Parameter estimation of a physically based land surface hydrologic model using the ensemble Kalman filter: A synthetic experiment

Yuning Shi, Kenneth James Davis, Fuqing Zhang, Christopher J. Duffy, Xuan Yu

Research output: Contribution to journalArticle

40 Citations (Scopus)

Abstract

This paper presents multiple parameter estimation using multivariate observations via the ensemble Kalman filter (EnKF) for a physically based land surface hydrologic model. A data assimilation system is developed for a coupled physically based land surface hydrologic model (Flux-PIHM) by incorporating EnKF for model parameter and state estimation. Synthetic data experiments are performed at a first-order watershed, the Shale Hills watershed (0.08 km 2). Six model parameters are estimated. Observations of discharge, water table depth, soil moisture, land surface temperature, sensible and latent heat fluxes, and transpiration are assimilated into the system. The results show that, given a limited number of site-specific observations, the EnKF can be used to estimate Flux-PIHM model parameters. All the estimated parameter values are very close to their true values, with the true values inside the estimated uncertainty range (1 standard deviation spread). The estimated parameter values are not affected by the initial guesses. It is found that discharge, soil moisture, and land surface temperature (or sensible and latent heat fluxes) are the most critical observations for the estimation of those six model parameters. The assimilation of multivariate observations applies strong constraints to parameter estimation, and provides unique parameter solutions. Model results reveal strong interaction between the van Genuchten parameters α and β, and between land surface and subsurface parameters. The EnKF data assimilation system provides a new approach for physically based hydrologic model calibration using multivariate observations. It can be used to provide guidance for observational system designs, and is promising for real-time probabilistic flood and drought forecasting. Key Points EnKF can be used to estimate the model parameters of a physical hyrologic model Multivariate observations provide unique parameter estimation solutions EnKF identifies interacting parameters and quantifies parameter correlation

Original languageEnglish (US)
Pages (from-to)706-724
Number of pages19
JournalWater Resources Research
Volume50
Issue number1
DOIs
StatePublished - Jan 1 2014

Fingerprint

Kalman filter
land surface
experiment
latent heat flux
sensible heat flux
data assimilation
parameter estimation
parameter
surface temperature
soil moisture
watershed
transpiration
water table
shale
drought
calibration

All Science Journal Classification (ASJC) codes

  • Water Science and Technology

Cite this

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title = "Parameter estimation of a physically based land surface hydrologic model using the ensemble Kalman filter: A synthetic experiment",
abstract = "This paper presents multiple parameter estimation using multivariate observations via the ensemble Kalman filter (EnKF) for a physically based land surface hydrologic model. A data assimilation system is developed for a coupled physically based land surface hydrologic model (Flux-PIHM) by incorporating EnKF for model parameter and state estimation. Synthetic data experiments are performed at a first-order watershed, the Shale Hills watershed (0.08 km 2). Six model parameters are estimated. Observations of discharge, water table depth, soil moisture, land surface temperature, sensible and latent heat fluxes, and transpiration are assimilated into the system. The results show that, given a limited number of site-specific observations, the EnKF can be used to estimate Flux-PIHM model parameters. All the estimated parameter values are very close to their true values, with the true values inside the estimated uncertainty range (1 standard deviation spread). The estimated parameter values are not affected by the initial guesses. It is found that discharge, soil moisture, and land surface temperature (or sensible and latent heat fluxes) are the most critical observations for the estimation of those six model parameters. The assimilation of multivariate observations applies strong constraints to parameter estimation, and provides unique parameter solutions. Model results reveal strong interaction between the van Genuchten parameters α and β, and between land surface and subsurface parameters. The EnKF data assimilation system provides a new approach for physically based hydrologic model calibration using multivariate observations. It can be used to provide guidance for observational system designs, and is promising for real-time probabilistic flood and drought forecasting. Key Points EnKF can be used to estimate the model parameters of a physical hyrologic model Multivariate observations provide unique parameter estimation solutions EnKF identifies interacting parameters and quantifies parameter correlation",
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Parameter estimation of a physically based land surface hydrologic model using the ensemble Kalman filter : A synthetic experiment. / Shi, Yuning; Davis, Kenneth James; Zhang, Fuqing; Duffy, Christopher J.; Yu, Xuan.

In: Water Resources Research, Vol. 50, No. 1, 01.01.2014, p. 706-724.

Research output: Contribution to journalArticle

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