A Nomograph to Incorporate Geophysical Heterogeneity in Soil Moisture Downscaling
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AbstractHydrological applications require robust and periodic spatially distributed soil moisture data. Radiometerbased soil moisture (~3060km resolution), after being appropriately downscaled (<5km resolution), can be a valuable resource for providing such data globally. However, the accuracy of available downscaling algorithms is severely affected by subgrid variability in geophysical factors and precipitation within a satellite footprint. In this work, we introduce a scaling nomograph that incorporates the scale and site specific dependence of soil moisture on geophysical heterogeneity and antecedent wetness conditions to overcome this limitation. We developed functional scaling relationships to estimate the semivariogram of downscaled soil moisture change without any available finescale soil moisture data. The nomograph enables these relationships to be specific to the geophysical heterogeneity and antecedent wetness within a radiometerbased satellite footprint through footprint specific heterogeneity and wetness indices. The heterogeneity index quantifies the subgrid scale variability and covariability of soil, vegetation, and topography within the footprint, and the wetness index is a measure of antecedent precipitation. The nomograph was developed for Arizona, Iowa, and Oklahoma and can enable downscaling to scales varying between 0.8 and 6.4km. The true power of the nomograph is to enable the use of static dominant factors like soil to define dynamic scale specific scaling relationships for soil moisture for different kinds of land use and land cover in a data driven yet scientific approach, thus providing spatial transferability to the downscaling scheme. The spatial transferability of the nomograph was validated by downscaling Soil Moisture Ocean Salinity data in Manitoba, Canada.
