Groundwater Vulnerability Mapping Research Articles

Advancing aquifer vulnerability mapping through integrated deep learning approaches

Groundwater vulnerability maps are crucial for safeguarding groundwater quality. A research gap exists in using advanced data fusion techniques to identify areas subject to seawater intrusion. To address this gap, this research enhances the GALDIT method and applies diverse deep learning models, combined with machine learning techniques, to improve the precision of aquifer vulnerability mapping. The new GALDITMW model incorporates the seawater mixing index and the parameters related to the production well density and aquifer porous medium. For the first time, supervised and unsupervised deep learning models, such as deep neural networks, deep belief networks, deep stacked autoencoders, and convolutional neural networks, are used for vulnerability mapping. In the second stage, the results of various machine learning models are fused to improve performance. The models' effectiveness is evaluated using a vulnerability index based on total dissolved solids (TDS) in an aquifer hydraulically connected with Salt Lake in central Iran, which faces groundwater depletion and salinization. The evaluation of the models based on performance metrics and the confusion matrix demonstrates that initial deep-learning models perform well. Significant improvements were observed in the second stage involving machine learning models, confirming their strong correlation (R2 > 0.985) with observed chloride values. The GPR model achieved an F1 score of 86.92%, an NSE of 0.911, and an RMSE reduction of 0.026 mg/L compared to the first-stage models. The proposed method offers a novel and accurate method for identifying vulnerable areas and provides helpful information for groundwater resource management.

Assessing groundwater contamination risk in industrial zone of Ranipet district, Southern India: A modified DRASTIC and Fuzzy-AHP approach

Groundwater plays a substantial function in sustaining the water requirements of Ranipet district, Tamil Nadu, India, across a variety of domains, including drinking water, agriculture, and industry. However, rising demand and industrial activities have caused concern about groundwater pollution and susceptibility in the region. So, this study employed a modified DRASTIC model to find the regions that are most vulnerable to groundwater contamination. The model includes a novel parameter, "proximity to industry,” in addition to the seven traditional factors (depth to water level, net recharge, aquifer media, soil media, topography, impact of vadose zone and hydraulic conductivity) to account for the industrial nature of the study region. The fuzzy-analytical hierarchy process (FAHP) was used to compute parameter weights and ratings. All the parameters are overlayed in ArcGIS software to create the groundwater vulnerability map (GWVM). The map revealed that the central area along the Palar River is highly vulnerable to contamination. The southern and northern parts have medium to extremely low groundwater contamination risks. The depth of the water table was the most significant component impacting the vulnerability, as per the sensitivity analysis. The model is further validated by chromium concentrations, which were shown to be high in highly sensitive zones and low in low-risk zones. This study also suggests the need for extensive and periodic groundwater quality assessments in highly vulnerable zones for sustainable water resource management.

Early Warning of Regional Groundwater Pollution： A Case Study for Plain Area of Barkol-Yiwu Basin

Groundwater pollution early warning is an effective means for regional groundwater pollution prevention. The groundwater pollution early warning model coupled with the current situation of groundwater quality, groundwater quality variation trend, and groundwater pollution risk were applied to the plain area of Barkol-Yiwu Basin, and the regional scale groundwater pollution early warning was realized by combining the early warning of groundwater quality status and trend. The TOPSIS method based on comprehensive weight was used to evaluate the current situation of groundwater quality. The variation trend of groundwater quality was analyzed by calculating the trend interpolation results of 18 in-situ groundwater quality monitoring wells. The groundwater vulnerability map, groundwater pollution load map, and groundwater function value map were superimposed using the superposition index method to evaluate groundwater pollution risk. The results showed that the groundwater quality was good and relatively good, and the poor groundwater quality in some areas was mainly affected by the shallow groundwater depth and the large porosity of the vadose zone. Groundwater quality was stable from 2011 to 2022, mainly due to the leakage of wastewater generated by industries and agriculture into groundwater, resulting in the deterioration of groundwater quality in some areas. Groundwater pollution risk was generally low, and the dual effects of high vulnerability and high pollution load of groundwater led to local areas with high pollution risk. The early warning level of groundwater pollution was generally low, and the heavy and highly heavy warning areas accounted for 16.4% and 17.5% of the study area, respectively, mainly distributed in Xiamaya Township of Yiwu County and northern Santanghu Town, Dahongliuxia Township, and Dahe Town of Barkol County. The quaternary sediments exposed were mainly sandy pebbles, with developed pores and strong water permeability. The interception and adsorption capacity of pollutants were weak. Pollutants produced by industries, agriculture, and life easily leaked into groundwater aquifers, resulting in poor groundwater quality and high risk of groundwater pollution, which ultimately led to a high early warning level of groundwater pollution in some areas. The research on early warning of groundwater pollution provided an important theoretical basis for the development of groundwater pollution remediation.

