the candid RF model and an ensemble of the best RF, SVM, and KNN models predicted mid to high levels of vulnerability in the central parts and the downhills in the southwest.

A framework for risk assessment of groundwater contamination integrating hydrochemical, hydrogeological, and electrical resistivity tomography method

Groundwater contamination have been widely concerned. To reliably conduct risk assessment, it is essential to accurately delineate the contaminant distribution and hydrogeological condition. Electrical resistivity tomography (ERT) has become a powerful tool because of its high sensitivity to hydrochemical parameters, as well as its advantages of non-invasiveness, spatial continuity, and cost-effectiveness. However, it is still difficult to integrate hydrochemical, hydrogeological, and ERT datasets for risk assessment. In this study, we develop a general framework for risk assessment by sequentially jointing hydrochemical, hydrogeological, and ERT surveys, while establishing petrophysical relationships among these data. This framework can be used in groundwater-contaminated site and help to delineate the distribution of contaminants. In this study, it was applied to a nitrogen-contaminated site where field ERT survey and borehole information provided valuable measurement data for validating the consistency of contamination and hydrogeological condition. Risk assessment was conducted based on the refined results by the establishment of relationship between conductivity and contaminants concentration withR 2 > 0.84 . The contamination source was identified and the transport direction was predicted with the good agreement ofR 2 = 0.965 between simulated and observed groundwater head, which can help to propose measures for anti-seepage and monitoring. This study thus enhances the reliability of risk assessment and prediction through a thought-provoking innovation in the realm of groundwater environmental assessment.

Theoretical analysis and considerations of the main parameters used to evaluate intrinsic karst groundwater vulnerability to surface pollution

Karst aquifers are highly susceptible to surface pollution scenarios due to exokarst features allowing a fast infiltration regime, bypassing the unsaturated zone. Intrinsic vulnerability maps are a visual interpretation of different levels of vulnerability estimated from multiple arrays of natural characteristics of the aquifer. However, for karst aquifers, this type of analysis is affected by the high subjectivity and personal interpretations of some karst features from hydrological or geological points of view. Current methodologies to assess groundwater vulnerability in karst differ in the number and type of evaluated parameters; they have unsimilar rates, weights, and sometimes a contradictory evaluation of some karst features' hydrogeological behaviour. This paper reviews the main parameters utilized to obtain intrinsic vulnerability maps, including their rating and weighting process, in order to provide additional insights and assist on the process of groundwater vulnerability analysis. After the review of twelve methodologies' guidelines and their application on 45 study areas around the world, new considerations to evaluate parameters and the assignation of rates and weights, according to infiltration scenarios, are here proposed.

A modified DRASTIC model for groundwater vulnerability assessment using connecting path and analytic hierarchy process methods

Groundwater plays a vital role in supporting water for the different needs of domestic, agricultural, and industrial sectors, and its vulnerability assessment to pollution is a valuable tool for establishing protective and preventive management. DRASTIC is a well-known GIS-based model for assessing groundwater vulnerability to pollution, which uses seven parameters including depth-to-water level, net recharge, aquifer media, soil media, topography, the impact of the vadose zone, and hydraulic conductivity. The predefined weights of DRASTIC parameters have made a barrier to its applicability for different regions with different hydroclimatic conditions. To overcome this problem, it has been suggested to apply analytic hierarchy process (AHP) method for modifying the model by adjusting the weights of the parameters. AHP is a widely used method to elicit experts' judgments about different involving parameters through constructing pairwise comparison matrixes (PCMs). Since AHP calculates the weights by performing pairwise comparisons between the parameters, achieving consistent comparisons is difficult when the number of parameters increases. The objective of this research is to modify the DRASTIC model by integrating the connecting path method (CPM) and AHP. The proposed methodology involves asking experts to perform a number of pairwise comparisons between the parameters and then construct an incomplete PCM using the obtained information. To complete the missing values in the PCM, CPM is employed. The CPM is an effective approach that not only estimates missing judgments but also ensures minimal geometric consistency. The proposed method along with DRASTIC and pesticide DRASTIC models is applied to Khoy County, which is located in the northwest part of Iran. The efficiency of the proposed method was further confirmed through the results of the Pearson coefficient test conducted on nitrate concentrations. The test revealed correlation values of 0.47, 0.27, and 0.57 for DRASTIC, pesticide DRASTIC, and modified DRASTIC, respectively. These results demonstrated that the proposed method provides a more precise evaluation of groundwater vulnerability.

