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United States Department of Agriculture

Agricultural Research Service

Research Project: USING REMOTE SENSING & MODELING FOR EVALUATING HYDROLOGIC FLUXES, STATES, & CONSTITUENT TRANSPORT PROCESSES WITHIN AGRICULTURAL LANDSCAPES Title: Field-Scale soil moisture assimilation: State, parameter or bias estimation?

Authors
item DE Lannoy, Gabrielle -
item Pauwels, Valentijn -
item Reichle, Rolf -
item Kustas, William
item Gish, Timothy
item Houser, Paul -
item Verhoest, Niko -

Submitted to: American Meteorological Society
Publication Type: Abstract Only
Publication Acceptance Date: January 30, 2011
Publication Date: February 4, 2011
Citation: De Lannoy, G.J., Pauwels, V., Reichle, R.H., Kustas, W.P., Gish, T.J., Houser, P.R., Verhoest, N. 2011. Field-Scale soil moisture assimilation: State, parameter or bias estimation? [abstract]. American Meteorological Society. Available: http://ams.confex.com/ams/91Annual/25hydro/papers/Abstract.

Technical Abstract: Observations can be used to constrain model parameters (calibration), model state variables (state updating,initialization), model error (bias estimation, error characterization) or any combination thereof. It is studied how soil moisture profile observations are best exploited with Community Land Model (CLM) simulations to optimize forecasts of the land surface state and fluxes in a small agricultural field (Production Inputs for Economic and Environmental Enhancement field, OPE3). Observations are assimilated to (i) optimize the model parameters with a variational method, (ii) sequentially update the state, or (iii) sequentially correct for forecast bias. The advantages and disadvantages of each technique are described with respect to their impact on the soil moisture and land surface flux estimation. It is shown that calibration only cannot remove all discrepancy between models and observations and bias estimation in dataassimilation is necessary.

Last Modified: 8/22/2014
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