Investigation of Aquifer Vulnerability under some Protective Measures in Ehime Mbano, South-Eastern Nigeria for Sustainable Groundwater Development

This study focuses on assessing aquifer vulnerability to contamination in Ehime Mbano South-Eastern Nigeria by applying the DRASTIC model. The data was obtained using (VES) vertical electrical sounding technique by applying Schlumberger configurations with AB/2 = 400 m. The VES data were interpreted using state of the art soft wares e.g. IP12WIN and Surfer 12 to obtain the final model for each VES, groundwater vulnerability map was also developed while aquifer media was gotten by taking into account, the depth at which water was struck and correlating those depths with the lithological description of the VES results obtained. The hydraulic conductivity was calculated from apparent resistivity of the aquifer. However, the net recharge, soil media, and topography was obtained from research documentaries. Aquifer vulnerability assessment carried out revealed areas with high, low and moderate vulnerability based on the DRASTIC Index. Locations with high vulnerability rating of 126 -165 includes: Umuokiri Umunumo and Ikperejere. Locations with moderate vulnerability rating of 86-125 includes: Ikpem, Ikweii Nzerem while locations with low vulnerability rating of 70-85 includes: Umuokara Uzinomi and areas close to Umueze II. The findings of the study can be used to identify regions of contamination of groundwater in the study area.

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.

Introducing a novel approach for assessment of groundwater salinity hazard, vulnerability, and risk in a semiarid region

The application of machine learning approaches to improve groundwater salinity risk mapping is limited despite all potential advantages. Therefore, there is an ongoing need for investigations that present new techniques, like machine learning, validated against conventional methods. These advances are particularly important for arid and semiarid regions, such as the Bakhtegan Basin (southern Iran), where groundwater use may far outweigh recharge, but groundwater management improvements are essential. To address these needs, groundwater salinity hazard, vulnerability, and risk maps were investigated using an integrated application of statistical (i.e., frequency ratio and statistical index models), machine learning (i.e., random forest and classification and regression trees algorithms), and decision-making models (fuzzy analytic hierarchy process (FAHP)). Results showed that the risk of groundwater salinity was high in the central areas of the region as well as at the margins of Bakhtegan Lake and irrigated farming lands. Based on the modeling results in the testing phase, it was found that the frequency ratio and random forest models exhibited better performance, with Nash–Sutcliffe efficiency metrics of 0.73 and 0.70, respectively, compared to the classification and regression trees and statistical index models, which had Nash–Sutcliffe efficiency metrics of 0.65 and 0.63, respectively. The innovative techniques developed in the current work accurately identified variables such as normalized difference salinity index, distance from mines, and land use associated with the highest weight values (0.59, 0.14, and 0.11, respectively) compared to other variables to identify groundwater salinity areas of vulnerability. The application of the fuzzy analytic hierarchy process method in developing vulnerability maps, integrating them with hazard maps, and developing groundwater salinity risk maps was innovative in this study. The innovative approach is transferrable for groundwater salinity susceptibility, vulnerability, and risk assessments globally in the study area and similar settings.

