11A.3 The Use of Microwave Vegetation Optical Depth for Studies of Plant Water Stress (Invited Presentation)

Thursday, 11 January 2018: 11:00 AM
Room 18A (ACC) (Austin, Texas)
Alexandra G. Konings, Stanford Univ., Stanford, CA; and M. Momen

Microwave radiometry-derived vegetation optical depth (VOD) is proportional to canopy water content. In this presentation, we will demonstrate and discuss potential uses of VOD data in understanding the effect of water stress on ecosystems and the coupled carbon and energy cycles. First, we introduce a new conceptual model to explain VOD dynamics as a function of leaf water potential and leaf area index. We validate this conceptual model directly against in-situ leaf water potential measurements at three ecosystems across the United States and indirectly using surface soil moisture estimates across the globe. We will further demonstrate a number of possible uses for VOD observations. Diurnal variations of VOD can be used to estimate the ecosystem-scale anisohydricitya measure of how rapidly or slowly leaf water potential declines along with soil water potential during drought We demonstrate that in agricultural areas, the isohydricity varies according to expected patterns based on predominant crop type. Similarly, in dry tropical regions, spatial patterns in isohydricity match those of plant productivity sensitivity to soil water availability from a new data-consistent model-data fusion system. A case study over North American grasslands is used to demonstrate that ecosystem-scale isohydricity affects the sensitivity of seasonal vegetation growth to aridity – with anisohydric ecosystems more than three times as sensitive to vapor pressure deficit as isohydric ones, and similarly for precipitation sensitivities. Because of the strong coupling between the water and carbon cycles in this region, we therefore expected water and energy cycles to be sensitive to isohydricity as well. The next generation of land surface models is slated to include plant hydraulics. The conceptual model introduced in this talk is a first step towards the use of VOD to constrain parameterization ofthese models and in data assimilation.
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