How do different meteorological forcings influence NoahMP soil moisture and turbulent fluxes?
Alexa Marcovecchio1, John Eylander2, Ali Behrangi1, Guo-Yue Niu1, Xiquan Dong1
1 Department of Hydrology and Atmospheric Sciences
The University of Arizona, Tucson, AZ
2 Coastal and Hydraulics Laboratory
USACE Engineer Research and Development Center, Vicksburg, MS
When using Land Surface Models (LSMs) to predict future soil moisture quantities or fill in geographical gaps in historical soil moisture, we need to account for uncertainty. Some of this uncertainty is inherent to the model’s parameterization of energy, mass, and momentum exchange at the surface, but some is propagated from input data like meteorological forcings. Meteorological data has high spatial and temporal variability that is passed on to model output. To address this, we investigate the sensitivity of the NoahMP LSM to meteorological forcings by running the same LSM scenario using different meteorological forcing datasets. In doing so, we plan to see how the strengths and weaknesses of each meteorological dataset propagate to inaccuracies in soil moisture in the continental United States. One run uses ERA5 (ECMWF Reanalysis Version 5), one uses GDAS (Global Data Assimilation System), and one uses the US Air Force Weather Analysis (AFWA). Model output of soil moisture is compared to in-situ measurements from the USCRN (U.S. Climate Reference Network) and satellite observations from SMAP (Soil Moisture Active Passive). Model output of surface turbulent fluxes (sensible and latent heat) are compared to in-situ flux tower measurements at seven ARM Southern Great Plains sites.