Assessment of Groundwater Vulnerability to Seawater Intrusion Using GALDIT, SITE and SIVI Methods in Laspias River Coastal Aquifer System, NE Greece

This paper presents the investigation of groundwater vulnerability to seawater intrusion of the aquifer system in the coastal area of Laspias River, NE Greece, for the year 2023, by applying the GALDIT, SITE and SIVI methods, in the context of the groundwater management of the area. The relevant research works include the collection and analysis of data and information regarding the geological and geomorphological environment, as well as the hydrogeological system of the area. The calculation of the GALDIT, SITE and SIVI indices values is described, and the results from the application of the methods are presented, as well as the design of relevant groundwater vulnerability maps of the study area. This paper concludes with findings and proposals useful for the reliable assessment of the hydrogeological regime of the wider study area.

Оценка уязвимости карстовых подземных вод к загрязнению на основных массивах Горного Крыма с использованием региональной методики

Introduction. Assessment of groundwater vulnerability to contamination is an effective tool for scientific substantiation of the sanitary protection zones for drinking water sources. Karst ground waters of the Mountain Crimea form the main resources for water supply in the Crimean Peninsula. This study has a purpose to perform the assessme nt and mapping of karst ground waters resource vulnerability to contamination on such representative massifs of the Crimean Mountains as Chatyrdag, Dolgorukovsky, Karabi and Ay-Petri . Materials and methods. Assessment of karst ground waters vulnerability is conducted by use of Mountain -Crimean method. It has been developed on the base of some wide-tested European methods (COP-method and Slovene approach) with methodological modification and adaptation to regional conditions of the Mountain Crimea. The input data for assessment is spatial information about ground waters vulnerability factors: protective layers in vadose zone, flow concentration by streams, sinkholes and caves, precipitation regime etc. The collected spatial information was converted into attributed geo data layers, which were subsequently subjected to overlay operations in Arc GIS 10 software according to assessment scheme. Results. According to the assessment results, approximately 34 and 14% of the Chatyrdag massif area gained the category of high and very high vulnerability, on the Karabi massif – 14 and 17%, on the Dolgorukovsky massif – 14 and 2%, on the Ay-Petri massif – 19 and 7% of total area respectively. The result ing vulnerability maps demonstrate significant mosaics and contrast, which is associated with discrete distribution of protective covers and high differentiation of ground water recharge conditions within the catchments of karst massifs. Discussion. Analysis of the resulting vulnerability maps have revealed that the leading factors determining areas of high and very high vulner ability of karst waters are runoff concentration factors, while the factors of protective cover mainly determine the areas with low vulnerability. The zones of high vulnerability on the Chatyrdag and Karabi massifs are mainly coincide with areas of high density of karst forms (sinkholes and caves), while high vulnerability zones of the Ay-Petri and Dolgorukovsky massifs are predominantly associated with areas near permanent and temporary watercourses. In general, the obtained maps of groundwater vulnerability are quite consistent with the known data on the hydro-geological conditions of the assessed massifs. Conclusions. The identified differences in the area ratio and spatial distribution of ground waters vulnerability categories between the assessed massifs are a consequence of local geological, geo-morphological and hydrological features. The obtained results confirm high applicability of the Mountain-Crimean regional method for groundwater vulnerability assessment, at least for the karst massifs of the Mountain Crimea. Suggestions for practical application and direction for future research. The produced groundwater vulnerability maps create a scientific basis for delineation of water protection zones, assessing the risks of groundwater pollution and developing the territorial planning schemes within studied areas. The next stage of study is an assessment of groundwater source vulnerability for individual karst springs used in water supply.

