The quality of meteorological forcing data has a strong impact on the simulation of the land surface component of the hydrological cycle. In this paper, the sensitivity of soil moisture simulations to combinations of different external meteorological forcing and vegetation parameters supplied by the International Satellite Land-Surface Climatology Project (ISLSCP) Initiative II (II2) as part of the Second Global Soil Wetness Project (GSWP-2) is evaluated by using the SSiB land surface model. The simulated plant-available soil moisture in the top 1 m of soil is compared against in situ observations over grasslands and agricultural regions in the former Soviet Union, United States (Illinois), China, and Mongolia from the Global Soil Moisture Data Bank. It is found that the skill of the simulations is very sensitive to the source of meteorological forcing and that hybridization of reanalysis products with observational data substantially improves the soil moisture simulations compared to reanalysis data alone. Sensitivity is highest among products of precipitation, radiation, and vegetation class. The range of changes in the skill of soil moisture simulations with the same land surface model in 13 different sensitivity studies is as large as that resulting from 11 different land surface models driven with the same meteorological forcing from the baseline integrations of GSWP-2. Assuming differences among versions of meteorological forcing fields are indicative of uncertainties in our knowledge of these drivers of the land surface climate, the impact of that uncertainty on land surface hydrology is as large as that from the variations among land surface models.
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