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Volume 38, Issue 2 (2023)
GeoRes 2023, 38(2): 221-231 |
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Received: 2022/12/22 | Accepted: 2023/04/19 | Published: 2023/05/31
Received: 2022/12/22 | Accepted: 2023/04/19 | Published: 2023/05/31
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Pourkhosravani M, nezhadafzali K, Jamshidi Gohari F. Evaluation of Jiroft Plain Aquifer Vulnerability Potential Using DRASTIC and CD Models. GeoRes 2023; 38 (2) :221-231
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1- Department of Geography and Urban Planning, Faculty of Literature and Humanities, Shahid Bahonar University of Kerman, Kerman, Iran
2- Department of Geography, Faculty of Literature and Humanities, Jiroft University, Kerman, Iran
2- Department of Geography, Faculty of Literature and Humanities, Jiroft University, Kerman, Iran
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Introduction
Groundwater resources are among the most essential and vital water supplies across large parts of the world, particularly in arid and semi-arid regions. Unfortunately, due to the rapid growth of population and increasing water demands, the consequent over-extraction of groundwater, the rising use of fertilizers, herbicides, and pesticides to enhance food production, the expansion of urbanization, and the rapid growth of industries without the establishment of proper wastewater disposal and treatment systems and, consequently, the excessive discharge of domestic, industrial, and agricultural effluents into aquifers the quality of these valuable resources has been deteriorating and is increasingly at risk of contamination [Nadiri et al., 2018]. Unlike surface water pollution, groundwater contamination poses even greater concerns since the detection of pollutants in groundwater is challenging. Furthermore, due to the weak self-purification capacity of groundwater, the removal of contaminants and the rehabilitation of aquifers are significantly more complex, requiring substantial time, enormous financial investment, and advanced facilities. Consequently, the pollution generated may persist for years, decades, or even centuries.
In this regard, the U.S. National Research Council (1993) defines vulnerability as the tendency or likelihood of contaminants introduced above the aquifer surface to reach a specific location within the groundwater system [Wang et al., 2007]. The preparation of vulnerability maps, or in other words, vulnerability zoning, is considered a powerful yet cost-effective method to identify aquifers requiring priority monitoring and protection, as well as to select safe zones for urban, agricultural, and industrial development that would inflict minimal damage on groundwater resources. Such maps assist planners and decision-makers in groundwater protection. Vulnerability mapping is based on the premise that some regions are more susceptible to groundwater pollution than others [Moratalla et al., 2011]. Practically, vulnerability maps can be used to localize potentially hazardous activities in areas where aquifers are less threatened. Simultaneously, they identify sensitive aquifers that require enhanced protection and thus facilitate urgent monitoring and remediation measures [Bhuvaneswaran & Ganesh, 2019; Hasan et al., 2019].
To assess the potential vulnerability of aquifers and develop vulnerability maps, multiple methods exist, generally classified into three groups: process-based or simulation methods, statistical methods, and overlay and index methods [Javadi et al., 2011]. Among these, the DRASTIC and Composite DRASTIC models are the most common, widely recognized, and frequently applied index-overlay models, employed extensively by researchers to estimate groundwater vulnerability. For example, Neshat et al. apply the DRASTIC method and concluded that the southern and southeastern parts of the study area in Kerman require considerable protection against contamination [Neshat et al., 2014]. The findings of Sinha et al. have indicated that out of a total study area of 4,191 km², approximately 8, 197, 2,730, 1,229, and 27 km² were categorized into very low, low, moderate, high, and very high vulnerability classes, respectively [Sinha et al., 2016]. Similarly, Neshat and Pradhan have found that the DRASTIC model demonstrates high accuracy in assessing groundwater vulnerability to contamination [Neshat & Pradhan, 2017]. Hao et al. report that the presence of both vulnerable areas with good and poor water quality highlights discrepancies between vulnerability indices and actual groundwater quality [Hao et al., 2017]. Mfonka et al., through the application of the DRASTIC model to assess the aquifer in Foumban, have concluded that the study area was weak to moderately vulnerable [Mfonka et al., 2018]. Kadkhodaie Ilkhchi et al. show that in evaluating the vulnerability of the Shabestar Plain aquifer, the higher determination coefficients of the Syntax, Wilcox, and Genetic Algorithm models, compared to optimized DRASTIC models, indicate their superior performance in the study region [Kadkhodaie Ilkhchi et al., 2019]. Moreover, the vulnerability assessment of the Shandiz–Torqabeh aquifer using DRASTIC revealed that river flow from the northeastern part of the basin facilitates greater recharge, potentially transporting fertilizers from surrounding farmlands into groundwater, thereby increasing aquifer vulnerability. Very low and low vulnerability zones were also identified in the western and central parts of the basin [Amiri et al., 2020]. Likewise, Bera et al. have categorized groundwater vulnerability into five classes, very high, high, moderate, low, and very low, using the DRASTIC index [Bera et al., 2021].
