Identifying influencing wells for gradient estimation in the confined portion of the Gulf Coast aquifer near Kingsville, TX

摘要

Hydraulic gradient is a fundamental aquifer characteristic required to estimate groundwater flow and quantify advective fluxes of pollutants. Graphical and local estimation schemes using potentiometric head information from three or four wells are used to compute hydraulic gradients but suffer from imprecision and subjectivity. The use of linear regression is recommended when hydraulic head data from a groundwater monitoring network consisting of several wells are available. In such cases, statistical influence analysis can be carried out to evaluate how each well within the network impacts the gradient estimate. A suite of five metrics, namely-(1) the hat-values, (2) studentized residuals, (3) Cook's distance, (4) DFBETAs and (5) Covariance ratio (COVRATIO) are used in this study to identify influential wells within a regional groundwater monitoring network in Kleberg County, TX. The hat-values indicated that the groundwater network was reasonably well balanced and no well exerted an undue influence on the regression. The studentized residuals and Cook's distance indicated the wells with the highest influence on the regression predictions were those that were close to high groundwater production centers or affected by coastal-aquifer interactions. However, the wells in the proximity of the production centers had the highest impact on the estimated gradient values as ascertained using DFBETAs. The covariance ratio which indicates the sensitivity of a monitoring well on the estimated standard error of regression was noted to be significant at most wells within the network. Therefore, networks seeking to address changes in groundwater gradients due to climate and anthropogenic influences need to be denser than those used to ascertain synoptic gradient estimates alone. The magnitude of the groundwater velocity was greatly underestimated when the influential wells were excluded from the network. The direction of flow was affected to a lesser extent, but a complete gradient reversal was noted when the network consisted of only four peripheral wells. The influence analysis therefore provides a valuable tool to assess the importance of individual wells within a monitoring network and can therefore be useful when existing networks are to be pruned due to fiscal constraints.