Identifying Biophysical Drivers of Evapotranspiration for Forest Cover in a Mountainous Region

Abstract

Evapotranspiration (ET) is critical for understanding the impacts of climate change and land-use/land-cover change on water availability, ecosystem health, and agricultural productivity. However, point-based, field-measured ET data often lacks sufficient spatial and temporal coverage, especially in complex mountainous terrains such as the Appalachian Mountains. Consequently, characterizing ET rates across diverse land cover types and changing climate conditions remains challenging. This study uses remote sensing-derived ET data from the METRIC model for four selected watersheds in Virginia's Appalachian Mountains. Landsat-derived ET data with a 30-meter resolution spanning from 2015 to 2020 were obtained through the Earth Engine Evapotranspiration Flux (EEFLUX) platform on Google Earth Engine. Using supplementary GridMET reference evapotranspiration (ETr) data, temporal interpolation methods were applied to generate pixel-level daily ET profiles for the entire study area. The main objectives included comparing ET rates across land cover types from the National Land Cover Database (NLCD) and quantifying relative differences among land covers. Within forested land covers specifically, I further examined how topographic, soil, and vegetative factors influence ET variability. Generalized Least Squares and Random Forest models were employed to assess the relationships between selected biophysical variables and ET, highlighting both linear modeling with correlated error structures and the identification of non-linear patterns. Results from both models highlighted the significant roles of aspect, slope, and tree canopy cover in influencing ET variability, providing valuable insights into landscape-scale hydrological processes. Additionally, these models can be used to potentially fill gaps in ET estimates when satellite-derived data are limited due to cloud cover or other data availability constraints.