TY - JOUR
T1 - Particle Filter-based assimilation algorithms for improved estimation of root-zone soil moisture under dynamic vegetation conditions
AU - Nagarajan, Karthik
AU - Judge, Jasmeet
AU - Graham, Wendy D.
AU - Monsivais-Huertero, Alejandro
N1 - Funding Information:
This research was supported by the NSF Earth Science Division ( EAR-0337277 ) and the NASA Terrestrial Hydrology Program ( NASA-THP-NNX09AK29G ). Partial support for the MicroWEX-2 was provided by 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 - 2011/4
Y1 - 2011/4
N2 - In this study, we implement Particle Filter (PF)-based assimilation algorithms to improve root-zone soil moisture (RZSM) estimates from a coupled SVAT-vegetation model during a growing season of sweet corn in North Central Florida. The results from four different PF algorithms were compared with those from the Ensemble Kalman Filter (EnKF) when near-surface soil moisture was assimilated every 3. days using both synthetic and field observations. In the synthetic case, the PF algorithm with the best performance used residual resampling of the states and obtained resampled parameters from a uniform distribution and provided reductions of 76% in root mean square error (RMSE) over the openloop estimates. The EnKF provided the RZSM and parameter estimates that were closer to the truth than the PF with an 84% reduction in RMSE. When field observations were assimilated, the PF algorithm that maintained maximum parameter diversity offered the largest reduction of 16% in root mean square difference (RMSD) over the openloop estimates. Minimal differences were observed in the overall performance of the EnKF and PF using field observations since errors in model physics affected both the filters in a similar manner, with maximum reductions in RMSD compared to the openloop during the mid and reproductive stages.
AB - In this study, we implement Particle Filter (PF)-based assimilation algorithms to improve root-zone soil moisture (RZSM) estimates from a coupled SVAT-vegetation model during a growing season of sweet corn in North Central Florida. The results from four different PF algorithms were compared with those from the Ensemble Kalman Filter (EnKF) when near-surface soil moisture was assimilated every 3. days using both synthetic and field observations. In the synthetic case, the PF algorithm with the best performance used residual resampling of the states and obtained resampled parameters from a uniform distribution and provided reductions of 76% in root mean square error (RMSE) over the openloop estimates. The EnKF provided the RZSM and parameter estimates that were closer to the truth than the PF with an 84% reduction in RMSE. When field observations were assimilated, the PF algorithm that maintained maximum parameter diversity offered the largest reduction of 16% in root mean square difference (RMSD) over the openloop estimates. Minimal differences were observed in the overall performance of the EnKF and PF using field observations since errors in model physics affected both the filters in a similar manner, with maximum reductions in RMSD compared to the openloop during the mid and reproductive stages.
KW - EnKF
KW - MicroWEX-2
KW - Particle Filter
KW - Root zone soil moisture
KW - SVAT-vegetation models
UR - http://www.scopus.com/inward/record.url?scp=79952184589&partnerID=8YFLogxK
U2 - 10.1016/j.advwatres.2010.09.019
DO - 10.1016/j.advwatres.2010.09.019
M3 - Artículo
SN - 0309-1708
VL - 34
SP - 433
EP - 447
JO - Advances in Water Resources
JF - Advances in Water Resources
IS - 4
ER -