Abstract
In this study, an EnKF-based assimilation algorithm was implemented to estimate root-zone soil moisture (RZSM) using the coupled LSP–DSSAT model during a growing season of corn. Experiments using both synthetic and field observations were conducted to understand effects of simultaneous state–parameter estimation, spatial and temporal update frequency, and forcing uncertainties on RZSM estimates. Estimating the state–parameters simultaneously with every 3-day assimilation of volumetric soil moisture (VSM) observations at 5 depths lowered the average standard deviation (ASD) and the root mean square error (RMSE) for RZSM by approximately 1.77% VSM (78%) and 2.18% VSM (93%), respectively, compared to the open-loop ASD where as estimating only states lowered the ASD by approximately 1.26% VSM (56%) and the RMSE by 1.66% VSM (71%). The synthetic case obtained RZSM estimates closer to the observations than the MicroWEX-2 case, particularly after precipitation/irrigation events. The differences in EnKF performance between MicroWEX-2 and synthetic observations may indicate other sources of errors in addition to those in parameters and forcings, such as errors in model biophysics.
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