HR: 1340h
AN: H33C-0484    [Abstracts]
TI: On the effectiveness of assimilation of coarse scale near-surface soil moisture imagery and fine scale temporal ratios of soil moisture into land surface models
AU: * Parada, L M
EM: lparada@berkeley.edu
AF: University of California, Berkeley, Dept. of Civil and Environmental Engineering Hearst Field Annex Building B, Berkeley, CA 94720 United States
AU: Liang, X
EM: liang@ce.berkeley.edu
AF: University of California, Berkeley, Dept. of Civil and Environmental Engineering Hearst Field Annex Building B, Berkeley, CA 94720 United States
AB: Remote sensing retrievals of near-surface soil moisture constitute a major asset for constraining and improving the predictions of the water and energy fluxes at the earth surface in ungauged basins. The expected near-future resolution of near-surface soil moisture imagery derived from passive remote sensing sources is approximately 30 km. While active remote sensing retrievals of near-surface soil moisture may be derived at finer resolutions (~100 m), these have been shown to be prone to error and large uncertainties in the presence of vegetation. However, recent evidence suggests that the temporal ratios of near-surface soil moisture retrievals from active remote sensing sources may retain much of the soil moisture signal with decreased uncertainty. In this study, we first evaluate the impacts of the resolution of passive remotely sensed soil moisture imagery (from 800-m to 25-km) on the effectiveness of assimilation of these fields with regards to improving the prediction of soil moisture states and energy fluxes from the Three Layer Variable Infiltration Capacity (VIC-3L) land surface model. We additionally assess the value of fine-scale remotely sensed temporal ratios of near-surface soil moisture when assimilated in conjunction with coarse scale near-surface soil moisture imagery. We conduct our investigation with the near-surface soil moisture retrievals derived during the Southern Great Plains Hydrology experiment of 1997 (SGP97). Assimilation is performed with an extension of multiscale Kalman filtering (MKF). MKF is ideally suited for this study as it permits for the remotely sensed near-surface soil moisture retrievals and land surface model predictions to be available at distinct spatial scales and allows for optimal characterizations of time varying uncertainties in the land surface model predictions and the observations, respectively.
DE: 3337 Numerical modeling and data assimilation
DE: 3360 Remote sensing
DE: 1866 Soil moisture
DE: 1869 Stochastic processes
DE: 1878 Water/energy interactions
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting