HR: 1400h
AN: H33B-03    [Abstracts]
TI: Real-time Soil Characterization with a Stochastic Data Assimilation Approach
AU: Wu, C
EM: chechuanwu@ucla.edu
AF: Department of Civil and Environmental Engineering, University of California at Los Angeles, 5731/5732 Boelter Hall 405 Hilgard Avenue, Los Angeles, CA 90095-1593, United States
AU: * Margulis, S A
EM: margulis@seas.ucla.edu
AF: Department of Civil and Environmental Engineering, University of California at Los Angeles, 5731/5732 Boelter Hall 405 Hilgard Avenue, Los Angeles, CA 90095-1593, United States
AB: Data assimilation (DA) is an estimation framework to characterize the states of a system by merging the information in measurements and physical models. The purpose of this study is to explore the feasibility of DA methods in maximizing the information content of sensor networks deployed in a complex environmental system, a test bed for wastewater re-use in Palmdale, CA. However, to protect the groundwater from pollution, a real-time monitoring system, consisting of a sensor network and coupled flow and transport models, especially for nitrate concentration, is implemented to ultimately provide feedback for irrigation operation. Synthetic experiments, which also consider sensor measurement errors, with the Ensemble Kalman Filter (EnKF) are performed to estimate the space-time evolution of the soil states under scenarios of successively more input uncertainty. The results from the EnKF are compared with the performance of an open-loop forward modeling simulation, which is not updated with sensor measurements, and show significant improvement in the estimate of soil moisture profiles under all uncertainty scenarios. Based on the improved estimation of water flow in vadose zone, improved estimates of nitrate concentrations are expected as well. Work to assimilate nitrate and soil temperature measurements and estimate sensor error characteristics online are ongoing.
DE: 1800 HYDROLOGY
DE: 1875 Vadose zone
SC: Hydrology [H]
MN: 2007 Joint Assembly