GROUNDWATER VULNERABILITY MAP OF BASARA BASIN, SULAIMANI GOVERNORATE, IRAQI KURDISTAN REGION

The Basara basin which is one of the most promising hydrogeological basin in Iraqi Kurdistan Region, located in north east of Iraq, 25 km west of Sulaimani city, between the 496652 - 537752 East and 3911038 - 3951906 North in Universal Transverse Mercator (UTM) and lie in Zone 38N. The basin has a rectangular shape and covering an area of 571 km². The present hydrogeological investigations have revealed three inhomogeneous and anisotropic water bearing formations: Eocene Karstic Fissured Aquifer (EKFA), Intergranular Aquifer represented by Alluvium and Pliocene (AIA) & (PIA), as well as Miocene Complex Aquifer (MCA).For the first time, not only in Kurdistan but also in Iraq, Groundwater Vulnerability Map has been constructed in this study, using DRASTIC method with the assistance of Geographic Information System (GIS) to show zonation area of high and low groundwater susceptibility to pollution. Accordingly, vulnerability classes of the study area were classified into four classes. Most of the basin shows the highest extension of the zones with very low and low vulnerability zones, in contrast the zones with high vulnerability are distributed mainly in the mountain areas, solely in the eastern Uloblagh and Kuwaik mountains, in addition to that small zones in the farthest northern corner and south western corner of the area have less or no human activity.

Using VES and GIS-Based DRASTIC Analysis to Evaluate Groundwater Aquifer Contamination Vulnerability in Owerri, Southeastern Nigeria

This study assesses groundwater vulnerability in Owerri, Nigeria, using Vertical Electrical Sounding (VES) and Geographic Information System (GIS)-based DRASTIC modeling. The research methodology includes literature review, field survey, geological feature mapping, hydrogeological assessment, geo-electrical sounding, and data interpretation. Owerri, a rapidly developing city with flat topography and a growing population, uses the DRASTIC model to construct a groundwater vulnerability map. The model evaluates the risk of groundwater contamination using seven critical criteria, including depth to water table, net recharge, aquifer media, soil media, topography, vadose zone impact, and hydraulic conductivity. Each parameter was given a weight and rating, and the DRASTIC Index (DI) was calculated by summing the products of the weights and ratings for each factor. The results of the vulnerability assessment indicated that approximately 49% of the study area falls into the high vulnerability category, around 45% is classified as moderate vulnerability, and the remaining 6% is labeled as low vulnerability. The study reveals moderate to high vulnerability zones in Owerri, Nigeria, due to factors like lower slope terrain, permeable aquifer media, and vadose zone impact. The use of VES and GIS-based DRASTIC mapping techniques provides insights into groundwater vulnerability, aiding in sustainable resource management and environmental protection. The findings emphasize the importance of understanding potential risks and the need for effective management strategies to safeguard clean water supplies. Further research and mitigation efforts should focus on highly vulnerable areas.

Advancing groundwater vulnerability assessment in Bangladesh: a comprehensive machine learning approach

In response to Bangladesh's severe water shortages, this research delves into the intricate dynamics of groundwater vulnerability. Integrating often-overlooked factors such as topography, meteorology, socio-economic conditions, and land use & geology, the study employs advanced Random Forest (RF) modeling. Rigorously analyzing 200 strategically chosen sample points, the research uncovers critical areas like Rajshahi, Nawabganj, Naogaon, and Dhaka, constituting 21% of the land, as highly vulnerable. In contrast, regions like Rangpur, Mymensingh, and Barisal, encompassing 31% of the area, exhibit lower vulnerability. Topographic factors, accounting for 45% of the vulnerability, including aspect, drainage density, and slope, significantly influence susceptibility. Socio-economic elements contribute 22%, particularly population density and industrial activities. The RF model achieves exceptional accuracy (over 90%), emphasizing the complexity of groundwater dynamics. By integrating geological, social, and economic aspects, the study provides actionable insights for nuanced and sustainable management strategies. This research not only unveils a highly accurate groundwater vulnerability map, enriching scientific understanding, but also offers a unique approach by incorporating often-overlooked variables and leveraging machine learning. These insights empower policymakers and urban planners to craft targeted and sustainable groundwater management strategies, ensuring a resilient water supply system for Bangladesh's growing population. Through this work, the aim is to significantly contribute to the scientific community while providing data-driven solutions for the nation's pressing water challenges.