Groundwater resources constitute one of the primary water supplies in Jiroft County, located in the south of Kerman Province, and are utilized for both drinking and agricultural purposes. Therefore, considering the significance of groundwater in providing water for drinking, agricultural, and industrial uses in the study area, the present research was conducted with the objective of evaluating the vulnerability of the Jiroft aquifer using the DRASTIC and Composite DRASTIC models.
Methodology
The present study employed a descriptive–analytical approach and was conducted in the Jiroft Plain aquifer, located in the south of Kerman Province, in 2022. The region is characterized by hot and relatively humid summers and short mild winters. The mean annual precipitation is approximately 170 mm, and the elevation of the plain ranges between 400 and 500 m above sea level. The aquifer, with an area of about 1,400.95 km², lies between longitudes 57°33′–58°09′ and latitudes 28°11′–28°51′.
To evaluate the aquifer vulnerability to contamination, two models were applied: DRASTIC and Composite DRASTIC. The analysis considered seven parameters: depth to water table, net recharge, aquifer media, unsaturated zone media, soil media, topography, and hydraulic conductivity. In the Composite DRASTIC model, land use was added as an additional factor to better reflect the influence of anthropogenic activities. All parameters were processed and mapped using ArcGIS 10.3, and interpolation was carried out with the Inverse Distance Weighted (IDW) method.
Depth to Water Table (D): Greater depth generally reduces the vulnerability of the aquifer. To prepare this layer, data from 26 wells were obtained from the Kerman Regional Water Organization, digitized, and interpolated in GIS to produce a depth-to-water map.
Net Recharge (R): Recharge was estimated based on precipitation, slope, and soil permeability. Digital elevation models and soil maps were used to derive relevant classifications, which were then integrated to produce the recharge layer.
Aquifer Media (A): Well log data were analyzed to characterize aquifer lithology. Each lithological unit was classified, and the results were transformed into a raster map in GIS.
Soil Media (S): Soil data from well logs and maps provided by the Department of Natural Resources were used. The characteristics of soils up to 2 m depth were classified and mapped in GIS.
Unsaturated Zone Media (I): This layer, located between soil and aquifer, was derived from well log data. The lithological composition and grain size were analyzed, and interpolation was used to generalize the results across the study area.
Topography (T): Slope information was extracted from 1:50,000 topographic maps and 30 m DEMs, and classified in ArcGIS.
Hydraulic Conductivity (C): Hydraulic conductivity was derived from transmissivity and saturated thickness data of the aquifer. The data were transferred into GIS and interpolated to generate the final map.
Land Use (L): For the Composite DRASTIC model, land use information was incorporated, as groundwater vulnerability is strongly influenced by human activities. Land use maps were prepared using remote sensing and GIS techniques.
Finally, a sensitivity analysis was conducted to evaluate the influence of each parameter on the vulnerability results. Two approaches were used: map removal and single-parameter analysis. This process enabled the identification of the most influential factors in controlling groundwater vulnerability within the Jiroft Plain aquifer.
