A comparative analysis on groundwater vulnerability models-fuzzy DRASTIC and fuzzy DRASTIC-L

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.

Geostatistical estimates of groundwater nitrate-nitrogen concentrations with spatial auxiliary information on DRASTIC-LU-based aquifer contamination vulnerability

Groundwater nitrate-nitrogen contamination typically involves several natural and anthropogenic factors, including those related to hydrology, hydrogeology, topography, and land use (LU). DRASTIC-LU-based aquifer contamination vulnerability could be used to characterize the pollution potentials of groundwater nitrate-nitrogen and to determine groundwater protection zones. This study used regression kriging (RK) with environmental auxiliary information on DRASTIC-LU-based aquifer contamination vulnerability to investigate groundwater nitrate-nitrogen pollution in the Pingtung Plain of Taiwan. First, the relationship between groundwater nitrate-nitrogen pollution and assessments of aquifer contamination vulnerability was determined using stepwise multivariate linear regression (MLR). Subsequently, the residuals between the nitrate-nitrogen observations and MLR predictions were estimated by kriging techniques. Finally, the groundwater nitrate-nitrogen distributions were spatially analyzed using RK, ordinary kriging (OK), and MLR. The findings indicated that the land used for orchards and the medium- and coarse-sand fractions of vadose zones were associated with groundwater nitrate-nitrogen concentrations. The fertilizer used for orchards was identified as the primary source of groundwater nitrate-nitrogen pollution. The RK estimates could be used to analyze the characteristics of the pollution source for land used for orchards and exhibited high spatial variability and accuracy after residual correction. Moreover, RK had an excellent estimate ability for extreme data compared to MLR and OK. Correctly determining groundwater nitrate-nitrogen distributions using RK was useful for administering environmental resources and preventing public health hazards.

Assessment and validation of groundwater vulnerability to nitrate and TDS using based on a modified DRASTIC model: a case study in the Erbil Central Sub-Basin, Iraq

In recent years, groundwater vulnerability assessment has become a crucial step in effectively protecting groundwater resources against increasing groundwater pollution in recent years. Sustainable effectual management of groundwater sources in terms of quality has become a critical factor in the development of unplanned urbanization areas, especially in regions with intensive agricultural and industrial activities in the land use/land cover (LULC) models. In this study, the GIS-based DRASTIC model was used by modified to estimate the groundwater vulnerability of porous aquifers to nitrate and total dissolved solids (TDS). The DRASTIC and the modified DRASTIC models generate four different groundwater vulnerability zones: high (33.6, 37.8%), moderate (45.9, 42.3%), low (18.7, 18.3%), and very low (1.8,1.6%). DRASTIC_LULC index map provides four different vulnerability zones: low, moderate, high, and very high, covering 0.1%, 7.6%, 83.6%, and 8.7% of the Erbil Central Sub-Basin, respectively. The most important hydrogeological factors determining the DRASTIC vulnerability obtained from sensitivity analyses are depth to the water table and impact of vadose zone parameters with average effective weight values of 23.7% and 22.6%. For validating the DRASTIC_LULC model, the water quality parameters, nitrate and TDS, have been used with an accuracy of 68% and 79%, which indicates that the validation accuracy for this model is quite high. Maps obtained as a result of this study can be used to create a baseline map for the sustainable management of groundwater quality in vulnerable areas of the Erbil Central Sub-Basin and its planning.

Comparative assessment of groundwater vulnerability using GIS-based DRASTIC and DRASTIC-AHP for Thoothukudi District, Tamil Nadu India

The groundwater is very precious in the world. Rapid urbanization and industrialization create tremendous stress on groundwater quality and quantity. Unscientific groundwater extraction and waste disposal methods impact the groundwater aquifer's susceptibility in the coastal area. This research examines how industrial waste, seawater intrusion, and solid waste dumping affect the Thoothukudi District, located on the southwest coast of Tamil Nadu, India. The groundwater vulnerability potential is determined using the DRASTIC and analytical hierarchy process (AHP)-based DRASTIC model. DRASTIC-AHP method's weights and ranks are determined using multi-criteria decision analysis (MCDA)-based pairwise comparison method. Remote sensing (RS) and geographical information system (GIS) are implemented to prepare the input layers for DRASTIC and DRASTIC-AHP. The findings reveal a very high category of vulnerability along the coastline that is covered in sand and loose sediments from an aquifer. Similar conditions exist on the southeast side, which is covered with gravel, sand, and sandstone with shale and has relatively low-slope topography. This enables higher contaminant percolation into the groundwater and raises the possibility for pollution. The DRASTIC-AHP method's results reveal that the southeast side has a significant possibility of contamination. The water table, net recharge, vadose zone, and conductivity greatly impacted the DRASTIC vulnerability assessment due to their stronger weight than theoretical weight. It may be stated that the DRASTIC technique is more cost-effective and time-efficient in analyzing a wide range of regional groundwater risks while avoiding sloppy, uncontrolled land development and other unwanted activities.

Integration of Electrical Resistivity and Modified DRASTIC Model to Assess Groundwater Vulnerability in the Surrounding Area of Hulene-B Waste Dump, Maputo, Mozambique

In this study, electrical resistivity was applied in six 400 m profiles around the Hulene-B waste dump (Mozambique). Afterwards, an inversion was performed by RES2Dinv. The use of the electrical resistivity method allowed us to characterize in detail some underlying aspects of the DRASTIC index by identifying anomalous zones considered to be permeable and prone to leachate migration. The modified DRASTIC index revealed high values in areas near contaminated surface groundwater and surface layers of the vadose zone, characterized by low resistivities. Areas with lower index results were characterized by high resistivity on surface layers and high depth at which groundwater was detected. The overall modified DRASTIC index result revealed medium vulnerability. However, high vulnerability index values were detected in areas with higher surface elevation, suggesting groundwater contamination by horizontal dilution of leachates from the surrounding area of the Hulene-B waste dump.