title: Parameter Estimation for Groundwater Models under Uncertain Irrigation Data.
abstract: The success of modeling groundwater is strongly influenced by the accuracy of the model parameters that are used to characterize the subsurface system. However, the presence of uncertainty and possibly bias in groundwater model source/sink terms may lead to biased estimates of model parameters and model predictions when the standard regression-based inverse modeling techniques are used. This study first quantifies the levels of bias in groundwater model parameters and predictions due to the presence of errors in irrigation data. Then, a new inverse modeling technique called input uncertainty weighted least-squares (IUWLS) is presented for unbiased estimation of the parameters when pumping and other source/sink data are uncertain. The approach uses the concept of generalized least-squares method with the weight of the objective function depending on the level of pumping uncertainty and iteratively adjusted during the parameter optimization process. We have conducted both analytical and numerical experiments, using irrigation pumping data from the Republican River Basin in Nebraska, to evaluate the performance of ordinary least-squares (OLS) and IUWLS calibration methods under different levels of uncertainty of irrigation data and calibration conditions. The result from the OLS method shows the presence of statistically significant (p < 0.05) bias in estimated parameters and model predictions that persist despite calibrating the models to different calibration data and sample sizes. However, by directly accounting for the irrigation pumping uncertainties during the calibration procedures, the proposed IUWLS is able to minimize the bias effectively without adding significant computational burden to the calibration processes.
authors.forename: Yonas
authors.initials: Y
authors.lastname: Demissie
authors.affiliation: Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801.
authors.forename: Albert
authors.initials: A
authors.lastname: Valocchi
authors.affiliation: Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801.
authors.forename: Ximing
authors.initials: X
authors.lastname: Cai
authors.affiliation: Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign, Urbana, IL 61801.
authors.forename: Nicholas
authors.initials: N
authors.lastname: Brozovic
authors.affiliation: USGS Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD 57030.
authors.forename: Gabriel
authors.initials: G
authors.lastname: Senay
authors.affiliation: Department of Civil & Environmental Engineering, University of California Los Angeles, Los Angeles, CA 90095.
authors.forename: Mekonnen
authors.initials: M
authors.lastname: Gebremichael
mesh_terms: D002138:Calibration; D060587:Groundwater; D062070:Hydrology; D016018:Least-Squares Analysis; D008962:Models, Theoretical; D009329:Nebraska; D035501:Uncertainty