Global Soil Moisture Estimates from a Long-Term Hydrometeorological Forcing Data Set

Offline land surface modeling simulations require 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 set 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, satellite observations and to the modeled estimates of Nijssen et al. [2001]. In general, there is good agreement between anomalies in modeled and observed root zone soil moisture (top one meter). Similarly for the surface soil wetness state, modeled estimates and satellite observations are also in general statistical agreement, however, correlations decline with increasing sub-grid variability and vegetation amount. Comparisons to the data set of Nijssen et al., [2001] also demonstrates that both simulations present complimentary estimates of wet and dry root zone soil moisture anomalies, despite being derived from different land surface models, using different data sources for meteorological forcing, and with different specifications of the land surfaces properties. Results of this study demonstrate that reanalysis products corrected to observations can be used within a land surface model to produce soil moisture estimates in general agreement with surface and root zone observations.