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Mathematical Geosciences

, Volume 48, Issue 1, pp 25–44 | Cite as

A Geostatistical Methodology to Evaluate the Performance of Groundwater Quality Monitoring Networks Using a Vulnerability Index

  • Hugo Júnez-Ferreira
  • Julián González
  • Emmanuel Reyes
  • Graciela S. Herrera
Special Issue

Abstract

A geostatistics-based methodology is proposed to evaluate existing groundwater quality monitoring networks by considering the spatial correlation of various physicochemical parameters and the aquifer vulnerability index simultaneously, using the weighted normalized estimate error variance of all variables as the optimization criterion to be minimized. The DRASTIC method was chosen to determine the vulnerability index. The methodology requires a covariance matrix for each variable that is obtained from a geostatistical analysis of the corresponding data. Each matrix is normalized to give the same initial weight to each parameter, whereas different weights can be specified later during the optimization process, depending on the monitoring goals. The vulnerability index is used in the evaluation to include areas within the aquifer that are highly susceptible to contamination. Two optimization strategies are presented. In the first strategy, the vulnerability index is included as an additional variable during the optimization process and more weight is assigned to this variable than to the others. In the second strategy, the optimization process seeks to minimize the total weighted variance, prioritizing the areas with the highest vulnerability index values. For the estimation, the static Kalman filter, which requires an initial estimate, was chosen and its error covariance matrix for each variable is involved in the evaluation. Employing successive-inclusions optimization, the contribution of each monitoring well in reducing the estimate error variance for all parameters at predefined estimation points is evaluated and those that reduce the variance the most are retained in the optimal monitoring network.

Keywords

Optimal monitoring Kalman filter Successive inclusions Redundancy 

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Copyright information

© International Association for Mathematical Geosciences 2015

Authors and Affiliations

  • Hugo Júnez-Ferreira
    • 1
  • Julián González
    • 1
  • Emmanuel Reyes
    • 1
  • Graciela S. Herrera
    • 2
  1. 1.Maestría en Ingeniería AplicadaUniversidad Autónoma de ZacatecasZacatecasMexico
  2. 2.Instituto de GeofísicaUniversidad Nacional Autónoma de México, Ciudad UniversitariaMexico CityMexico

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