489 NCA-LDAS, a Terrestrial Hydrology Satellite Data Assimilation System for the National Climate Assessment: Overview and Ver. 1.0 Hydrologic Indicators

Tuesday, 9 January 2018
Exhibit Hall 3 (ACC) (Austin, Texas)
Michael F. Jasinski, NASA GSFC, Greenbelt, MD; and S. V. Kumar, J. S. Borak, D. M. Mocko, C. D. Peters-Lidard, M. Rodell, H. Rui, H. Beaudoing, B. Li, K. R. Arsenault, B. Vollmer, and J. Bolten

The National Climate Assessment - Land Data Assimilation System, or NCA-LDAS, is a terrestrial hydrology satellite data assimilation system in support of the National Climate Assessment (NCA). Developed within the NASA Land Information System (LIS) modeling framework, NCA-LDAS: i) integrates input data from multiple sources including a 35-year record of satellite Earth observations within an ensemble of land surface models over the continental U.S., ii) demonstrates for the first time, a multi-sensor, multivariate data assimilation model, simultaneously assimilating spaced-based observations of soil moisture, snow depth and cover, and irrigation intensity, to improve regional to continental scale hydrologic characterization, iii) delivers gridded, daily time series of land surface variables including fluxes (e.g., precipitation, net radiation, runoff, latent and sensible heat), storages (e.g. soil moisture and snow water equivalent), and other states (e.g., surface temperature, snow cover, flooded area); and iv) provides public access to all input and output data products through several available pathways, for downloading and visualization, from a dedicated NCA-LDAS web portal at the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC).

In additional to an overview of NCA-LDAS, hydrologic indicators derived from NCA-LDAS Version 1.0 energy and water balance data products are presented. Indicators were constructed for trends in the annual mean precipitation, temperature, net radiation, runoff, evapotranspiration, soil moisture, snow cover and snow water equivalent (SWE) as well as several extreme trends for precipitation and runoff. Results show that the NCA-LDAS trends using the multivariate data assimilation (DA) demonstrate an overall improvement when compared to the non-data assimilation or ensemble open loop (OL) model, and compare favorably to similar available published indicators based mainly on in situ data. Guidelines on the use of NCA-LDAS data products and indicators are presented for scientific research and decision support.

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