Soil Heterogeneity In Macroscale Hydrologic Models: Unresolved Challenges.

Global hydrologic models have their roots in numerical weather prediction and global circulation models. As a result, they emphasize simulating land surface fluxes while oversimplifying hydrologic processes, limiting their usefulness for local scale applications. One of the main challenges is the adequate representation of landscape heterogeneity (e.g. soil properties). In this presentation, we will discuss how an increase in the reliability and accessibility of high-resolution continental scale soil data can help solve this problem.

An enhanced statistical representation of land units (subsurface, surface, and vegetation) at the coarser grid scale can improve the representation of landscape heterogeneity and scalability between grid resolutions. To this end, adequate knowledge of the statistical distribution of soils within the coarser grid (including uncertainties) must be available from continental soil datasets. This is especially challenging over data-sparse regions where downscaling techniques must be developed to recreate the unknown heterogeneity.

Using available high-resolution continental-scale land datasets (SSURGO, NLCD, and NED) over the Little River Experimental Watershed in Georgia, we will explore how topographic information can be used to reproduce the fine scale heterogeneity from a coarse soil dataset. The TOPLATS hydrologic model will also be used to illustrate how the adequate statistical representation of fine-scale land units enables scalable model parameters and simulations.