Parameter Sensitivity in LSMs: An Analysis Using Stochastic Soil Moisture Models and ELDAS Soil Parameters

Integration of simulated and observed states through data assimilation as well as model evaluation requires a
realistic representation of soil moisture in land surface models (LSMs). However, soil moisture in LSMs is
sensitive to a range of uncertain input parameters, and intermodel differences in parameter values are often
large. Here, the effect of soil parameters on soil moisture and evapotranspiration are investigated by using
parameters from three different LSMs participating in the European LandData Assimilation System (ELDAS)
project. To prevent compensating effects from other than soil parameters, the effects are evaluated within a
common framework of parsimonious stochastic soil moisture models. First, soil parameters are shown to affect
soilmoisturemore strongly than the average evapotranspiration. In arid climates, the effect of soil parameters is
on the variance rather than the mean, and the intermodel flux differences are smallest. Soil parameters from the
ELDAS LSMs differ strongly,most notably in the available moisture content between the wilting point and the
critical moisture content, which differ by a factor of 3. The ELDAS parameters can lead to differences in mean
volumetric soil moisture as high as 0.10 and an average evapotranspiration of 10%–20% for the investigated
parameter range. The parsimonious framework presented here can be used to investigate first-order parameter
sensitivities under a range of climate conditions without using full LSM simulations. The results are consistent
with many other studies using different LSMs under a more limited range of possible forcing conditions.