Abstract
A novel approach for upscaling land-surface parameters based on inverse stochastic surface-vegetation-atmosphere transfer (SVAT) modelling is presented. It allows estimation of effective parameters that yield scale invariant outputs e.g. for sensible and latent heat fluxes and evaporative fraction. The general methodology is used to estimate effective parameters for the Oregon State University Land-Surface Model, including surface albedo, surface emissivity, roughness length, minimum stomatal resistance, leaf area index, vapour pressure deficit factor, solar insolation factor and the Clapp–Hornberger soil parameter. Upscaling laws were developed that map the mean and standard deviation of the distributed land-surface parameters at the subgrid scale to their corresponding effective parameter at the grid scale. Both linear and bi-parabolic upscaling laws were obtained for the roughness length. The bi-parabolic upscaling law fitted best for the remaining land-surface parameters, except surface albedo and emissivity, which were best fitted with linear upscaling laws.
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