Findings
Depth to Water Table (D): According to the depth map and ranking results, the depth to the groundwater table in the Jiroft Plain ranged from 65 m in the eastern part to 4 m in the central part of the aquifer. The majority of the aquifer area had depths greater than 30 m, which indicates low vulnerability to contamination.
Net Recharge (R): Based on the Piscopo method, net recharge in the aquifer was classified into two categories: classes 3 and 5. In terms of areal extent, 74.11% of the study area was ranked as class 5, while 25.88% fell into class 3.
Aquifer Media (A): The aquifer media were categorized into five classes: 4, 6, 7, 8, and 10. The dominant classes were 6, 7, and 8, covering most of the aquifer area. Class 10 represented the highest contamination potential, whereas class 4 indicated the lowest.
Soil Media (S): The soil media of the Jiroft aquifer were divided into four classes: 1, 2, 4, and 8. Approximately 84.22% of the aquifer area fell within class 4. Class 8 indicated the highest contamination potential, while class 1 indicated the lowest.
Topography (T): Topographic slope is inversely related to contamination potential. Slope was classified into two categories: classes 9 and 10. Class 9 covered a smaller area, while class 10 indicated higher contamination potential.
Unsaturated Zone Media (I): The unsaturated zone was divided into five classes (4, 5, 6, 7, and 8). Class 8 indicated the highest potential for contamination, while a large portion of the aquifer area fell within classes 4 and 5.
Hydraulic Conductivity (C): Hydraulic conductivity, which depends on soil texture and aquifer lithology, ranged from 0.8 m/day to 28.5 m/day. The aquifer was classified into three classes: 1, 2, and 4.
Land Use (L): The land use map of the aquifer area. Three main land use types were identified. Barren lands dominated the region, covering 61.45% of the aquifer, followed by orchards (13.38%) and built-up areas (0.41%).
DRASTIC Vulnerability Index: The DRASTIC index values ranged between 82 and 128. The aquifer was classified into two vulnerability categories: low and moderate. Approximately 8.04% of the area fell within the low vulnerability class, while 91.96% was in the moderate category.
Composite DRASTIC (CD) Vulnerability Index: The land use layer was incorporated into the Composite DRASTIC model. The CD index values ranged from 109 to 158, categorized into low and moderate vulnerability. The majority of the area (99.13%) fell within the low class, while only 0.86% was classified as moderate.
Sensitivity Analysis (DRASTIC).
Discussion
Groundwater resources are among the most important sources of water in arid and semi-arid regions; therefore, protecting these resources against contamination is a crucial issue. In this regard, groundwater vulnerability maps serve as valuable tools for aquifer protection and for assessing the potential risk of pollution. Considering the hydrogeological and hydrological characteristics of the Jiroft Plain aquifer, its vulnerability to contaminants was spatially assessed.
The results of this study are consistent with those of Mfonka et al. (2018) in the Foumban area, both identifying the unsaturated zone as the most influential factor controlling aquifer vulnerability. However, in a different study, Colins et al. (2016) report that depth to groundwater was the most important factor influencing the vulnerability of groundwater resources in the Kudagnar Basin, southern India.
The findings of this research showed that the DRASTIC index for the Jiroft aquifer ranged from 82 to 128, placing the aquifer within two vulnerability classes: low and moderate. Similarly, Pourkhosravani et al. (2021) have reported that in the Sirjan aquifer, 30% of the area was classified as low vulnerability, while 69% fell into the moderate class. In the Baghin–Kerman aquifer, Malakootian and Nozari (2019) have found that 25% of the aquifer was categorized as very low vulnerability, 38% as low, 26% as moderate, 8% as high, and 2% as very high.
