GIS-statically-based modelling the groundwater quality assessment coupled with soil and terrain attributes data

Area of Science:

• Environmental Science
• Hydrogeology
• Geospatial Analysis

Background:

• Groundwater quality assessment is crucial for sustainable resource management and environmental planning.
• Integrating diverse datasets is essential for comprehensive spatial analysis of groundwater resources.

Purpose of the Study:

• To apply Geographic Information Systems (GIS) for groundwater quality assessment.
• To identify key soil parameters and topographical attributes influencing groundwater quality.
• To evaluate geostatistics and GIS for spatial analysis of groundwater resources.

Main Methods:

• Collected groundwater and surface soil samples from 43 agricultural wells.
• Utilized multiple linear regression (MLR) and principal component regression (PCR) models.
• Integrated soil properties, topographical attributes (e.g., TWI, slope), and geostatistics within a GIS framework.

Main Results:

• Significant correlations found between groundwater electrical conductivity (GEC) and soil electrical conductivity (EC, r=0.89) and soil carbonate (CaCO3, r=0.68).
• Terrain wetness index (TWI) and slope were the most influential topographical attributes on GEC.
• The MLR model (R2=0.89, RMSE=150 μScm-1) outperformed PCR (R2=0.85, RMSE=170 μScm-1) for GEC prediction.

Conclusions:

• Combining statistical modeling, geostatistics, and GIS provides an effective approach for groundwater quality assessment.
• The developed GEC map reveals significant spatial variations, aiding in targeted resource management.
• This integrated methodology offers valuable insights for environmental planning and groundwater protection.