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United States Department of Agriculture

Agricultural Research Service

Title: A Generic, Interoperable, Hydrologic Data Assimilation Framework using the Land Information System

Authors
item Kumar, Sujay - UMBC/GEST NASA GSFC
item Peters-Lidard, Christa - NASA GSFC
item Eylander, John - AIR FORCE
item Reichle, Rolf - UMBC/GEST NASA GSFC
item Crow, Wade
item Zhan, Xiwu
item Houser, Paul - GMU CREW
item Koster, Randal - NASA GSFC
item Suarez, Max - NASA GSFC
item Dong, Jiarui - UMBC/GEST NASA GSFC

Submitted to: American Geophysical Union
Publication Type: Abstract Only
Publication Acceptance Date: November 5, 2006
Publication Date: December 11, 2006
Citation: Kumar, S., Peters-Lidard, C., Eylander, J., Reichle, R., Crow, W., Zhan, X., Houser, P., Koster, R.D., Suarez, M., Dong, J. 2006. A generic, interoperable, hydrologic data assimilation framework using the land information system [abstract]. EOS Transactions, American Geophysical Union, Fall Supplements. 87(52):H23E-1558.

Technical Abstract: Land Information System (LIS; http://lis.gsfc.nasa.gov) is a hydrologic modeling system that integrates the use of various community land surface models, use of ground and satellite-based observations, and high performance computing and data management tools to enable hydrologic prediction at various spatial and temporal scales of interest. The LIS architecture is designed using advanced software engineering principles, allowing the interoperability of land surface models, meteorological inputs, land surface parameters, and observational data. In this work, we describe the extension of the LIS framework to incorporate data assimilation capabilities, through a collaborative effort. The extensible data assimilation framework in LIS allows the incorporation and interplay of multiple observational sources, multiple data assimilation algorithms, and the use of multiple land surface models. These capabilities are demonstrated using a suite of experiments using various sources observational data assimilated into different land surface models using algorithms of interest, by propagating observational information in space and time. The sophistication of the algorithms varies from simple direct insertion to rule-based approaches to ensemble Kalman Filter (EnKF). To date, we have demonstrated the assimilation of soil moisture and snow water equivalent data sources in Noah land surface model using a number of different algorithms including the EnKF. Similar applications using the Catchment and community land surface model (CLM) will be demonstrated in this study. The study will also demonstrate the ability of the system to assimilate multiple observations such as soil moisture and surface temperature using these land surface models. These experiments are used to demonstrate the use of the flexible, extensible data assimilation framework provided by LIS in the effective application of hydrological observations and modeling tools to understand and improve the prediction water and energy cycles.

Last Modified: 8/22/2014
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