Moreover, the findings of this study revealed that the proportion of the aquifer classified as low vulnerability increased when applying the Composite DRASTIC model compared to the standard DRASTIC index—from 8.04% to 99.13%. This can be explained by the purpose of the Composite DRASTIC model, which is to evaluate the likelihood of nitrate contamination by incorporating land use as an additional parameter into the hydrogeological framework of the DRASTIC model. The model primarily assesses the potential risks posed by extensive land use practices on groundwater quality.
In the case of the Jiroft Plain aquifer, a large portion of the area is covered by agricultural land. Unfortunately, in recent years, the widespread use of chemical fertilizers by local farmers has contributed to nitrate leaching into groundwater through return flows from irrigated fields. This highlights the urgent need for sustainable agricultural practices and effective management strategies in the region. Therefore, it is recommended to adopt modern irrigation systems, restrict over-extraction of groundwater resources, and prevent the establishment of polluting industrial units in areas with high contamination potential.
Conclusion
The overall vulnerability of the Jiroft plain aquifer to groundwater contamination is moderate, with the unsaturated zone identified as the most influential parameter in assessing the aquifer’s vulnerability.
Acknowledgments: The authors gratefully acknowledge the Regional Water Authority of Kerman Province for their cooperation in data collection.
Ethical Permission: No issues were reported by the authors.
Conflict of Interest: The authors affirm full compliance with publication ethics, including the avoidance of plagiarism, research misconduct, data fabrication, and duplicate submission or publication. They declare no commercial interests in relation to this work and confirm that no financial compensation was received for submitting their manuscript. The authors also state that this work has not been published elsewhere and has not been simultaneously submitted to another journal.
Authors’ Contributions: Pourkhosravani M (First Author), Methodologist/Principal Researcher/Discussion Writer (33%); Nezhadafzali K (Second Author), Introduction Writer/Principal Researcher (33%), Jamshidi Gohari F (Third Author): Methodologist/Principal Researcher/Statistical Analyst/Discussion Writer (34%)
Funding: No funding was reported by the authors.
Groundwater resources are among the most essential and vital water supplies across large parts of the world, particularly in arid and semi-arid regions. Unfortunately, due to the rapid growth of population and increasing water demands, the consequent over-extraction of groundwater, the rising use of fertilizers, herbicides, and pesticides to enhance food production, the expansion of urbanization, and the rapid growth of industries without the establishment of proper wastewater disposal and treatment systems and, consequently, the excessive discharge of domestic, industrial, and agricultural effluents into aquifers the quality of these valuable resources has been deteriorating and is increasingly at risk of contamination [Nadiri et al., 2018]. Unlike surface water pollution, groundwater contamination poses even greater concerns since the detection of pollutants in groundwater is challenging. Furthermore, due to the weak self-purification capacity of groundwater, the removal of contaminants and the rehabilitation of aquifers are significantly more complex, requiring substantial time, enormous financial investment, and advanced facilities. Consequently, the pollution generated may persist for years, decades, or even centuries.
In this regard, the U.S. National Research Council (1993) defines vulnerability as the tendency or likelihood of contaminants introduced above the aquifer surface to reach a specific location within the groundwater system [Wang et al., 2007]. The preparation of vulnerability maps, or in other words, vulnerability zoning, is considered a powerful yet cost-effective method to identify aquifers requiring priority monitoring and protection, as well as to select safe zones for urban, agricultural, and industrial development that would inflict minimal damage on groundwater resources. Such maps assist planners and decision-makers in groundwater protection. Vulnerability mapping is based on the premise that some regions are more susceptible to groundwater pollution than others [Moratalla et al., 2011]. Practically, vulnerability maps can be used to localize potentially hazardous activities in areas where aquifers are less threatened. Simultaneously, they identify sensitive aquifers that require enhanced protection and thus facilitate urgent monitoring and remediation measures [Bhuvaneswaran & Ganesh, 2019; Hasan et al., 2019].
