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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 #354405

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Global investigation of soil moisture and latent heat flux coupling strength

item LEI, FANGNI - US Department Of Agriculture (USDA)
item Crow, Wade
item HOLMES, T. - Nasa Goddard Institute For Space Studies
item HAIN, C. - Goddard Space Flight Center
item Anderson, Martha

Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/1/2018
Publication Date: 10/15/2018
Citation: Lei, F., Crow, W.T., Holmes, T., Hain, C., Anderson, M.C. 2018. Global investigation of soil moisture and latent heat flux coupling strength. Water Resources Research. 54:8196-8215.

Interpretive Summary: Accurately estimating the flux of water and energy between the land surface and the atmosphere is an important component of forecasting the onset and evolution of agricultural drought. However, such fluxes are notoriously difficult to measure and available flux estimates are plagued by high inherent uncertainty. This uncertainty, in turn, hampers our ability to understand how such fluxes are coupled to land surface states like soil moisture. This paper develops and applies a new mathematical technique for removing the impact of random measurement error from estimates of the correlation between surface soil moisture and surface evaporation. This technique allows us to utilize existing remotely sensed estimates of both soil moisture and surface evaporation to produce the first reliable, global-scale assessment of soil moisture/surface evaporation coupling strength and identify global regions where such coupling is poorly described in existing land surface models. Results from this analysis will eventually be used by the numerical weather prediction community to improve the representation of land/atmosphere feedbacks within land surface models and, thus, their ability to forecast agricultural drought

Technical Abstract: As a key variable in the climate system, soil moisture (SM) plays a central role in the earth’s terrestrial water, energy, and biogeochemical cycles through its coupling with surface latent heat flux (LH). SM/LH coupling also modulates the strength of feedback loops along the land-atmosphere interface. Despite the need to accurately represent this coupling in earth system models, we currently lack quantitative, observation-based, and unbiased estimates of SM/LH coupling strength. Here, we present a robust, global triple collocation approach to obtain the true coupling strength between surface SM and LH based on the simultaneous availability of SM and LH triplets acquired from multiple satellite remote sensing platforms and land surface models (LSMs). Results demonstrate that, relative to coupling strength estimates derived from SM and LH remote sensing datasets, the application of triple collocation generally enhances estimates of warm-season SM/LH coupling, especially in the western United States, the Sahel, central Asia and Australia. However, relative to triple collocation estimates, LSMs (still) over-predict SM/LH coupling strength along transitional climate regimes between wet and dry climates, such as the central Great Plains of North America, India and coastal Australia. Specific climate zones with either over- or under-coupled relation in LSMs are identified to focus the re-examination of LSM parameterizations on certain regions. Despite diverse predictions of SM/LH coupling strength in individual land surface models, triple-collocation-based coupling strength estimates are relatively robust to the selection of a particular LSM (to contribute SM and LH estimates to each triplet in the triple collocation analysis). This lack of sensitivity lends credence to the triple collocation results and suggests that assumptions underlying the approach are respected.