title: Local hydraulic gradient estimator analysis of long-term monitoring networks.
abstract: Three measurements of head at unique locations form a three-point estimator of the local magnitude and orientation of the hydraulic gradient. The relative head measurement error (RHME) is defined here as the measurement error normalized by the head drop across the three-point estimator. Monte Carlo simulation results show that estimators with base to height ratios between 0.5 and 5.0 and that are large enough to keep the RHME below 0.05 create the most accurate gradient estimates and provide criteria for identifying good estimators. These criteria are applied to an example ground water monitoring network design problem in the Culebra dolomite near the Waste Isolation Pilot Plant repository to both analyze temporal changes and modify and expand the current monitoring network. Limiting the three-point estimators to those that meet the shape and RHME criteria reduces the number of possible estimators by >50% and leads to approximately 1 order of magnitude decrease in the average estimated magnitude of the gradient relative to using all estimators. Application of these criteria also reduces the variability in estimated gradient magnitude and orientation between the two time periods of measurements. Redundant wells in the network are identified by removing each existing well in turn and determining which removals yield the smallest decrease in the number of acceptable estimators. Optimal new well locations are identified by mapping the increase in total number of acceptable estimators for a single new well placed in the study domain.
authors.affiliation: Geohydrology Department, Sandia National Laboratories, P.O. Box 5800, MS 0735, Albuquerque, NM 87185-0735, USA. samcken@sandia.gov
authors.forename: Sean A
authors.initials: SA
authors.lastname: McKenna
authors.forename: Arun
authors.initials: A
authors.lastname: Wahi
mesh_terms: D003198:Computer Simulation; D004784:Environmental Monitoring / Q000379:methods*; D005618:Fresh Water; D008962:Models, Theoretical; D009010:Monte Carlo Method; D012107:Research Design; D014872:Water Movements; D014881:Water Supply