Enhancing groundwater vulnerability assessment for improved environmental management: addressing a critical environmental concern

Groundwater serves as a primary water source for various purposes. Therefore, aquifer pollution poses a critical threat to human health and the environment. Identifying the aquifer’s highly vulnerable areas to pollution is necessary to implement appropriate remedial measures, thus ensuring groundwater sustainability. This paper aims to enhance groundwater vulnerability assessment (GWVA) to manage aquifer quality effectively. The study focuses on the El Orjane Aquifer in the Moulouya basin, Morocco, which is facing significant degradation due to olive mill wastewater. Groundwater vulnerability maps (GVMs) were generated using the DRASTIC, Pesticide DRASTIC, SINTACS, and SI methods. To assess the effectiveness of the proposed improvements, 24 piezometers were installed to measure nitrate concentrations, a common indicator of groundwater contamination. This study aimed to enhance GWVA by incorporating new layers, such as land use, and adjusting parameter rates based on a comprehensive sensitivity analysis. The results demonstrate a significant increase in Pearson correlation values (PCV) between the produced GVMs and measured nitrate concentrations. For instance, the PCV for the DRASTIC method improved from 0.42 to 0.75 after adding the land use layer and adjusting parameter rates using the Wilcoxon method. These findings offer valuable insights for accurately assessing groundwater vulnerability in areas with similar hazards and hydrological conditions, particularly in semi-arid and arid regions. They contribute to improving groundwater and environmental management practices, ensuring the long-term sustainability of aquifers.

Assessing Groundwater Resources Vulnerability with the New DRASTIC-LP: A Case Study in Chiang Mai Province, Thailand

Groundwater vulnerability has gained widespread attention, particularly in Chiang Mai Province, one of Thailand’s fastest-growing cities, which is experiencing rapid development in both economic and social sectors. The expansion of urban communities and the industrial, tourism, and agriculture sectors has resulted in the overutilization of available resources, notably water resources. This overuse, coupled with the adoption of modern technology to boost productivity and meet market demands, has led to an increased reliance on groundwater to supplement surface water sources, providing benefits across all sectors. However, the economic and social growth plays a pivotal role in shaping the diversity of land use, encompassing residential, commercial, industrial, and agricultural activities. These activities, in turn, directly contribute to environmental pollution, particularly in terms of the risk of groundwater contamination in Chiang Mai Province. This study aims to predict the future vulnerabilities of groundwater resources under an ensemble of climate change scenarios and changes in land-use patterns. Chiang Mai Province in northern Thailand is one of the fastest-growing cities and therefore is experiencing rapid urbanization, as well as land-use pattern changes, which was important for the case study. The new DRASTIC model, namely the DRASTIC-LP model, combined with GIS-based techniques and overlay techniques, was used to generate the map of groundwater resource vulnerabilities. A point pollution source (P)-related land-use pattern (L) that represents contamination impacts was considered an additional new DRASTIC parameter. The study’s findings reveal the high reliability and maximum effectiveness of the new DRASTIC-LP model in assessing groundwater vulnerability and contamination-risk areas under a climate change scenario (by MIROC-ESM-CHEM model under RCP.8.5 scenario) and land-use pattern changes (by CA_Markov Chian Model) for both the current year (2020) and the next 50-year period (2021–2070). Furthermore, the new DRASTIC-LP model is employed to trace the movement of pollutants from high- to very high-risk areas based on the groundwater vulnerability and contamination-risk maps. The results highlight that waste disposal dumping sites pose a more critical distribution and movement of pollutants when compared to industrial sites. Additionally, unconsolidated aquifers and cracked consolidated rock aquifers show a potentially higher occurrence of pollutant distribution and movement when compared to consolidated aquifers. Consequently, the study’s outcomes are applied to formulate guidelines for the management and control of groundwater resource contamination. These guidelines serve as valuable tools for decision makers, aiding in pollution prevention and the effective management of contamination risks in groundwater resources.

