TY - JOUR
T1 - Effect of simultaneous state-parameter estimation and forcing uncertainties on root-zone soil moisture for dynamic vegetation using EnKF
AU - Monsivais-Huertero, Alejandro
AU - Graham, Wendy D.
AU - Judge, Jasmeet
AU - Agrawal, Divya
N1 - Funding Information:
This research was supported by the NSF Earth Science Directorate ( EAR-0337277 ) and the NASA New Investigator Program ( NASA-NIP-00050655 ). The authors acknowledge computational resources and support provided by the University of Florida High-Performance Computing Center for the simulations conducted in this study.
PY - 2010/4
Y1 - 2010/4
N2 - 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.
AB - 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.
KW - Ensemble Kalman Filter
KW - Root-zone soil moisture
KW - SVAT-vegetation models
UR - http://www.scopus.com/inward/record.url?scp=77950472509&partnerID=8YFLogxK
U2 - 10.1016/j.advwatres.2010.01.011
DO - 10.1016/j.advwatres.2010.01.011
M3 - Artículo
SN - 0309-1708
VL - 33
SP - 468
EP - 484
JO - Advances in Water Resources
JF - Advances in Water Resources
IS - 4
ER -