To assess the potential vulnerability of aquifers and develop vulnerability maps, multiple methods exist, generally classified into three groups: process-based or simulation methods, statistical methods, and overlay and index methods [Javadi et al., 2011]. Among these, the DRASTIC and Composite DRASTIC models are the most common, widely recognized, and frequently applied index-overlay models, employed extensively by researchers to estimate groundwater vulnerability. For example, Neshat et al. apply the DRASTIC method and concluded that the southern and southeastern parts of the study area in Kerman require considerable protection against contamination [Neshat et al., 2014]. The findings of Sinha et al. have indicated that out of a total study area of 4,191 km², approximately 8, 197, 2,730, 1,229, and 27 km² were categorized into very low, low, moderate, high, and very high vulnerability classes, respectively [Sinha et al., 2016]. Similarly, Neshat and Pradhan have found that the DRASTIC model demonstrates high accuracy in assessing groundwater vulnerability to contamination [Neshat & Pradhan, 2017]. Hao et al. report that the presence of both vulnerable areas with good and poor water quality highlights discrepancies between vulnerability indices and actual groundwater quality [Hao et al., 2017]. Mfonka et al., through the application of the DRASTIC model to assess the aquifer in Foumban, have concluded that the study area was weak to moderately vulnerable [Mfonka et al., 2018]. Kadkhodaie Ilkhchi et al. show that in evaluating the vulnerability of the Shabestar Plain aquifer, the higher determination coefficients of the Syntax, Wilcox, and Genetic Algorithm models, compared to optimized DRASTIC models, indicate their superior performance in the study region [Kadkhodaie Ilkhchi et al., 2019]. Moreover, the vulnerability assessment of the Shandiz–Torqabeh aquifer using DRASTIC revealed that river flow from the northeastern part of the basin facilitates greater recharge, potentially transporting fertilizers from surrounding farmlands into groundwater, thereby increasing aquifer vulnerability. Very low and low vulnerability zones were also identified in the western and central parts of the basin [Amiri et al., 2020]. Likewise, Bera et al. have categorized groundwater vulnerability into five classes, very high, high, moderate, low, and very low, using the DRASTIC index [Bera et al., 2021].
Groundwater resources constitute one of the primary water supplies in Jiroft County, located in the south of Kerman Province, and are utilized for both drinking and agricultural purposes. Therefore, considering the significance of groundwater in providing water for drinking, agricultural, and industrial uses in the study area, the present research was conducted with the objective of evaluating the vulnerability of the Jiroft aquifer using the DRASTIC and Composite DRASTIC models.
Methodology
The present study employed a descriptive–analytical approach and was conducted in the Jiroft Plain aquifer, located in the south of Kerman Province, in 2022. The region is characterized by hot and relatively humid summers and short mild winters. The mean annual precipitation is approximately 170 mm, and the elevation of the plain ranges between 400 and 500 m above sea level. The aquifer, with an area of about 1,400.95 km², lies between longitudes 57°33′–58°09′ and latitudes 28°11′–28°51′.
To evaluate the aquifer vulnerability to contamination, two models were applied: DRASTIC and Composite DRASTIC. The analysis considered seven parameters: depth to water table, net recharge, aquifer media, unsaturated zone media, soil media, topography, and hydraulic conductivity. In the Composite DRASTIC model, land use was added as an additional factor to better reflect the influence of anthropogenic activities. All parameters were processed and mapped using ArcGIS 10.3, and interpolation was carried out with the Inverse Distance Weighted (IDW) method.
Depth to Water Table (D): Greater depth generally reduces the vulnerability of the aquifer. To prepare this layer, data from 26 wells were obtained from the Kerman Regional Water Organization, digitized, and interpolated in GIS to produce a depth-to-water map.
Net Recharge (R): Recharge was estimated based on precipitation, slope, and soil permeability. Digital elevation models and soil maps were used to derive relevant classifications, which were then integrated to produce the recharge layer.
Aquifer Media (A): Well log data were analyzed to characterize aquifer lithology. Each lithological unit was classified, and the results were transformed into a raster map in GIS.