Designing dynamic groundwater management strategies through a composite groundwater vulnerability model: Integrating human-related parameters into the DRASTIC model using LightGBM regression and SHAP analysis

Groundwater nitrate contamination has emerged as a pressing global concern. Given its potential for long-term impacts on aquifers, protective measures should primarily focus on prevention. Drawing on the theory of groundwater vulnerability (GV), the original DRASTIC model and parameters related to human activities are employed as inputs and integrated with the LightGBM regression algorithm to facilitate nitrate index (NI) prediction tasks. The SHAP analysis is conducted to effectively examine the contribution of parameters to the NI prediction and interpret the issue of parameter interactions. In addition, to mitigate the limitations of the intrinsic GV model, a composite nitrate index (CNI) is developed by linearly combining the DRASTIC index with the NI. The framework presented in this study provides adaptive strategies for managing groundwater resources over different time periods. A representative region for arid and semiarid climates, the Yinchuan region, is studied using the framework. As compared to 2012, the intrinsic GV index has changed spatially in 2022. Human activities have increased the influence of the nitrate concentration as shown by the Pearson correlation coefficient of -0.082 between the DRASTIC index and nitrate concentration. A significant increase in pollution levels was predicted by NI, ranging from -0.116 to 0.968. According to SHAP analysis, the significant increase in NI levels in 2022 was mainly due to high-value industrial and agricultural production. In 2022, 12.02% of the areas had an increase of at least 0.549 in the CNI. 42.1% of the areas were classified as moderate or high CNI levels. The farm was identified as a high-contributing source to nitrate pollution. The small-scale agricultural and livestock activities in non-urban areas also contribute to groundwater pollution. Dynamic groundwater management strategies need to be implemented in high-growth and high-level CNI areas.

Modeling assessment of groundwater vulnerability to contamination risk in a typical basement terrain using TOPSIS-entropy developed vulnerability data mining technique

This study involved a comparative analysis in the groundwater vulnerability domain, which is a crucial component of groundwater management decision support systems (DSS). This was achieved by creating models that covered the range of algorithms from the subjective to the data-driven. The study was conducted in a basement complex area. Databases of climatic, remote sensing, and geophysical datasets were created using varieties of data acquisition techniques. The datasets included in this assessment were: rainfall (R), land use (LU), bedrock topography (BT), recharge rate (Re), and slope (S). The slope and rainfall were determined to have the highest (0.78) and lowest (0.01) weighted factors, respectively, using the entropy method. For the development of the TOPSIS-Entropy model algorithm, the weights results were combined with the TOPSIS outranking method. To generate the Groundwater Vulnerability Model map of the study area, the hybrid model was applied to griddled raster layers of the factors. Also, the TOPSIS and Entropy-WLA model algorithms were also explored and used to generate groundwater vulnerability maps. The TOPSIS-Entropy algorithms produced an accuracy of 70%, while TOPSIS and Entropy-WLA produced accuracy of 50 and 47%, respectively. The resulting model maps were validated by using correlation technique on the produced map and the longitudinal conductance map of the study area. The TOPSIS-Entropy, which followed an object-oriented model pattern, demonstrates greater accuracy and has the potential to provide appropriate insights and alternatives to decision-making in the field of groundwater hydrology in the study area and other regions of the world with comparable geology.

Water risk modeling: A framework for finance

Climate change has intensified water risk events, threatening water security for societies and ecosystems. Although current water risk models focus on geophysical and business-related effects, they do not quantify the monetary dimensions of water-related challenges and opportunities. This study aims to address this gap by exploring the objectives and directions for modeling water risk in finance. We identify requirements for adequately modeling financial water risk, assess existing water risk approaches in finance, outline their benefits and shortcomings, and develop directions for future modeling. Recognizing the interplay between climate and water as well as the systemic dimension of water risk, we emphasize the need for forward-looking, diversification-based, and mitigation-adjusted modeling approaches.

