An integrated DRASTIC model using frequency ratio and two new hybrid methods for groundwater vulnerability assessment

Groundwater management can be effectively implemented by mapping groundwater contamination. Intense agricultural activities and land overexploitation have resulted in groundwater contamination, which is becoming a critical issue, specifically in areas where fertilizers are extensively used on large plantations. The goal of this study was to develop an integrated DRASTIC model with a frequency ratio (FR) as a novel approach. Two new hybrid methods namely single-parameter sensitivity analysis (SPSA) and an analytical hierarchy process (AHP) are also implemented for adjusting feature weights to local settings. The FR is used for DRASTIC model rates, whereas both SPSA and AHP are used for DRASTIC weights. The FR-DRASTIC, FR-SPSA and FR-AHP methods are developed; nitrate samples from the same month in different years are used for analysis and correlation (May 2010 and May 2012). The first nitrate samples are interpolated using the Kriging approach. The Kerman plain is used as an example, which is located in south-eastern part of Iran. Additionally, the new methods are employed in the study area to compare with each other and the original DRASTIC model. The validation results exhibited that using FR approach improved the correlation between vulnerability index and nitrate concentrations compared with original DRASTIC vulnerability correlation which was 0.37. The results indicated that the new hybrid methods exhibited higher correlation 0.75 in the FR-DRASTIC model. Correlations of the FR-SPSA and FR-AHP approaches were 0.77 and 0.80. Hence, the new hybrid methods are more effective and provide reasonably good results. Furthermore, quantitative measures of vulnerability offer an excellent opportunity to effectively prevent as well as reduce contamination

Risk assessment of groundwater pollution with a new methodological framework: application of Dempster-Shafer theory and GIS

Managing natural groundwater resources is challenged by nitrate pollution resulting from agricultural activities. This issue is emerging as an important environmental concern that needs to be addressed through effective groundwater management. Groundwater assessment is an important aspect of groundwater management, particularly in arid and semi-arid regions. This study focused on the Kerman Plain, which is exposed to intensive agricultural activities and land exploitation that result in intense land pollution. The effects of nitrate pollution may be controlled by applying specific measures. Dempster–Shafer theory (DST) was applied in this study to develop a new methodology for assessing pollution risk. Applying this theory as a pioneering approach to assessing groundwater pollution risk is the novel component of this research. This approach provides a major advantage by dealing with varying levels of precision related to information. The spatial association between DRASTIC parameters including D (depth of water), R (net recharge), A (aquifer media), S (soil media), T (topography), I (impact of vadose zone) and C (hydraulic conductivity) and underground nitrate occurrence was evaluated by applying bivariate DST to assign mass functions. Dempster’s rule of combination using GIS was then applied to determine a series of combined mass functions for multiple hydrogeological data layers. The uncertainty of system responses was directly addressed by the proposed methodology. Finally, the modified DRASTIC map with the highest validity and accuracy was selected and combined with the damage map. The comparison between nitrate distribution and vulnerability and the risk maps exhibit high similarity between different vulnerability degrees and nitrate concentrations. Long-term planning of preventive measures and associated developments can be aided by the regions with low and very low risks located in the northeast, northwest, and central regions

Groundwater vulnerability assessment using an improved DRASTIC method in GIS

Groundwater management can be effectively conducted by using groundwater contamination map assessment. In this study, a modified DRASTIC approach using geographic information system (GIS) was applied to evaluate groundwater vulnerability in Kerman plain (Iran). The Wilcoxon rank-sum nonparametric statistical test was applied to modify the rates of DRASTIC. In addition, the analytic hierarchy process (AHP) method was employed to evaluate the validity of the criteria and sub criteria of all the parameters of the DRASTIC model, which proposed as an alternative treatment of the imprecision demands. The GIS offers spatial analysis in which the multi index evaluation can be effectively conducted through the AHP. The non-point source pollution was effectively determined by the modified DRASTIC method compared with the traditional method. The regression coefficient revealed the relationship between the vulnerability index and the nitrate concentration. The best result was obtained by using AHP–AHP, followed by DRASTIC–AHP, modified DRASTIC–AHP, and AHP–DRASTIC models. In this study, the DRASTIC method failed to provide satisfactory result. Additionally, by using both the original DRASTIC and the modified DRASTIC methods in the study area, AHP–AHP performed highly in the Kerman plain, suggesting that the southern and south east parts of the area considerably calls for conservation against contamination