Soil Media (S): Soil data from well logs and maps provided by the Department of Natural Resources were used. The characteristics of soils up to 2 m depth were classified and mapped in GIS.
Unsaturated Zone Media (I): This layer, located between soil and aquifer, was derived from well log data. The lithological composition and grain size were analyzed, and interpolation was used to generalize the results across the study area.
Topography (T): Slope information was extracted from 1:50,000 topographic maps and 30 m DEMs, and classified in ArcGIS.
Hydraulic Conductivity (C): Hydraulic conductivity was derived from transmissivity and saturated thickness data of the aquifer. The data were transferred into GIS and interpolated to generate the final map.
Land Use (L): For the Composite DRASTIC model, land use information was incorporated, as groundwater vulnerability is strongly influenced by human activities. Land use maps were prepared using remote sensing and GIS techniques.
Finally, a sensitivity analysis was conducted to evaluate the influence of each parameter on the vulnerability results. Two approaches were used: map removal and single-parameter analysis. This process enabled the identification of the most influential factors in controlling groundwater vulnerability within the Jiroft Plain aquifer.
Findings
Depth to Water Table (D): According to the depth map and ranking results, the depth to the groundwater table in the Jiroft Plain ranged from 65 m in the eastern part to 4 m in the central part of the aquifer. The majority of the aquifer area had depths greater than 30 m, which indicates low vulnerability to contamination.
Net Recharge (R): Based on the Piscopo method, net recharge in the aquifer was classified into two categories: classes 3 and 5. In terms of areal extent, 74.11% of the study area was ranked as class 5, while 25.88% fell into class 3.
Aquifer Media (A): The aquifer media were categorized into five classes: 4, 6, 7, 8, and 10. The dominant classes were 6, 7, and 8, covering most of the aquifer area. Class 10 represented the highest contamination potential, whereas class 4 indicated the lowest.
Soil Media (S): The soil media of the Jiroft aquifer were divided into four classes: 1, 2, 4, and 8. Approximately 84.22% of the aquifer area fell within class 4. Class 8 indicated the highest contamination potential, while class 1 indicated the lowest.
Topography (T): Topographic slope is inversely related to contamination potential. Slope was classified into two categories: classes 9 and 10. Class 9 covered a smaller area, while class 10 indicated higher contamination potential.
Unsaturated Zone Media (I): The unsaturated zone was divided into five classes (4, 5, 6, 7, and 8). Class 8 indicated the highest potential for contamination, while a large portion of the aquifer area fell within classes 4 and 5.
Hydraulic Conductivity (C): Hydraulic conductivity, which depends on soil texture and aquifer lithology, ranged from 0.8 m/day to 28.5 m/day. The aquifer was classified into three classes: 1, 2, and 4.
Land Use (L): The land use map of the aquifer area. Three main land use types were identified. Barren lands dominated the region, covering 61.45% of the aquifer, followed by orchards (13.38%) and built-up areas (0.41%).
DRASTIC Vulnerability Index: The DRASTIC index values ranged between 82 and 128. The aquifer was classified into two vulnerability categories: low and moderate. Approximately 8.04% of the area fell within the low vulnerability class, while 91.96% was in the moderate category.
Composite DRASTIC (CD) Vulnerability Index: The land use layer was incorporated into the Composite DRASTIC model. The CD index values ranged from 109 to 158, categorized into low and moderate vulnerability. The majority of the area (99.13%) fell within the low class, while only 0.86% was classified as moderate.
Sensitivity Analysis (DRASTIC).
- Map removal method: Results showed that the unsaturated zone was the most influential parameter, with an average variation index of 1.65. This was followed by depth to water table, hydraulic conductivity, soil media, aquifer media, and topography. Net recharge exhibited the lowest sensitivity (0.63).