Groundwater vulnerability mapping of hyper-humid urban aquifers for an improved water management in African megacities, opportunities and limitations in the case of Douala (Cameroon)

Groundwater vulnerability mapping of hyper-humid urban aquifers for an improved water management in African megacities, opportunities and limitations in the case of Douala (Cameroon)

Hydro-chemical based assessment of groundwater vulnerability in the Holocene multi-aquifers of Ganges delta

Determining the degree of high groundwater arsenic (As) and fluoride (F−) risk is crucial for successful groundwater management and protection of public health, as elevated contamination in groundwater poses a risk to the environment and human health. It is a fact that several non-point sources of pollutants contaminate the groundwater of the multi-aquifers of the Ganges delta. This study used logistic regression (LR), random forest (RF) and artificial neural network (ANN) machine learning algorithm to evaluate groundwater vulnerability in the Holocene multi-layered aquifers of Ganges delta, which is part of the Indo-Bangladesh region. Fifteen hydro-chemical data were used for modelling purposes and sophisticated statistical tests were carried out to check the dataset regarding their dependent relationships. ANN performed best with an AUC of 0.902 in the validation dataset and prepared a groundwater vulnerability map accordingly. The spatial distribution of the vulnerability map indicates that eastern and some isolated south-eastern and central middle portions are very vulnerable in terms of As and F− concentration. The overall prediction demonstrates that 29% of the areal coverage of the Ganges delta is very vulnerable to As and F− contents. Finally, this study discusses major contamination categories, rising security issues, and problems related to groundwater quality globally. Henceforth, groundwater quality monitoring must be significantly improved to successfully detect and reduce hazards to groundwater from past, present, and future contamination.

Groundwater Vulnerability Mapping Using the Susceptibility Index (SI) Method and Tritium Isotopes: A Case Study of the Gharb Aquifer in Northwestern Morocco

The Gharb aquifer plays a critical role as a source of drinking water and irrigation in Morocco. However, rapid economic growth and increased use of chemical fertilizers have led to groundwater pollution and land degradation. To address this issue, a vulnerability assessment was conducted using the Susceptibility Index (SI) method. Five parameters including depth to groundwater, effective recharge, slope, soil type, and land use/land cover were considered to evaluate groundwater vulnerability. The vulnerability assessment revealed index values ranging from 31 to 160. The study area was categorized into three zones of relative vulnerability (low, moderate, and high). Spatial analysis demonstrated significant spatial heterogeneity, with moderate vulnerability observed in areas characterized by shallow groundwater and composed primarily of sandstones in the northwest and southwest regions. In the entire study area, the low, medium, and high vulnerability zones covered 3%, 70%, and 26% (101 km2, 2725 km2, and 986 km2), respectively. Furthermore, Tritium isotope analysis and nitrate content were selected and applied as pollution markers to confirm and validate the obtained vulnerability map. The Tritium (3H) content in groundwater reveals a correlation with an R2 value of 0.86 with the degree of vulnerability, while the nitrate concentration in the aquifer’s groundwater showed a correlation with an R2 value of 0.55.

GIS based Groundwater Vulnerability Assessment of Lahore a Metropolitan using Modified DRASTIC Model

The metropolitans of the developing countries are experiencing the hazard of low quality drinking water. Lahore, the heart of Pakistan, has over 12 million population and is going to face the shortage of clean drinking water. The study was designed to create a groundwater vulnerability map of Lahore using modified DRASTIC model. The water table, land use/cover, hydraulic conductivity, vadose zone, soil and aquifer media layers of Lahore aquifer were analyzed using ‘geostatistical analyst’ and ‘spatial analyst’ extensions of ArcGIS software to compute the groundwater vulnerability. The Vulnerability index of modified DRASTIC model ranged from 64 – 144. The spatial variability of modified DRASTIC index were divided into 4 categories, namely, Low, Moderate, High and Very High groundwater vulnerable zones. The modified DRASTIC model revealed that 51.38% areas have low vulnerability, 36.77% areas have moderate vulnerability, 9.48% areas have high vulnerability and 2.37% areas have very high vulnerability to groundwater pollution. The ‘high’ and ‘very high’ vulnerability classes were found in the western parts of Lahore. The results of modified DRASTIC model were validated using the groundwater contamination data of arsenic in the study area. The groundwater arsenic concentrations map also showed higher values in the northwestern parts while in the eastern parts of the study area, the arsenic concentrations had a decreasing trend. In Ravi Town, the arsenic concentrations were positively correlated with modified DRASTIC vulnerability index and inversely correlated with water table depth (p < 0.01). It is recommended that the arsenic removal plants should be installed at all the tubewells with higher arsenic levels (arsenic concentration > 50 ppb) to provide safe drinking water to the citizens of Lahore. It is recommended that the modified DRASTIC model based on geospatial techniques can be applied on other metropolitans of the world for taking preventive measures against massive water pollution.

