Presentation Type
Oral Presentation
Category
STEM (science, technology, engineering, mathematics)
Abstract/Artist Statement
Forests face accelerating threats due to increases in the severity and frequency of drought and heat stress associated with climate change. In particular, as rates of disturbance (e.g, wildfire) increase, and the climatological and hydrological suitability of landscapes for forest regeneration are diminished, vegetation shifts from forest to non-forest are expected to become more frequent and widespread. Most efforts at predicting these shifts have used correlative approaches which lack the ability to generalize to novel temporal or spatial conditions. To reduce uncertainty when predicting forest regeneration, researchers need mechanistic models and predictors of seedling mortality based on physiological processes and hydraulic function. Here, we introduce a novel environmental metric: potential land surface temperature (pLST), which is an estimate of land surface temperature in the hypothetical absence of overstory vegetation. Land surface temperature (LST) is a radiometric measure of the energy balance at the Earth’s surface: it is governed by net radiation and soil moisture, has strong effects on seedling physiology, and can be remotely sensed. Therefore, it is a key variable in many studies of how vegetation, hydrology, and climate interact. To develop pLST as a metric of forest regeneration suitability, we produce pLST estimates using a spatially explicit, physics-based ecohydrologic model and evaluate the skill of these estimates at predicting forest cover in watersheds distributed across the western U.S. We find that forest cover predictions based on modeled pLST can accurately reproduce satellite-measured patterns of forest cover throughout the western U.S, although the accuracy of these predictions is lower in the Southwest. This work leverages advances in ecohydrologic modeling and remote sensing, along with an easily observable and intuitive climate metric, to produce information about forest regeneration suitability that can be used by forest managers.
Mentor Name
Solomon Dobrowski
Developing potential land surface temperature as an indicator of forest regeneration potential in the western United States
Forests face accelerating threats due to increases in the severity and frequency of drought and heat stress associated with climate change. In particular, as rates of disturbance (e.g, wildfire) increase, and the climatological and hydrological suitability of landscapes for forest regeneration are diminished, vegetation shifts from forest to non-forest are expected to become more frequent and widespread. Most efforts at predicting these shifts have used correlative approaches which lack the ability to generalize to novel temporal or spatial conditions. To reduce uncertainty when predicting forest regeneration, researchers need mechanistic models and predictors of seedling mortality based on physiological processes and hydraulic function. Here, we introduce a novel environmental metric: potential land surface temperature (pLST), which is an estimate of land surface temperature in the hypothetical absence of overstory vegetation. Land surface temperature (LST) is a radiometric measure of the energy balance at the Earth’s surface: it is governed by net radiation and soil moisture, has strong effects on seedling physiology, and can be remotely sensed. Therefore, it is a key variable in many studies of how vegetation, hydrology, and climate interact. To develop pLST as a metric of forest regeneration suitability, we produce pLST estimates using a spatially explicit, physics-based ecohydrologic model and evaluate the skill of these estimates at predicting forest cover in watersheds distributed across the western U.S. We find that forest cover predictions based on modeled pLST can accurately reproduce satellite-measured patterns of forest cover throughout the western U.S, although the accuracy of these predictions is lower in the Southwest. This work leverages advances in ecohydrologic modeling and remote sensing, along with an easily observable and intuitive climate metric, to produce information about forest regeneration suitability that can be used by forest managers.