Hyper-Resolution Hydrologic Modeling Enabled by SMAP Observations

By controlling the partitioning of available energy incident on the land surface, surface and root zone soil moisture is a key variable in land–atmosphere interactions that impact local weather, such as cloud coverage and precipitation, and hydrological parameters, such as runoff and evapotranspiration. Hence, an accurate characterization of the soil water content profile can lead to improvements not only in weather and climate prediction, but also in hazard mitigation (floods and droughts), agricultural planning, and water resources management. However, in situ measurements of soil moisture are relatively rare and satellites can only measure soil moisture at relatively coarse resolution within the top few centimeters of the soil column. Thus, modeling is necessary to estimate root zone soil moisture and its variations over time, space and with depth in the soil column.

This project seeks to improve estimates of hyper-resolution root zone soil moisture following a three-step approach:

hyper-resolution modeling using the Land Information System (LIS) over Oklahoma;
constrain this with downscaling assimilation of SMAP level 3 products; and
validate and calibrate the hyper-resolution surface and root zone soil moisture products using ground-based observations.

The proposed hourly, 500-m hyper-resolution hydrological modeling system is largely driven by a hyper-resolution land surface weather boundary condition dataset (near-surface air temperature and humidity, wind speed and direction, incident longwave and shortwave radiation, pressure and precipitation) over Oklahoma. Spatial downscaling of the NLDAS dataset is performed using a combination of physically-based downscaling techniques (temperature/humidity lapse rate corrections, radiation slope corrections, land use, etc.). Then a land data assimilation system will merge the SMAP Level 3 products, which are daily composites of Level 2 surface soil moisture. Calibration and validation heavily relies on in-situ soil moisture networks (e.g., Oklahoma Mesonet, DOE-ARM sites) that provide high-resolution observations over the region.

The project will result in a radical improvement over the current state-of-the-art forcing data and soil moisture estimation, and will move the NASA Earth Science program into the era of hyper-resolution land modeling and data assimilation.

Funded by NASA ROSES 2015 Science Utilization of the Soil Moisture Active-Passive Mission

Team: Dr. Maggioni (PI); Dr. Houser (co-I); Dr. Rouf

Performance Period: August 2016 – July 2019