The capability of an ensemble Kalman filter (EnKF) to simultaneously estimate multiple parameters in a physically-based land surface hydrologic model using multivariate field observations is tested at a small watershed (0.08 km2). Multivariate, high temporal resolution, in situ measurements of discharge, water table depth, soil moisture, and sensible and latent heat fluxes encompassing five months of 2009 are assimilated. It is found that, for five out of the six parameters, the EnKF estimated parameter values from different test cases converge strongly, and the estimates after convergence are close to the manually calibrated parameter values. The EnKF estimated parameters and manually calibrated parameters yield similar model performance, but the EnKF sequential method significantly decreases the time and labor required for calibration. The results demonstrate that, given a limited number of multi-state, site-specific observations, an automated sequential calibration method (EnKF) can be used to optimize physically-based land surface hydrologic models
Shi, Y., K. J. Davis, F. Zhang and C. J. Duffy, and X. Yu (2015): Parameter estimation of a physically-based land surface hydrologic model using an ensemble Kalman filter: A multivariate real-data experiment. Advances in Water Resources, vol 83, 421–427. DOI: 10.1016/j.advwatres.2015.06.009
This Paper/Book acknowledges NSF CZO grant support.