PNNL

Abstract

Surface barriers are commonly installed to reduce downward water movement into contaminated zones. Specifically, evapotranspiration barriers are used to store water and release it, via ET, before it can percolate into an underlying waste zone. To assess the effectiveness of a surface barrier, neutron probe or other types of sensors may be used at different spatial locations to monitor this store-and-release mechanism. We used an existing data set, model-simulated data, and a dimensionality reduction approach called universal multiple linear regression (uMLR), to optimize the required number of sensors in a 2-m thick surface barrier. To understand the usefulness of implementing predictive uMLR prior to sensor installation, we compare several network designs, selected based on down-sampling of existing data, with a recommended sensor design based on model-simulations performed without consideration of existing data. We found that uMLR, combined with robust decision making, provides a simple and high-quality network design for robust monitoring of the total water stored in a surface barrier.