Water and Energy Budgets of Snow-Covered Montane Forests: Observations, Remote Sensing, and Modeling
type: CreativeWork
description: Montane forests provide essential freshwater resources to the local ecosystems and downstream communities worldwide. Climate change is altering the energy and water budgets of montane forests; however, the magnitude and consequences to water resources is uncertain. This dissertation seeks to understand the interconnection between energy and water budgets to improve hydrological predictions by fusing cutting-edge in-situ and remote sensing observations with energy-budget modeling. We first focus on the impacts of snow models structure on the uncertainty of snow behavior under future warmer climate using a multi-modeling approach. We illustrate that a range of reasonable model decision and parameter combinations can accurately predict historical snowpack dynamics, but do not agree on projecting future snowpack behavior. Secondly, we unravel mass and energy controls on snow disappearance date (SDD) in montane forests using multi-site lidar observations. We develop a new conceptual model that incorporates the role of topography and vegetation structure to predict differential SDD in open and under canopy locations. Finally, we explore the accuracy of remote sensing-based thermal imagery, and two source energy balance modeling to estimate evapotranspiration variations in a montane site. We show that remote sensing-derived ET may struggle to characterize ET in montane forests.
abstract: Montane forests provide essential freshwater resources to the local ecosystems and downstream communities worldwide. Climate change is altering the energy and water budgets of montane forests; however, the magnitude and consequences to water resources is uncertain. This dissertation seeks to understand the interconnection between energy and water budgets to improve hydrological predictions by fusing cutting-edge in-situ and remote sensing observations with energy-budget modeling. We first focus on the impacts of snow models structure on the uncertainty of snow behavior under future warmer climate using a multi-modeling approach. We illustrate that a range of reasonable model decision and parameter combinations can accurately predict historical snowpack dynamics, but do not agree on projecting future snowpack behavior. Secondly, we unravel mass and energy controls on snow disappearance date (SDD) in montane forests using multi-site lidar observations. We develop a new conceptual model that incorporates the role of topography and vegetation structure to predict differential SDD in open and under canopy locations. Finally, we explore the accuracy of remote sensing-based thermal imagery, and two source energy balance modeling to estimate evapotranspiration variations in a montane site. We show that remote sensing-derived ET may struggle to characterize ET in montane forests.
author: Safa, Hamideh
date published: 2022
date modified: 2026-07-27T21:46:41Z
provider: University of Nevada - Reno
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Nevada - Reno

PropertyValue
property ID: Handle
value: http://hdl.handle.net/11714/7695