Utilizing ensemble-based data assimilation and the NASA Land Information System to improve soil moisture estimation across high mountain Asia
Abstract
A soil moisture-retrieval assimilation framework is implemented across high mountain Asia (HMA) in an attempt to enhance regional soil moisture estimates as well as to provide a consistent regional soil moisture product. This study aims to improve the spatiotemporal variability of soil moisture estimates across HMA by assimilating Soil Moisture Active Passive (SMAP) soil moisture retrievals into land surface model estimates of soil moisture. The Noah-MP land surface model is run within the NASA Land Information System software framework to model regional land surface processes. NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) provides the meteorological boundary conditions for Noah-MP model simulation. CDF-matching is applied to correct the distribution moments of the SMAP soil moisture retrieval, and thus, map the satellite-based estimates into the model soil moisture space. Normalized innovation statistics are utilized to perform a diagnostic analysis of the error characteristics used within the assimilation framework. Assimilated and model-only soil moisture estimates are compared to both in situ observations (provided by the Tibetan Plateau Observatory) and remote sensing-based datasets during evaluation. MODIS Vegetation Index Products (NDVI and EVI) are used as ancillary evaluation datasets to access the phenological link between soil moisture estimates and vegetation as related to the influence of soil moisture assimilation.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2020
- Bibcode:
- 2020AGUFMH006.0014A
- Keywords:
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- 1847 Modeling;
- HYDROLOGY;
- 1848 Monitoring networks;
- HYDROLOGY;
- 1855 Remote sensing;
- HYDROLOGY;
- 1866 Soil moisture;
- HYDROLOGY