Modelling Groundwater Vulnerability to Contamination using DRASTIC Model through Geospatial Techniques over Northern Kwazulu-Natal, South Africa

This study models groundwater vulnerability to contamination in three northern district municipalities (Amajuba, Zululand and Umkhanyakude) in KwaZulu Natal province in South Africa using GIS-based DRASTIC model. The method considers seven parameters: depth to water table (D), recharge (R), aquifer media (A), soil media (S), topography (T), impact of the vadose zone (I), and hydraulic conductivity (C). DRASTIC parameter maps are generated in ArcGIS environment and relevant weights assigned. A weighted overlay analysis is then employed to generate the groundwater vulnerability map for the study area. Finally, the groundwater vulnerability map is combined with land use/cover to obtain groundwater pollution risk map. Results indicate that 22, 45, 21 and 12% of the total area are under low, moderate, high, and very high groundwater contamination vulnerable zones, respectively. Low, moderate, high, and very high groundwater pollution risk are found in 23, 40, 27 and 10% of the total area, respectively. These results can be used by environmental managers, spatial planers and other policy makers in formulating integrated and sustainable development plans to ensure optimal groundwater exploitation and conservation in the northern KwaZulu Natal region.

A comparison of methods for assessing groundwater vulnerability in karst aquifers: the case study of Terminio Mt. aquifer (Southern Italy)

The assessment of groundwater vulnerability to pollution is becoming even more important all over the world due to the increase of impacts of human activities on groundwater resources and the related risks to the human health, economics, and the environment. Owing to the variability of methods known for estimating groundwater vulnerability, basically depending on hydrogeological parameters considered and the scale of analysis, the comparison of results of different methods appears straightforward for identifying the best approach in a given hydrogeological condition and reference scale. In such a view, this work attempts to assess the groundwater vulnerability of the Terminio Mt. karst aquifer, by applying four different groundwater vulnerability methods, index-based, and comparing results in order to identify the best performing one in karst environments. The study aquifer, located in the Picentini Mts Regional Park (Campania region, southern Italy) represents a strategic drinking water resource since Roman times and hosts massive groundwater resources which outflow mainly from tapped basal and subordinately perched springs.The peculiar characters of the study karst aquifer, which favour direct infiltration and groundwater recharge processes, as well as the occurrence of industrial, agricultural and grazing activities, make it very vulnerable to groundwater pollution, thus requiring a proper and careful territorial management.Beside the most frequently and generally used methods for assessing groundwater vulnerability, such as the DRASTIC and SINTACS, also DAC and COP methods specifically designed for karst aquifers were applied and mutually compared. Results of SINTACS, DRASTIC and DAC methods show groundwater vulnerability maps of the Terminio Mt. karst aquifer as chiefly characterized by two classes of intrinsic groundwater vulnerability, varying between the medium and high degrees. Furthermore, high and extremely high values of groundwater vulnerability were found in areas controlled by the shallow depth of the water-table. Instead, the COP method resulted as the most effective in identifying the endorheic areas and the related karst morphologies as very high groundwater vulnerability zones, therefore the most suitable in capturing specific hydrogeological features of karst areas that control groundwater pollution and vulnerability.Results obtained will support decision tools aimed at the land use planning and protection of karst aquifers from pollution in karst areas.