An integrated GIS based statistical model to compute groundwater vulnerability index for decision maker in agricultural area

The conservation areas in a plain are affected by the groundwater contamination from intense application of the fertilizers. The vulnerability of groundwater can be tested by using the DRASTIC model for the pollutants. The groundwater susceptibility to pollution in the various areas is mapped through DRASTIC model. However, the effects of pollution types and its characteristics are not considered, as this model is used without any modifications. This technique must be standardized for usage in the various aquifers and specific pollution types. The rates of DRASTIC parameters are corrected to obtain the potential for a more accurate analysis of the vulnerability pollution. The relationships between the parameters are identified with respect to the nitrate concentration in the groundwater by calculating the new rates. The methodology was applied to the selected area situated in the south eastern region of Iran at Kerman plain. Twenty-seven different locations were selected to test and analyse the nitrate concentration in the water from underground wells. The pollution in the aquifer was associated and correlated with the DRASTIC index by using the measured nitrate concentrations. The relationship between the index and the measured pollution in the Kerman plain was determined by applying the Wilcoxon rank-sum nonparametric statistical tests and the rates were calculated. It was found specifically in the agricultural areas that the modified DRASTIC model performed more efficiently than the traditional method for nonpoint source pollution, as indicated by the results. After modifications, the regression coefficients revealed that the relationship between the vulnerability index and the nitrate concentration was 77 %, while it was 37 % before the modifications were used. These statistics show that the modified DRASTIC performed far more efficiently than the original version

Estimating groundwater vulnerability to pollution using a modified DRASTIC model in the Kerman agricultural area, Iran

Groundwater contamination from intensive fertilizer application affects conservation areas in a plain. The DRASTIC model can be applied in the evaluation of groundwater vulnerability to such pollution. The main purpose of using the DRASTIC model is to map groundwater susceptibility to pollution in different areas. However, this method has been used in various areas without modification, thereby disregarding the effects of pollution types and their characteristics. Thus, this technique must be standardized and be approved for applications in aquifers and particular types of pollution. In this study, the potential for the more accurate assessment of vulnerability to pollution is achieved by correcting the rates of the DRASTIC parameters. The new rates were calculated by identifying the relationships among the parameters with respect to the nitrate concentration in groundwater. The methodology was implemented in the Kerman plain in the southeastern region of Iran. The nitrate concentration in water from underground wells was tested and analyzed in 27 different locations. The measured nitrate concentrations were used to associate and correlate the pollution in the aquifer to the DRASTIC index. The Wilcoxon rank-sum nonparametric statistical test was applied to determine the relationship between the index and the measured pollution in Kerman plain. Also, the weights of the DRASTIC parameters were modified through the sensitivity analysis. Subsequently, the rates and weights were computed. The results of the study revealed that the modified DRASTIC model performs more efficiently than the traditional method for nonpoint source pollution, particularly in agricultural areas. The regression coefficients showed that the relationship between the vulnerability index and the nitrate concentration was 82 % after modification and 44 % before modification. This comparison indicated that the results of the modified DRASTIC of this region are better than those of the original method

Assessment of groundwater vulnerability and nitrate contamination risk using GIS-based drastic model with hybrid statistical and probabilistic techniques

