title: A geostatistical methodology for the optimal design of space-time hydraulic head monitoring networks and its application to the Valle de Querétaro aquifer.
abstract: This paper presents a new methodology for the optimal design of space-time hydraulic head monitoring networks and its application to the Valle de Querétaro aquifer in Mexico. The selection of the space-time monitoring points is done using a static Kalman filter combined with a sequential optimization method. The Kalman filter requires as input a space-time covariance matrix, which is derived from a geostatistical analysis. A sequential optimization method that selects the space-time point that minimizes a function of the variance, in each step, is used. We demonstrate the methodology applying it to the redesign of the hydraulic head monitoring network of the Valle de Querétaro aquifer with the objective of selecting from a set of monitoring positions and times, those that minimize the spatiotemporal redundancy. The database for the geostatistical space-time analysis corresponds to information of 273 wells located within the aquifer for the period 1970-2007. A total of 1,435 hydraulic head data were used to construct the experimental space-time variogram. The results show that from the existing monitoring program that consists of 418 space-time monitoring points, only 178 are not redundant. The implied reduction of monitoring costs was possible because the proposed method is successful in propagating information in space and time.
authors.affiliation: Maestría en Ingeniería Aplicada, Universidad Autónoma de Zacatecas, Av Ramón López Velarde No 801, Carretera a la Bufa, CP 98010 Zacatecas, Mexico.
authors.forename: H E
authors.initials: HE
authors.lastname: Júnez-Ferreira
authors.forename: G S
authors.initials: GS
authors.lastname: Herrera
mesh_terms: D004784:Environmental Monitoring / Q000379:methods*; D060587:Groundwater / Q000737:chemistry*; D008800:Mexico; D062211:Spatio-Temporal Analysis; D013223:Statistics as Topic; D014872:Water Movements; D014877:Water Pollution, Chemical / Q000032:analysis / Q000706:statistics & numerical data*