Groundwater vulnerability assessment of elevated arsenic in Gangetic plain of West Bengal, India; Using primary information, lithological transport, state-of-the-art approaches

Deterioration of groundwater quality is a long-term incident which leads unending vulnerability of groundwater. The present work was carried out in Murshidabad District, West Bengal, India to assess groundwater vulnerability due to elevated arsenic (As) and other heavy metal contamination in this area. The geographic distribution of arsenic and other heavy metals including physicochemical parameters of groundwater (in both pre-monsoon and post-monsoon season) and different physical factors were performed. GIS-machine learning model such as support vector machine (SVM), random forest (RF) and support vector regression (SVR) were used for this study. Results revealed that, the concentration of groundwater arsenic compasses from 0.093 to 0.448 mg/L in pre-monsoon and 0.078 to 0.539 mg/L in post-monsoon throughout the district; which indicate that all water samples of the Murshidabad District exceed the WHO's permissible limit (0.01 mg/L). The GIS-machine learning model outcomes states the values of area under the curve (AUC) of SVR, RF and SVM are 0.923, 0.901 and 0.897 (training datasets) and 0.910, 0.899 and 0.891 (validation datasets), respectively. Hence, "support vector regression" model is best fitted to predict the arsenic vulnerable zones of Murshidabad District. Then again, groundwater flow paths and arsenic transport was assessed by three dimensions underlying transport model (MODPATH). The particles discharging trends clearly revealed that the Holocene age aquifers are major contributor of As than Pleistocene age aquifers and this may be the main cause of As vulnerability of both northeast and southwest parts of Murshidabad District. Therefore, special attention should be paid on the predicted vulnerable areas for the safeguard of the public health. Moreover, this study can help to make a proper framework towards sustainable groundwater management.

Assessment of groundwater quality and human health risks of nitrate and fluoride contamination in a rapidly urbanizing region of India

Groundwater contamination studies are important to understand the risks to public health. In this study, groundwater quality, major ion chemistry, sources of contaminants, and related health risks were evaluated for North-West Delhi, India, a region with a rapidly growing urban population. Groundwater samples collected from the study area were analysed for physicochemical parameters - pH, electrical conductivity, total dissolved solids, total hardness, total alkalinity, carbonate, bicarbonate, chloride, nitrate, sulphate, fluoride, phosphate, calcium, magnesium, sodium and potassium. Investigation of hydrochemical facies revealed that bicarbonate was the dominant anion while magnesium was the dominant cation. Multivariate analysis using principal component analysis and Pearson correlation matrix indicated that major ion chemistry in the aquifer under study is primarily due to mineral dissolution, rock-water interactions and anthropogenic factors. Water quality index values showed that only 20% of the samples were acceptable for drinking. Due to high salinity, 54% of the samples were unfit for irrigation purposes. Nitrate and fluoride concentrations ranged from 0.24 to 380.19 mg/l and 0.05 to 7.90 mg/l, respectively due to fertilizer use, wastewater infiltration and geogenic processes. The health risks from high levels of nitrate and fluoride were calculated for males, females, and children. It was found that health risk from nitrate is more than fluoride in the study region. However, the spatial extent of risk from fluoride is more indicating that more people suffer from fluoride pollution in the study area. The total hazard index for children was found to be more than adults. Continuous monitoring of groundwater and application of remedial measures are recommended to improve the water quality and public health in the region.

Novel machine learning algorithms to predict the groundwater vulnerability index to nitrate pollution at two levels of modeling

The accurate mapping and assessment of groundwater vulnerability index are crucial for the preservation of groundwater resources from the possible contamination. In this research, novel intelligent predictive Machine Learning (ML) regression models of k-Neighborhood (KNN), ensemble Extremely Randomized Trees (ERT), and ensemble Bagging regression (BA) at two levels of modeling were utilized to improve DRASTIC-LU model in the Miryang aquifer located in South Korea. The predicted outputs from level 1 (KNN and ERT models) were used as inputs for ensemble bagging (BA) in level 2. The predictive groundwater pollution vulnerability index (GPVI), derived from DRASTIC-LU model was adjusted by NO₃-N data and was utilized as the target data of the ML models. Hyperparameters for all models were tuned using a Grid Searching approach to determine the best effective model structures. Various statistical metrics and graphical representations were used to evaluate the superior predictive performance among ML models. Ensemble BA model in level 2 was more precise than standalone KNN and ensemble ERT models in level 1 for predicting GPVI values. Furthermore, the ensemble BA model offered suitable outcomes for the unseen data that could subsequently prevent the overfitting issue in the testing phase. Therefore, ML modeling at two levels could be an excellent approach for the proactive management of groundwater resources against contamination.

