Spatiotemporal assessment of groundwater quality under climate change using multiscale clustering technique

Abstract

Assessing spatiotemporal variations of groundwater quality and identifying vulnerable areas is a crucial stage in the planning and management of water resources. This study focuses on utilizing a multiscale method to assess the water quality variables in the groundwater of Ardabil basin located in Iran. This plain is one of the important industrial and agricultural regions in Iran, and groundwater provides 89% of its total water demand. Therefore, investigating groundwater quality for this plain is indispensable. The monthly timescale datasets from 26 piezometers, covering the period of 2000–2022, were de-noised and decomposed using the Wavelet transform (WT) and Variational Mode Decomposition (VMD), respectively. The Permutation Entropy (PE) values of the subseries were computed and considered as inputs of the K-means method to zone and classify the basin in terms of the Total Dissolved Solids (TDS) and Electrical Conductivity (EC). The EC and TDS of central piezometers were predicted and the modeling uncertainty was investigated. From results, excessive use of groundwater resources resulted in a drop in groundwater levels even in rainy years. It was found that the integrated approach exhibited a desirable degree of reliability. Groundwater vulnerability assessment was done considering the hydrogeological parameters affecting groundwater pollution and using the DRASTIC approach. Nitrate values were used to validate the DRASTIC method. Matching the nitrate ion distribution map to the vulnerability map showed that the two maps corresponded, indicating that most of the points with high nitrate (21–42 mg/l) were located in areas with higher vulnerability potential (central parts).