A Geostatistical Methodology to Evaluate the Performance of Groundwater Quality Monitoring Networks Using a Vulnerabilit
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A Geostatistical Methodology to Evaluate the Performance of Groundwater Quality Monitoring Networks Using a Vulnerability Index Hugo Júnez-Ferreira1 · Julián González1 · Emmanuel Reyes1 · Graciela S. Herrera2
Received: 6 October 2014 / Accepted: 6 August 2015 © International Association for Mathematical Geosciences 2015
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 val-
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Hugo Júnez-Ferreira [email protected] Julián González [email protected] Emmanuel Reyes [email protected] Graciela S. Herrera [email protected]
1
Maestría en Ingeniería Aplicada, Universidad Autónoma de Zacatecas, Av. Ramón López Velarde 801, Col. Centro, C.P. 98010 Zacatecas, Mexico
2
Instituto de Geofísica, Universidad Nacional Autónoma de México, Ciudad Universitaria, Del. Coyoacán, C.P. 04510 Mexico City, Mexico
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Math Geosci
ues. 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
1 Introduction Groundwater monitoring serves a highly important role in supporting management strategies and operational conservation policies for aquifers. The implementation of programs for monitoring groundwater quantity and quality helps to improve the planning, development, protection and management of groundwater, by anticipating or controlling contamination and overexploitation problems. Understanding water quality and its evolution, owing
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