Combining the classic vulnerability index and affinity propagation clustering algorithm to assess the intrinsic aquifer vulnerability of coastal aquifers on an integrated scale

In the northern plains of Laizhou City, groundwater quality suffers dual threats from anthropogenic activities: seawater intrusion caused by overextraction of fresh groundwater, and vertical infiltration of agricultural pollutants. Groundwater management requires a comprehensive analysis of both horizontal and vertical pollution in coastal aquifers. In this paper, Intrinsic Aquifer Vulnerability (IAV) was assessed on an integrated scale using two classic IAV models (DRASTIC and GALDIT) separately based on a GIS database. Hydrogeological parameters from two classic IAV models were clustered using affinity propagation (AP) clustering algorithm, and silhouette coefficients were used to determine the optimal classification result. In our application, the objects of the AP algorithm are 3320 units divided from the whole study area with 500 m*500 m precision. A comparison of all four outputs in AP-DRASTIC shows that the clustering results of the 4-classification yielded the best silhouette coefficient of 0.406 out of all four. Cluster 4, which comprises 21% of the area, had relatively low level of groundwater contamination, despite its high level of vulnerability as indicated by the classic DRASTIC index. In the second level of vulnerability Cluster 3, 53.8% of all water samples were found to be contaminated, indicating a greater level of nitrate contamination. With respect to AP-GALDIT, the silhouette coefficient for result 7-classification reaches the highest value of 0.343. There was a high level of vulnerability identified in Clusters 2, 4 and 5 (34.7% of the study area) relating to the classic GALDIT index. The concentration of chloride in all water samples obtained in these areas was extremely high. Groundwater management should be addressed by AP-DRASTIC results on anthropogenic activity/contamination control, and by AP-GALDIT results on groundwater extraction limitation. Overall, this method allows for the evaluation of IAV in other coastal areas on an integrated scale, facilitating the development of groundwater management strategies based on a better understanding of the aquifer's essential characteristics.

Groundwater vulnerability assessment using AHP-DRASTIC-GALDIT comprehensive model: a case study of Binhai New Area, Tianjin, China

Binhai New Area (BHNA), as one of the most economically and industrially regions in the Haihe River Basin, China, is seriously affected by seawater intrusion and groundwater over-exploitation. Groundwater vulnerability assessment (GVA) is an effective tool to protect the groundwater resources from being polluted. In this study, vertical and horizontal groundwater conditional factors were first assessed separately by two different models. The AHP-DRASTIC model was used to evaluate the intrinsic groundwater vulnerability and the AHP-GALDIT model was used to evaluate the specific groundwater vulnerability to seawater intrusion. Then, a GIS-based overlaying approach was used to get the comprehensive shallow groundwater vulnerability. The results of the comprehensive model showed that the vulnerability areas of very low, low, medium, and high account for 1.37%, 11.36%, 60.56%, and 26.71%, respectively. Finally, to effectively manage the groundwater in the study area, two remediation areas, two control areas, and one protected area were determined based on the comprehensive groundwater vulnerability maps. This study can not only promote the development of rational exploitation of shallow groundwater and prevention of groundwater pollution in BHNA but also provide a framework for future research in the GVA on the coast.

Rural vulnerability to water scarcity in Iran: an integrative methodology for evaluating exposure, sensitivity and adaptive capacity

The water crisis is the main stress in arid and semi-arid areas, especially in rural areas where agriculture is the main livelihood. This study assessed vulnerability to water scarcity in six rural regions of Isfahan, Iran. These areas have lost their primary water source of agriculture, the Zayandeh Rud River, since 2006. They have confronted many socio-ecological problems which threatened their existence. A mixed methodology was used to assess vulnerability as a function of exposure, sensitivity, and adaptive capacity. Structured questionnaires and in-depth interviews were conducted with key informants and 266 households. The method of Multidimensional Poverty Index was applied to calculate the sensitivity index, which has not been used for sensitivity assessment yet. The results showed that the leading cause of water scarcity is poor water governance. The three districts that had direct access to the Zayandeh Rud river were more vulnerable to water scarcity (scores of 0.35, 0.39, and 0.44) than those that had never had direct access to the river (scores of 0.19, 0.21, and 0.23) due to the more exposure and less adaption to water shortage. Inappropriate financial resilience (from 0.24 to 0.41) and living standards (from 0.19 to 0.36) have made more contributions to creating sensitivity than socioeconomic factors (from 0.14 to 0.28). Different natural capitals have mainly created differences in adaptive capacity across rural areas. Villages located downstream have lost their natural capital due to water-quality degradation caused by river drying up and groundwater overexploitation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10708-022-10726-0.