Groundwater pollution is one of the most significant environmental problems today. It is caused by human activities, especially agricultural activities. Agricultural systemsdeveloped from traditional methods to modern applications, resulting in an overuse of chemical fertilizers that increase the amount of pollutants. Fertilizers such as nitrates play a significant role in water and soil pollution because of their special characteristics. Most of these fertilizers enter the groundwater through surplus water and create high-risk groundwater resources. Therefore, identifying and diagnosing the amount of pollutants using a groundwater risk map in the future can largely prevent more pollution in groundwater resources. Efficient preventive programs, such as risk management, should be implemented to reduce the risks of groundwater pollution. In this research, the DRASTIC approach based on a geographic information system (GIS) was applied to evaluate groundwater vulnerability in Kerman Plain (Iran), an arid and semi-arid region that encounters intensive agricultural activities and over exploitation of land that has resulted in groundwater contamination. DRASTIC model includes seven parameters of depth to water (D), net recharge (R), aquifer media (A),soil media (S), topography (T), impact of vadose zone (I), and hydraulic conductivity (C) of the Kerman Plain. The original DRASTIC model was applied and integrated using original rates and weights. The generation of groundwater vulnerability map was performed by optimizing the rates and weights of DRASTIC model using GIS modeling techniques. The models used were analytical hierarchy process (AHP),single parameter sensitivity analysis (SPSA), frequency ratio (FR), and Wilcoxon nonparametric model. The optimized rates and weights were computed using each model.The Wilcoxon non-parametric test and FR analysis were applied to optimize the rates of DRASTIC model. AHP method was also used to optimize both the rates and the weights of DRASTIC model, and sensitivity analysis was conducted to optimize only the weights of DRASTIC model. These methods were assigned to DRASTIC model and integrated to produce hybrid methods. So far, some of the generated hybrid methods using the abovementioned models have not been applied in other studies. The most proper optimization of the vulnerability map was determined using Pearson’s coefficient correlation. The Pearson’s correlation value of each modified DRASTIC model used in this study was calculated. The regression coefficients showed the relationship between each vulnerability index and the nitrate concentration. The regression coefficient of DRASTIC model indicated a correlation of 0.37. The combination of Wilcoxon non-parametric test for rates and the sensitivity analysis for weights revealed the highest correlation of 0.87 among all applied hybrid models The most appropriate groundwater vulnerability map with the highest validity and accuracy was selected and combined with the nitrate pollution map that indicates theamount of damage in the Kerman Plain. Then, Dempster–Shafer theory (DST) was applied to develop a new methodology for assessing pollution risk. DST method provides a major advantage by dealing with the varying levels of precision related to information and the more generalized form of probability theory. The combination of the damage map and the pollution occurrence probability map through DST method produces a novel method that can determine the groundwater risk map for the nitrate parameter. The application of risk assessment method is recommended if the objective is to develop a risk map of areas that are vulnerable to pollution. Aside from nitrate, other pollutants can also be identified in other regions. Therefore, analyses are urged to search for other factors that lead to the pollution of groundwater resources

The socioeconomic impact of severe droughts on agricultural lands over different provinces of Iran

The lack of rainfall is the primary cause of drought, reduced crop harvest (CH), and socioeconomic drought. Agriculture is the primary source of income for most Iranians, and drought can harm people's lives irreparably. This study examines the changes in the CHs and crop prices (CPs) across provinces of Iran during the most severe drought of all time in Iran and its impact on producers (farmers), consumers, and public prosperity using the Surplus Economic Method (SEM). Our study focused on crops that have a big impact on Iranian life, such as wheat, barley, potato, onion, tomato, lentils, chickpeas, and alfalfa. Our results indicated that Iran's most severe hydrological drought occurred from 2000 to 2002. The rainfed farms experienced the most pronounced changes in terms of CH, while financial damages were the highest in irrigated areas. Among the crops investigated, rainfed wheat has experienced the greatest reduction of 80%. Moreover, grains have the greatest price change (40% increase) during the hydrological drought. Wheat underwent the steepest CH reduction. Legumes experienced the steepest price rise. During the drought, most crops had lower yields, causing losses for consumers, but some producers still made a profit. The drought affected northwest and west Iran farmers adversely, but southern and central Iran farmers gained from the drought through increased product prices. Drought has had adverse effects on the public prosperity for most of the examined crops and reduced it. The greatest reduction corresponds to barley in the western regions and the Zagros Mountains. The diversity of crops in northwestern and western Iran has made these regions the most important areas for farming and crop supply in Iran. Agricultural droughts in these regions can affect the lives of all Iranian people and lead to socioeconomic drought in Iran. Our study demonstrated that hydrological droughts in northwestern and western areas of Iran are chiefly caused by the shortage of winter and spring rains. Moreover, Identifying the primary factors of hydrological drought showed that the hydrological drought is most affected by the depth of snow during winter. Additionally, the data analysis revealed that the combined effect of winter precipitation with snow depth and snow depth with snow coverage has the highest impact on hydrological drought (61%). The results can make farming policies based on the region and climate, marketing plans for droughts, and solutions to address the harmful effects of drought

