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Abstract:
Off-line land surface modeling simulations require accurate
meteorological forcing with consistent spatial and temporal
resolutions. Although reanalysis products present an attractive data
source for these types of applications, bias to many of the reanalysis
fields limits their use for hydrological modeling. In
this study, we develop a global 0.5 degree forcing data sets for
the time period 1979-1993 on a 6-hourly time step through application
of a bias correction scheme to reanalysis products. We then use this
forcing data to drive a land surface model for global estimation of
soil moisture and other hydrological states and fluxes. The simulated
soil moisture estimates are compared to in situ measurements,
satellite observations and to a modeled data set of root zone soil
moisture produced within a separate land surface model, using a
different data set of hydrometeorological forcing. In general, there
is good agreement between anomalies in modeled and observed (in situ)
root zone soil moisture. Similarly, for the surface soil wetness
state, modeled estimates and satellite observations are in general
statistical agreement; however, correlations decline with increasing
vegetation amount. Comparisons to a modeled data set of soil moisture
also demonstrates that both simulations present estimates that are
well correlated for the soil moisture in the anomaly time series,
despite being derived from different land surface models, using
different data sources for meteorological forcing, and with different
specifications of the land surfaces properties.