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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 #320081

Research Project: Leveraging Remote Sensing, Land Surface Modeling and Ground-based Observations ... Variables within Heterogeneous Agricultural Landscapes

Location: Hydrology and Remote Sensing Laboratory

Title: The impact of assumed error variances on surface soil moisture and snow depth hydrologic data assimilation

Author
item LU, HAISHN - Hohai University
item Crow, Wade
item ZHU, YONGHUA - Hohai University

Submitted to: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/1/2015
Publication Date: 10/27/2015
Citation: Lu, H., Crow, W.T., Zhu, Y. 2015. The impact of assumed error variances on surface soil moisture and snow depth hydrologic data assimilation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 8(11):5116-5129. doi: 10.1109/JSTARS.2015.2487740.

Interpretive Summary: Remotely-sensed surface soil moisture and snow depth information is extremely valuable for monitoring water resource availability for many agricultural regions. However, in order to be used most effectively, such information must be integrated into a hydrologic model to predict variables of direct interest to farmers and/or water resource managers (e.g., spring stream flow volumes, root-zone soil moisture content or crop water usage). Such remote sensing/model integration, in turn, requires detailed information concerning the amount of uncertainty in both the integrated remote sensing observations and the hydrologic model itself. This paper evaluates a new technique for providing such error information and applies it to the critical case of simultaneously-assimilating remotely-sensed soil moisture and snow coverage into a hydrologic model within the headwaters of a river system. Once applied in an operational water resource monitoring system, these techniques will improve our ability to anticipate extremes in water resource availability and mitigate their impact on agricultural production.

Technical Abstract: Accurate knowledge of antecedent soil moisture and snow depth conditions is often important for obtaining reliable hydrological simulations of stream flow. Data assimilation (DA) methods can be used to integrate remotely-sensed (RS) soil moisture and snow depth retrievals into a hydrology model and improve such simulations. In this paper, we examine the impact of assumed model and observation error variance on stream flow predictions obtained by assimilating RS soil moisture and snow depth data into a lumped hydrological model. The analysis is based on both synthetic and real data assimilation experiments conducted within the Tuotuo River watershed at the headwaters of the Yangtze River. Synthetic experiments demonstrate that, when the true model error variance is small, data assimilation is more sensitive to the overestimation of the error variance than to its underestimation. Conversely, if the true variance is large, data assimilation is sensitive to the assumed model error variance but not the underestimation of the observation error variance. Given this sensitivity, the maximum a posteriori estimation (MAP) method is applied to accurately estimate model and observation error variances. However, the utility of the MAP approach is limited somewhat by its equifinality with regards to various error statistics. In particular, MAP has relatively more difficulty in constraining observation error variances (versus model error variances).