- Single-parameter method: The effective weights of the parameters did not fully match their theoretical weights. The unsaturated zone again emerged as the most influential factor, with an average effective weight exceeding its theoretical weight. It was followed by aquifer media, net recharge, depth to water table, topography, and soil media. Hydraulic conductivity had the least influence.
- Map removal method: Land use was the most influential parameter, with an average variation index of 1.20. Depth to water table and hydraulic conductivity also showed notable influence.
- Single-parameter method: That land use was the dominant parameter (average effective weight: 20.93%), exceeding its theoretical weight. Effective weights of aquifer media, topography, and unsaturated zone were also higher than their theoretical values, while depth to water table, net recharge, soil media, and hydraulic conductivity showed lower values
Discussion
Groundwater resources are among the most important sources of water in arid and semi-arid regions; therefore, protecting these resources against contamination is a crucial issue. In this regard, groundwater vulnerability maps serve as valuable tools for aquifer protection and for assessing the potential risk of pollution. Considering the hydrogeological and hydrological characteristics of the Jiroft Plain aquifer, its vulnerability to contaminants was spatially assessed.
The results of this study are consistent with those of Mfonka et al. (2018) in the Foumban area, both identifying the unsaturated zone as the most influential factor controlling aquifer vulnerability. However, in a different study, Colins et al. (2016) report that depth to groundwater was the most important factor influencing the vulnerability of groundwater resources in the Kudagnar Basin, southern India.
The findings of this research showed that the DRASTIC index for the Jiroft aquifer ranged from 82 to 128, placing the aquifer within two vulnerability classes: low and moderate. Similarly, Pourkhosravani et al. (2021) have reported that in the Sirjan aquifer, 30% of the area was classified as low vulnerability, while 69% fell into the moderate class. In the Baghin–Kerman aquifer, Malakootian and Nozari (2019) have found that 25% of the aquifer was categorized as very low vulnerability, 38% as low, 26% as moderate, 8% as high, and 2% as very high.
Moreover, the findings of this study revealed that the proportion of the aquifer classified as low vulnerability increased when applying the Composite DRASTIC model compared to the standard DRASTIC index—from 8.04% to 99.13%. This can be explained by the purpose of the Composite DRASTIC model, which is to evaluate the likelihood of nitrate contamination by incorporating land use as an additional parameter into the hydrogeological framework of the DRASTIC model. The model primarily assesses the potential risks posed by extensive land use practices on groundwater quality.
In the case of the Jiroft Plain aquifer, a large portion of the area is covered by agricultural land. Unfortunately, in recent years, the widespread use of chemical fertilizers by local farmers has contributed to nitrate leaching into groundwater through return flows from irrigated fields. This highlights the urgent need for sustainable agricultural practices and effective management strategies in the region. Therefore, it is recommended to adopt modern irrigation systems, restrict over-extraction of groundwater resources, and prevent the establishment of polluting industrial units in areas with high contamination potential.
Conclusion
The overall vulnerability of the Jiroft plain aquifer to groundwater contamination is moderate, with the unsaturated zone identified as the most influential parameter in assessing the aquifer’s vulnerability.
Acknowledgments: The authors gratefully acknowledge the Regional Water Authority of Kerman Province for their cooperation in data collection.
Ethical Permission: No issues were reported by the authors.
Conflict of Interest: The authors affirm full compliance with publication ethics, including the avoidance of plagiarism, research misconduct, data fabrication, and duplicate submission or publication. They declare no commercial interests in relation to this work and confirm that no financial compensation was received for submitting their manuscript. The authors also state that this work has not been published elsewhere and has not been simultaneously submitted to another journal.
Authors’ Contributions: Pourkhosravani M (First Author), Methodologist/Principal Researcher/Discussion Writer (33%); Nezhadafzali K (Second Author), Introduction Writer/Principal Researcher (33%), Jamshidi Gohari F (Third Author): Methodologist/Principal Researcher/Statistical Analyst/Discussion Writer (34%)
Funding: No funding was reported by the authors.
Keywords:
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