Stanford University
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ESS Oral Defense - Krishna Rao - May 13, 2022

When:
-
Where:
Y2E2 - 473 Via Ortega, Room 299
Audience:
Affiliates, Alumni, Everyone, Faculty, General Public, Members, Postdocs, Staff, Staff - Academic, Staff - Managers, Students, Students - Graduates, Students - Undergraduates

Stanford University

*** Ph.D. Thesis/ Oral Defense ***

 

Parched Plants to Flaming Forests: Understanding Tree Mortality and Wildfires with Microwave Remote Sensing of Vegetation Water Stress

Krishna Rao

Friday, May 13, 9:00 AM

Y2E2 299

Department of Earth System Science

Advisor: Dr. Alexandra Konings

 
Vegetation water stress is a key driver of several critical processes in the Earth system. The amount of water stress an ecosystem can tolerate affects stomatal closure and thus its ability to sequester carbon dioxide (including anthropogenic emissions), and transpire water vapour. High amounts of vegetation water stress can also deplete water stored in vegetation, leaving them flammable and vulnerable to wildfires. Further, prolonged water stress in vegetation can lead to hydraulic failure in the xylem, eventually leading to drought-driven mortality. Both mortality and wildfires can lead to permanent changes in vegetation cover, and also impact ecosystem services such as air and water quality, recreation, and timber. However, despite its importance, we lack accurate, real-time, scalable measurements of vegetation water stress.
 
In this dissertation, I first introduce three methods to estimate vegetation water stress with microwave observations ranging from plot-scale to global scale. I then show that vegetation water stress can predict drought-driven tree mortality in California, and wildfire hazard in the western US. In Chapter 1, I introduce a P-band side-facing radar measurement system to measure vegetation water stress in ~100 m scales. In Chapter 2, I derive a new method to estimate vegetation water stress using an X-band satellite radiometer, and show that it is the best predictor of drought-driven forest mortality in California as compared to all existing metrics. In Chapter 3, I design a physics-guided deep learning algorithm to map live fuel moisture content- the percentage of vegetation water per unit dry biomass across the western US using several satellite measurements, including a C-band synthetic aperture radar. Using the resulting live fuel moisture content maps, in Chapter 4, I show that plant sensitivity to water limitation regulates the rise in burned area under a warming climate. In chapter 5, I use the same maps to quantify the effect of live fuel moisture content on wildfire ignition probability using a causal inference framework. Together, this research provides the tools and data to estimate vegetation water stress across scales, and also shows its impacts on tree mortality and wildfire hazard.
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