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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #296727
Title: Benchmarking a soil moisture data assimilation system for agricultural drought monitoring

Author
item HAN, E - Collaborator
item Crow, Wade
item Holmes, Thomas
item BOLTEN, J - Goddard Space Flight Center

Submitted to: Journal of Hydrometeorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/28/2014
Publication Date: 6/1/2014
Publication URL: http://handle.nal.usda.gov/10113/60021
Citation: Han, E., Crow, W.T., Holmes, T.R., Bolten, J. 2014. Benchmarking a soil moisture data assimilation system for agricultural drought monitoring. Journal of Hydrometeorology. 15:1117-1134. DOI: 10.1175/JHM-D-13-0125.1.

Interpretive Summary: State-of-the-art methods for monitoring agricultural drought are based on the application of land surface data assimilation systems to optimally merge remotely-sensed surface soil moisture retrievals with root-zone soil moisture estimates derived from a soil water balance model. As a result, these systems contain three separate components: a retrieval component to convert remotely-sensed observations into surface soil moisture estimates, a soil water balance modeling component to convert observed precipitation into root-zone soil moisture predictions, and a data assimilation component to integrate remotely-sensed surface soil moisture estimates (component #1) into the water balance model (component #2). Recent work has demonstrated that - taken as a whole - such systems perform rather well. However, much less is currently known about the relative performance of individual components within the aggregate system. This paper derives and applies a benchmarking evaluation strategy to examine the performance of each individual component of a land data assimilation system relative to a set of simplified linear regression models. A lack of significant improvement relative to these simplified models can be taken as evidence that a given component of the land data assimilation system is performing sub-optimally and/or can be replaced by a much simpler approach. In this way, the paper provides new insight into how complex land data assimilation systems can be improved to enhance agricultural drought monitoring

Technical Abstract: Despite considerable interest in the application of land surface data assimilation systems (LDAS) for agricultural drought applications, relatively little is known about the large-scale performance of such systems and, thus, the optimal methodological approach for implementing them. To address this need, this paper evaluates an LDAS for agricultural drought monitoring by benchmarking individual components of the system (i.e., a satellite soil moisture retrieval algorithm, a soil water balance model and a sequential data assimilation filter) against a series of linear models which perform the same function (i.e., have the same basic inputs/output) as the full component. Benchmarking is based on the calculation of the lagged rank cross-correlation between the normalized difference vegetation index (NDVI) and soil moisture estimates acquired for various components of the system. Lagged soil moisture/NDVI correlations obtained using individual LDAS components versus their linear analogs reveal the degree to which non-linearities and/or complexities contained within each component actually contribute to the performance of the LDAS system as a whole. Here, a particular system based on surface soil moisture retrievals from the Land Parameter Retrieval Model (LPRM), a two-layer Palmer soil water balance model and an Ensemble Kalman filter (EnKF) is benchmarked. Results suggest significant room for improvement in each component of the system