Submitted to: American Geophysical Union
Publication Type: Abstract Only
Publication Acceptance Date: 1/20/2009
Publication Date: 2/15/2009
Citation: Crow, W.T., Bolten, J.D., Ryu, D. 2009. The development of terrestrial water cycle applications for SMAP Soil Moisture Data Products [abstract]. American Geophysical Union. 89(53).
Technical Abstract: Soil moisture storage sits at the locus of the terrestrial water cycle and governs the relative partitioning of precipitation into various land surface flux components. Consequently, improved observational constraint of soil moisture variations should improve our ability to globally monitor the terrestrial water cycle. However, to date, most evidence for such enhancement has been based on synthetic studies and not actual data. The maturity of existing soil moisture datasets (from e.g. the NASA/JAXA AMRS-E and TMI satellite sensors) provides an opportunity to better describe this potential prior to the anticipated launch of the NASA SMAP mission. Using existing remotely-sensed soil moisture datasets, the presentation will demonstrate the potential for improving satellite-based rainfall accumulation products over land and describe a novel data assimilation strategy for leveraging improved rainfall products to enhance global runoff modeling. Despite well-known shortcomings in existing satellite soil moisture data sets (e.g. limited accuracy over vegetation and shallow vertical measurement depths), these strategies lead to measurable improvements in rainfall and runoff estimates over a large fraction of global continental areas. Realized benefits are most profound in lightly-vegetated areas amenable to satellite estimation of surface soil moisture and data-poor land areas lacking adequate ground-based instrumentation. The ability to enhance precipitation also allows for dual data assimilation strategies in which remotely-sensed soil moisture is used to simultaneously correct both the representation of antecedent soil moisture in a hydrologic model and the precipitation forcing applied to the model. Prospects for applying such a dual assimilation approach to data poor areas of Africa will be examined as will potential enhancements associated with the improved accuracy and resolution of SMAP soil moisture products relative to existing datasets.