Evaluating and Benchmarking enhancements to the North American Land Data Assimilation System (Invited Presentation)

With funding from NOAA’s MAPP program, we have been augmenting the suite of land surface models and adding data assimilation capabilities to the North American Land Data Assimilation System (NLDAS).   We have systematically evaluated new models such as the Catchment LSM and Noah-MP, in addition to soil moisture, snow, and GRACE data assimilation.  Overall, these efforts have led to small improvements in water and energy fluxes and states that support implementation in the next phase of NLDAS.  Inspired by the PLUMBER project, which demonstrated that decades of land surface model inter-comparison projects have resulted in little model improvement against a regression benchmark, we recently extended this method to benchmark soil moisture state and evapotranspiration flux predictions made by the four land surface models in NLDAS phase 2. Parameters dominated uncertainty in soil moisture estimates and forcing data dominated uncertainty in evapotranspiration estimates; however, the models themselves used only a fraction of the information available to them. This means that there is significant potential to improve all three components of NLDAS-2. In particular, continued work toward refining the parameter maps and lookup tables, the forcing data measurement and processing, and also the land surface models themselves, has potential to result in improved estimates of surface mass and energy balances.