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<p>Groundwater monitoring networks are pivotal in water resource management, aiding sustainable utilization and policy formulation. However, the costs associated with establishing and maintaining such networks are considerable, and existing data often needs to be more utilized for decision-making. This inefficiency is exacerbated by the pitfalls of improper interpolation techniques and the undue influence of densely monitored areas on estimates. In the case of Punjab State, which heavily relies on groundwater for agricultural and domestic needs, the absence of a robust configuration of observation wells and accurate spatio-temporal trend mapping presents a significant knowledge gap. This study evaluates kriging (ordinary kriging) and co-kriging geostatistical techniques, employing various semi-variogram models (exponential, Gaussian, circular, spherical, etc.), for precise spatial mapping of groundwater behavior using distinct statistical error parameters. The Gaussian model, coupled with ordinary kriging, emerged as the best geostatistical approach for interpolating hydraulic heads. The research optimized the groundwater monitoring network by utilizing standard prediction error maps, resulting in the identification of 237 new well locations and the existence of 18 redundant well sites. The study concluded that a monitoring well spacing of 7.5&#xa0;km is essential for achieving accurate groundwater mapping. These findings offer valuable insights to planners and policymakers, enhancing regional understanding of groundwater resources.</p>

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Geostatistical evaluation and network optimization for precision groundwater resource mapping and management in Punjab State, India

  • 期刊论文

  • 发表日期：

    2025年3月17日

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  • Bhavana Thakur,
  • Samanpreet Kaur,
  • V. K. Verma,
  • Asim Biswas

摘要

Groundwater monitoring networks are pivotal in water resource management, aiding sustainable utilization and policy formulation. However, the costs associated with establishing and maintaining such networks are considerable, and existing data often needs to be more utilized for decision-making. This inefficiency is exacerbated by the pitfalls of improper interpolation techniques and the undue influence of densely monitored areas on estimates. In the case of Punjab State, which heavily relies on groundwater for agricultural and domestic needs, the absence of a robust configuration of observation wells and accurate spatio-temporal trend mapping presents a significant knowledge gap. This study evaluates kriging (ordinary kriging) and co-kriging geostatistical techniques, employing various semi-variogram models (exponential, Gaussian, circular, spherical, etc.), for precise spatial mapping of groundwater behavior using distinct statistical error parameters. The Gaussian model, coupled with ordinary kriging, emerged as the best geostatistical approach for interpolating hydraulic heads. The research optimized the groundwater monitoring network by utilizing standard prediction error maps, resulting in the identification of 237 new well locations and the existence of 18 redundant well sites. The study concluded that a monitoring well spacing of 7.5 km is essential for achieving accurate groundwater mapping. These findings offer valuable insights to planners and policymakers, enhancing regional understanding of groundwater resources.