Observed and Simulated Soil Moisture Variability over the Lower Mississippi Delta Region

To better understand error and spatial variability sources of soil moisture simulated with land surface models, observed and simulated values of soil moisture (using offline simulations with the Noah land surface model with four soil layers and approximately 1-km horizontal grid spacing) were compared. This comparison between observed and modeled daily values of soil moisture was performed over the Lower Mississippi Delta region during summer-fall months 2004-06. The Noah simulations covered the 2.5 degrees x 2.5 degrees latitude-longitude domain and were forced by the North American Land Data Assimilation System (NLDAS) atmospheric forcing fields. Hourly soil moisture measurements and other data, including local meteorological and soil physical properties data from 12 Soil Climate Analysis Network (SCAN) sites, were used. The results show that both the observed and simulated level of soil moisture depend critically on the specified - sampled soil texture. Soil types with a relatively high observed clay content (more than 50% of weight) retain more water as a result of low water diffusivity than silty-sandy soils with 20% or less clay, provided that other conditions are the same. This fact is in agreement with previous studies and implies a strong soil texture control (through related hydraulic parameters) on the accuracy of simulated soil moisture. Sensitivity tests using the Noah model were performed to assess the effect of using the hydraulic parameters related to the site-specific soil texture on soil moisture quality. Indeed, at some SCAN sites, the errors (root-mean-square difference and bias) were reduced. Simulated soil moisture showed at least a 50% reduction when the site-specific soil texture was used in Noah simulations compared to those derived from the State Soil Geographic (STATSGO) data. The most significant improvement of simulated soil moisture was observed within the top 0-10 cm layer where an original positive bias (an excessive wetness) was almost eliminated. Meanwhile, excessive dryness (negative soil moisture bias), which was dominant within the second and third model layers, was also reduced. These improvements are expected to be valid at sites/regions with low (< 0.3) vegetation fraction.