ABSTRACT: Sensitivity of a land surface model and soil moisture assimilation system to bias in the hydrometeorological forcing.

CGRG Bibliography of Canadian Geomorphology
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Abstract

At regional to continental scales numerous studies have demonstrated the impact of realistic soil moisture initializations on the prediction of seasonal to sub-seasonal weather and climate, and streamflow. To develop realistic estimates of the soil moisture state researchers have focused on the development of appropriate land surface models (LSMs), on the development of suitable hydrometeorological forcing data sets for use in regional to continental LSMs, and on the development of methods for assimilating satellite soil moisture observations into land surface hydrological models. In this study we examine the sensitivity of a coupled LSM and data assimilation system to biases in the meteorological forcing for simulations of soil moisture and runoff over North America. Four simulations were completed as part of this study. In two simulations the LSM was driven with corrected and biased versions of the hydrometeorological forcing. These control simulations demonstrate impacts of bias in the forcing on LSM simulations of runoff and soil moisture. In the remaining two simulations, a data set of satellite derived soil moisture was assimilated into the LSM using an extended Kalman filter approach; identical to the control simulations, both corrected and biased versions of the hydrometeorological forcing were used for driving the LSM. Model errors and the sensitivity of the LSM/assimilation system to forcing bias are evaluated through comparisons of runoff and soil moisture between the four simulations. Further comparisons to observed runoff and soil moisture demonstrate the importance of the extended Kalman filter for reducing bias in the LSM.

Bibliography of Canadian Geomorphology