Effect of simultaneous state-parameter estimation and forcing uncertainties on root-zone soil moisture for dynamic vegetation using EnKF

Alejandro Monsivais-Huertero, Wendy D. Graham, Jasmeet Judge, Divya Agrawal

Research output: Contribution to journalArticleResearchpeer-review

23 Citations (Scopus)

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. © 2010.
Original languageAmerican English
Pages (from-to)468-484
Number of pages17
JournalAdvances in Water Resources
DOIs
StatePublished - 1 Apr 2010
Externally publishedYes

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vegetation dynamics
rhizosphere
soil moisture
biophysics
effect
parameter estimation
growing season
maize
irrigation

Cite this

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title = "Effect of simultaneous state-parameter estimation and forcing uncertainties on root-zone soil moisture for dynamic vegetation using EnKF",
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. {\circledC} 2010.",
author = "Alejandro Monsivais-Huertero and Graham, {Wendy D.} and Jasmeet Judge and Divya Agrawal",
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Effect of simultaneous state-parameter estimation and forcing uncertainties on root-zone soil moisture for dynamic vegetation using EnKF. / Monsivais-Huertero, Alejandro; Graham, Wendy D.; Judge, Jasmeet; Agrawal, Divya.

In: Advances in Water Resources, 01.04.2010, p. 468-484.

Research output: Contribution to journalArticleResearchpeer-review

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

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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. © 2010.

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. © 2010.

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