Groundwater quality assessment and modeling utilizing water quality index and GIS in Kabul Basin, Afghanistan

Groundwater stands as a unique source of water supply in Kabul city, Afghanistan. In this investigation, 35 samples of groundwater were comprehensively analyzed to determine its hydrogeochemical characterizations, quality, water types, and its acceptability as drinking sources. A portable digital multiparameter instrument (LAB MAN Scientific instrument) was used to measure the total dissolved solids (TDS), hydrogen potential (pH), and electrical conductivity (EC). Total hardness, chloride, and bicarbonate were examined via a titrimetric approach. Sodium, calcium, magnesium, and potassium concentrations were measured with a flame photometer. Fluoride was determined by using a digital portable multiparameter. UV-VIS spectrophotometers were employed to count sulfate and nitrate concentrations. The distribution pattern of measured parameters and the Water Quality Index (WQI) in groundwater were spatially modeled utilizing the ArcGIS tool. The findings provide insight into the main anions and cations, which are found in ascending sequence F < NO₃ < SO₄ < Cl < HCO₃ and K < Ca < Na < Mg, respectively. Based on the measurements of ion concentrations, bicarbonate (71.4%), chloride (14.28%), nitrate (2.85%), magnesium (80%), sodium (82.85%), calcium (5.71%), and potassium (17.14%) were all determined to be over the World Health Organization (WHO) limits of drinking water. Using the Piper trilinear diagram, two significant hydrochemical facies (CaNaHCO₃ and NaHCO₃) were discovered. Based on the mathematical model of WQI outputs, 88.57% of the research region has excellent to good water, whereas 11.43% has poor to very poor water.

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.

Groundwater vulnerability assessment: A review including new statistical and hybrid methods

The concept of groundwater vulnerability was first introduced in the 1970s in France to recognize sensitive areas in which surface pollution could affect groundwater, and to enable others to develop management methods for groundwater protection against surface pollutants. Since this time, numerous methods have been developed for groundwater vulnerability assessment (GVA). These can be categorized into four groups: (i) overlay and index-based methods, (ii) process-based simulation models, (iii) statistical methods, and (iv) hybrid methods. This work provides a comprehensive review of modern GVA methods, which in contrast to previous reviews, examines the last two categories in detail. First, the concept of groundwater vulnerability is defined, then the major GVA methods are introduced and classified. This includes detailed accounts of statistical methods, which can be subdivided into orthodox statistical, data-driven and Bayesian methods, and their advantages and disadvantages, as well as modern hybrid methods. It is concluded that Bayesian inference offers many advantages compared with other GVA methods. It combines theory and data to give the posterior probabilities of different models, which can be continually updated with new data. Furthermore, using the Bayesian approach, it is possible to calculate the probability of a proposition, which is exactly what is needed to make decisions. However, despite the advantages of Bayesian inference, its applications to date have been very limited.

Sensitivity, Hazard, and Vulnerability of Farmlands to Saltwater Intrusion in Low-Lying Coastal Areas of Venice, Italy

Saltwater intrusion is a growing threat for coastal aquifers and agricultural practices in low-lying plains. Most of the farmlands located between the margin of the Southern Venice lagoon and the Northern Po delta, Italy, lie a few meters below mean sea level and are drained by a large network of artificial channels and hydraulic infrastructures to avoid frequent flooding and allow agricultural practices. This work proposes an assessment of the vulnerability to saltwater intrusion, following a new concept of the hazard status, resulting in combining the depth of the freshwater/saltwater interface and the electrical resistivity of the shallow subsoil. The sensitivity of the farmland system was assessed by using ground elevation, distance from freshwater and saltwater sources, permeability, potential runoff, land subsidence, and sea-level rise indicators. Relative weights were assigned by a pairwise comparison following the Analytic Hierarchy Process approach. The computed vulnerability map highlights that about 30% of the farmlands is under strong and extreme conditions, 28% between marginal and moderate, and 40% under negligible conditions. Results from previous vulnerability assessments are discussed in order to explain their differences in terms of hazard status conceptualization and sensitivity characterization of farmland system.