Abstract:
Simulations of hydrologic and vegetation states as well as water, energy and carbon fluxes from the land surface to the atmosphere are crucial for a wide range of applications, including agricultural advisories, forecasts of (short-term) atmospheric behavior and seasonal weather predictions including forecasts of extreme events, such as heatwaves or droughts. The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Eco-Hydrology (L4-ECO) project aims to improve modeled estimates of the terrestrial water, energy and carbon fluxes and states by developing a fully-coupled hydrology-vegetation data assimilation system. This system is developed around the NASA Goddard Earth Observing System (GEOS) Catchment-CN land surface model, which combines land hydrology and energy balance components of the GEOS Catchment model with dynamic vegetation components of the Community Land Model version 4. Catchment-CN fully couples the terrestrial water, energy and carbon cycles, allowing feedbacks from the land hydrology to the biosphere and vice versa.

The SMAP L4-ECO project comprises three phases. First, we implement a calibration of the Catchment-CN vegetation parameterization against observations of the fraction of absorbed photosynthetically active radiation (FPAR) from the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 data to improve the model’s standalone skill. Secondly, the assimilation algorithm used to produce the SMAP L4 soil moisture product will be adapted to allow Catchment-CN to assimilate SMAP brightness temperatures and inform the model’s land hydrology component. Finally, the L4 ECO system will be further extended to assimilate MODIS FPAR observations in order to constrain the model’s dynamic vegetation component as well.

This presentation focuses on the calibration of Catchment-CN vegetation parameters. We show that the calibration reduces the root mean square error of Catchment-CN vegetation simulations with respect to observations across different plant functional types, mostly resulting from a bias reduction between model and observations. Additionally, we investigate the effect of the parameter calibration on the model’s related hydrologic states, such as the surface and root-zone soil moisture, as well as the terrestrial water, energy and carbon fluxes.