A. Boone et Pj. Wetzel, A simple scheme for modeling sub-grid soil texture variability for use in an atmospheric climate model, J METEO JPN, 77(1B), 1999, pp. 317-333
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 dea
l of this variability results from subgrid variability of soil texture. For
such scales, consideration of the variability of the parameters used to ch
aracterize 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 configur
ed beneath a single soil/vegetation surface. The hydrological parameter mea
n values and statistical moments, which must be defined for each column, ar
e generated using simple regression relationships which relate the paramete
rs to the grid box mean soil texture (sand and clay composition). This simp
le approach is used because subgrid heterogeneity parameter data is somewha
t limited on a global scale.
The Parameterization for Land-Atmosphere-Cloud Exchange (PLACE) model is us
ed within the Global Soil Wetness Project (GSWP) experimental design to gen
erate 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 budge
t variables due to consideration of the parameter variability: globally-ave
raged evapotranspiration is reduced by 17 %, and total runoff is increased
by 48 % compared to a control run assuming a homogeneous soil texture distr
ibution within each grid box. The global mean runoff ratio is increased by
12 %. Soil wetness (SW) increases by 19 %, while the soil wetness index (SW
I) increases by 49 %. it is suggested that future land-surface global data
sets contain information regarding subgrid variability of the soil for furt
her testing of methods for modeling sub-grid heterogeneity.