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Space‐time optimization of groundwater quality sampling networks
Author(s) -
Herrera Graciela S.,
Pinder George F.
Publication year - 2005
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2004wr003626
Subject(s) - sampling (signal processing) , kalman filter , redundancy (engineering) , computer science , mathematical optimization , groundwater , algorithm , stochastic simulation , filter (signal processing) , mathematics , statistics , engineering , artificial intelligence , geotechnical engineering , computer vision , operating system
A method is herein proposed and demonstrated for the space‐time optimization of monitoring networks for groundwater quality. The objective is to provide a minimum cost design algorithm that will assist in determining where and when to sample existing wells dedicated to defining, through periodic sampling, the behavior of groundwater contaminants. The method has three characteristics: (1) The combined spatial and temporal redundancy of the sampling network is considered by using a Kalman filter coupled with a stochastic transport model in which velocity and dispersion are spatially correlated random fields. (2) The sampling network is optimized using an enumerative method that minimizes a function of the estimate variance. (3) A real‐time update of the estimate is obtained using again the Kalman filter. The synthetic examples presented show that for a contaminant plume in motion this method can obtain cost‐effective sampling networks.