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 km²). 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.

Citation

Shi, Y., K. J. Davis, F. Zhang, C. J. Duffy, and X. Yu (2014): Parameter estimation of a physically-based land surface hydrologic model using the ensemble Kalman Filter: A synthetic experiment. Water Resources Research, 50, 706—724 . DOI: 10.1002/2013WR014070

This Paper/Book acknowledges NSF CZO grant support.