A Simple Scheme for Modeling Sub-Grid Soil Texture Variability for Use in an Atmospheric Climate Model

Abstract

On global atmospheric climate model spatial scales, water budget variables (evapotranspiration, soil moisture and runoff) can vary nonlinearly within a typical grid box primarily due to soil moisture heterogeneity. A good deal of this variability results from subgrid variability of soil texture. For such scales, consideration of the variability of the parameters used to characterize the soil hydrology is warranted. A simple approach, amenable to climate modeling, for characterizing subgrid soil parameter variability is proposed in which several parallel noninteracting soil columns are configured beneath a single soil/vegetation surface. The hydrological parameter mean values and statistical moments, which must be defined for each column, are generated using simple regression relationships which relate the parameters to the grid box mean soil texture (sand and clay composition). This simple approach is used because subgrid heterogeneity parameter data is somewhat limited on a global scale. The Parameterization for Land-Atmosphere-Cloud Exchange (PLACE) model is used within the Global Soil Wetness Project (GSWP) experimental design to generate global soil moisture fields using the soil heterogeneity model. Grid box average evapotranspiration (used in the solution of the surface energy budget), soil moisture, and runoff represent the three soil columns surface-weighted totals: Results show a profound effect on the primary water budget variables due to consideration of the parameter variability: globally-averaged evapotranspiration is reduced by 17 %, and total runoff is increased by 48 % compared to a control run assuming a homogeneous soil texture distribution within each grid box. The global mean runoff ratio is increased by 12 %. Soil wetness (SW) increases by 19 %, while the soil wetness index (SWI) increases by 49 %. it is suggested that future land-surface global data sets contain information regarding subgrid variability of the soil for further testing of methods for modeling sub-grid heterogeneity.