Robust Predictive Design of Field Measurements for Evapotranspiration Barriers Using Universal Multiple linear Regression

Abstract

Surface barriers are commonly installed to reduce downward water movement into contaminated zones. Specifically, evapotranspiration (ET) 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, 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 to accommodate multiple monitoring objectives, 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 also added consideration of “fuzzy” design, which allows more practical guidelines for field implementation of uMLR. We found that uMLR, combined with robust decision‐making, provides a simple, flexible, and high‐quality network design for monitoring the total water stored in a surface barrier across multiple uncertain conditions.

Using Baseflow Ensembles for Hydrologic Hysteresis Characterization in Humid Basins of Southeastern China

“…Baseflow simulation approaches based on numerical simulation have distinct properties, although they are prone to frequent over‐ or underestimation, with huge errors. Ensemble simulation, which combines many sets of simulation data, enhances simulation accuracy and minimizes simulation uncertainty (Clutter et al., 2019; Tanaka et al., 2020; Wei et al., 2023). The problem of identifying the weights attached to different baseflow simulation methods is the main obstacle to using the baseflow ensemble simulation (BES) method.…”