Deriving New Topography-based Global Datasets for Land Surface Modeling

Abstract:

Topography exerts a major control on land surface processes through its influence on atmospheric forcing, soil and vegetation properties, network topology and drainage area. Land surface spatial structure that captures spatial heterogeneity influenced by topography is expected to improve representation of land surface processes in land surface models. For example, land surface modeling using subbasins instead of regular grids as computational units has demonstrated improved scalability of simulated runoff and streamflow processes. In this study, a local classification method is applied to derive a new land surface spatial structure defined by further dividing subbasins into subgrid units based on elevation, topographic slope and aspect to take advantage of the emergent patterns and scaling properties of atmospheric, hydrologic, and vegetation processes in land surface models. For this purpose, a more consistent 90 meter resolution global surface elevation data has been developed by blending elevation data obtained from various sources. Taking the advantage of natural hydrologic connectivity of watersheds, new subbasin-based river routing and reservoir dependency datasets are being developed to improve representation of the managed hydrologic systems in the Community Land Model.