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<p>In hyper-arid region where precipitation and surface water is scarce, groundwater (GW) is the primary source. This study examined the GW quality and the spatio-temporal variability in the Balotra district of the Luni River Basin, western Rajasthan (India), a hyper-arid region with a &lt; 100&#xa0;mm/year rainfall. The statistical analyses, namely Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA), were used to evaluate 35 pre-monsoon and post-monsoon GW samples to identify the dominant hydrochemical characteristics and delineate the contamination extent. We integrated these hydrochemical observations with hydrodynamic models, the GLOBal Groundwater Model (GLOBGM) and ERA5-Land reanalysis subsurface runoff to formulate linkages between the GW quality and aquifer processes. Steady-state simulations showed long-term equilibrium (Annual recharge: 18.4&#xa0;mm/yr; abstraction: 14.7&#xa0;mm/yr; 80% utilization rate) in the aquifer, whereas transient simulations showed seasonal changes in the water table. The model performed acceptably (RMSE = 4.7&#xa0;m, NSE = 0.68), confirming the reliability of simulated hydraulic heads. The integrated GLOBGM hydrodynamic-multivariate model showed that short-term recharge-discharge fluctuations play a major role in the spatiotemporal variability of GW quality. It revealed that despite monsoon recharge, post-monsoon EC increased paradoxically by 9.9%, attributed to vadose zone salt mobilization. These findings support a three-cluster spatial zoning approach and an 80% abstraction-to-recharge threshold for tiered GW management. PCA indicated five major components that explain 79.66% of the total variance, with geogenic processes (16.38%) and industrial contamination (37.46%) as the key drivers of quality. With a Silhouette coefficient of 0.61, HCA identified three different hydrochemical zones which are characterised by different levels of contamination. The key contribution of this work is integration of large-scale hydrodynamic modelling and multivariate hydrochemical analysis for impact quantification of GW quality transient hydraulic conditions and comprehensive sustainable management.</p>

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Integrating GLOBGM hydrodynamic modeling and multivariate analysis for groundwater quality assessment in hyper-arid regions

  • 期刊论文

  • 发表日期：

    2026年7月6日

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  • Sudheer Mathur,
  • Ajit Pratap Singh,
  • Suresh Kumar Singh,
  • Kunal Dhadse,
  • Shriya Singh

摘要

In hyper-arid region where precipitation and surface water is scarce, groundwater (GW) is the primary source. This study examined the GW quality and the spatio-temporal variability in the Balotra district of the Luni River Basin, western Rajasthan (India), a hyper-arid region with a < 100 mm/year rainfall. The statistical analyses, namely Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA), were used to evaluate 35 pre-monsoon and post-monsoon GW samples to identify the dominant hydrochemical characteristics and delineate the contamination extent. We integrated these hydrochemical observations with hydrodynamic models, the GLOBal Groundwater Model (GLOBGM) and ERA5-Land reanalysis subsurface runoff to formulate linkages between the GW quality and aquifer processes. Steady-state simulations showed long-term equilibrium (Annual recharge: 18.4 mm/yr; abstraction: 14.7 mm/yr; 80% utilization rate) in the aquifer, whereas transient simulations showed seasonal changes in the water table. The model performed acceptably (RMSE = 4.7 m, NSE = 0.68), confirming the reliability of simulated hydraulic heads. The integrated GLOBGM hydrodynamic-multivariate model showed that short-term recharge-discharge fluctuations play a major role in the spatiotemporal variability of GW quality. It revealed that despite monsoon recharge, post-monsoon EC increased paradoxically by 9.9%, attributed to vadose zone salt mobilization. These findings support a three-cluster spatial zoning approach and an 80% abstraction-to-recharge threshold for tiered GW management. PCA indicated five major components that explain 79.66% of the total variance, with geogenic processes (16.38%) and industrial contamination (37.46%) as the key drivers of quality. With a Silhouette coefficient of 0.61, HCA identified three different hydrochemical zones which are characterised by different levels of contamination. The key contribution of this work is integration of large-scale hydrodynamic modelling and multivariate hydrochemical analysis for impact quantification of GW quality transient hydraulic conditions and comprehensive sustainable management.