Effect of Soil Data Source and Complexity on Soil Water and Energy Flux Simulation By the Noah-MP LSM.

Soil plays a central role in a number of land surface models, but it is usually poorly represented. The objective of this project is to evaluate the suitability of different digital soil maps and their varying levels of detail on the performance of Noah-MP model in simulating water and energy fluxes in the southern Great Plains, USA. The CONUS-Soil (a 1-km resolution soil database based on the STATSGO) and POLARIS (a new soil database that remaps SSURGO using high-resolution geospatial environmental data and DSMART-HPC machine learning algorithm) were used to setup the Noah-MP land surface model. For these soil maps, the use of vertically heterogeneous soil data instead of uniform 2 m deep soil and the use of parameter maps instead of using look up tables were compared. In this presentation, we will demonstrate the effect of varying levels of representation of the soil system on the Noah-MP model outputs, and discuss comparisons of simulated soil moisture with soil moisture database from Soil Climate Analysis Network (SCAN), and simulated latent heat with latent heat estimates from Atmosphere-Land Exchange Inverse Model (ALEXI).