Risk assessment of groundwater pollution using Monte Carlo approach in an agricultural region: an example from Kerman Plain, Iran

In groundwater resource management, the risk assessment of groundwater pollution is an effective tool in arid and semi-arid regions, such as Kerman Plain, Iran. In addition to risk assessment, and the mapping of damage and pollution probability occurrence is considered as a fundamental phase of protective groundwater management in agricultural regions. To characterize risk index affecting the study area, a novel approach was developed by combining both damage map which was obtained by multiplying seven hydrogeological parameters of modified DRASTIC model with pollution and probability of pollution occurrence with consideration of uncertainty. The study area is located in an agricultural land; therefore, nitrate was used as a pollution parameter. The spatial distribution of nitrate concentration in the area was investigated by ordinary kriging. In addition, Monte Carlo simulation (MCS) and normal distribution function were used to evaluate the uncertainty of this parameter and the probability of pollution occurrence in the study area. Risk assessment parameters were constructed, classified, and integrated in a GIS environment. Groundwater movement induces the transport of pollutants underground. Thus, we proposed a new methodology combining damage map and Monte Carlo simulation for probability and parameters uncertainty. The proposed method can be used to monitor pollution damage on a regional scale and ensure effective groundwater resource management for reducing the amount of pollution for future. Damage index and risk classification were compared; results indicate a high degree of similarity. The regions with low and very low risks are located in the northeast, northwest, and central parts, where further studies could be conducted for the subsequent development and long-term design of protective measures

A new approach for vulnerability assessment of coastal aquifers using combined index

A new approach, developed for coastal aquifers, consists of determination of saltwater-freshwater interface as well as obtaining map of groundwater vulnerability, for which a number of methods have been provided, including index method and numerical modelling. In the previous studies, the common vulnerability methods such as DRASTIC have not took the coastal regions into account, or that method of GALDIT, proposed for coastal regions, assumed the saltwater intrusion fixed. The vulnerability of coastal aquifer is determined in two distinct regions (coastal and non-coastal areas) using two distinct methods (Index and Numerical modelling) and finally they are combined together. The results of this study in coastal aquifer in north of Iran suggest that due to overexploitation, the intrusion of saltwater from coastline into centre of aquifer is as deep as 720 m, inducing very high vulnerability in coastal regions. The correlation of DRASTICSea method with qualitative parameter is obtained 68%

Multi-parameter risk mapping of Qazvin aquifer by classic and fuzzy clustering techniques

This study proposes a new approach to establish a multi-parameter risk mapping method by employing the K-Means clustering technique. Accordingly, spatial assessment of arsenic (As), nitrate (NO3) and total dissolved solids (TDS) were carried out based on the type of land use to estimate contamination potential in an aquifer. Since risk mapping is always associated with the occurrence probability of a phenomenon, pollution occurrence probability was then obtained using the fuzzy C-means clustering. The results reveal that NO3 and As contamination levels increase from the first cluster (C1), covers 22.3% of the aquifer, to C5 encompassing 35.1% of the aquifer devoted to extensive industrial and agricultural activities. Fuzzy clustering results show that the pollution occurrence probability in each aquifer cell varied from less than 30 to more than 90%. Moreover, the results show, industrial and agricultural land uses cover about 70% of the areas with high risk of contamination