Project Description: 

The Sierra Nevada Mountains are increasingly impacted by both climate change and wildfires, both of which can significantly change snowpack water storage and downstream water availability. Water management practices involve predicting future water availability and using that information to issue water use permits. Understanding how snow responds to change is an important part of current and future sustainability. Specifically, forest disturbance from climate change and forest fires can impact snow accumulation and melt in mountainous regions in ways that are not sufficiently understood.

This project aims to use remote sensing data from multiple satellites and processing pipelines to better understand the impact of forest fires on snowpack. With a focus on recent megafires, including the 2020 Creek Fire. We will quantify trends in snowpack retention, melt duration, and forest water use in areas with different levels of fire disturbance. The focus on the Creek Fire also allows us to investigate new spatial patterns in forest disturbance from megafires and fires with very large, contiguous high-severity patches.

Undergraduate's Role: 

The student will develop an analysis of satellite-collected data of various hydrology and vegetation quantities to evaluate differences in the snow-forest relationship between burned and unburned forest areas in the Sierra Nevada and/or other mountainous domains (e.g., High Mountain Asia). The student will develop specific research questions in collaboration with a graduate student mentor and will be guided in learning the research process as well as remote sensing data analysis in Python. If the student is interested, there is also the possibility of developing a computational modeling and/or machine learning project as well.

Undergraduate's Qualifications: 

Interest in snow hydrology-forest interactions and remote sensing methods. Basic knowledge of Python or willingness to learn. Experience with coursework or research in environmental science, hydrology, remote sensing, and/or forestry.

On Campus
3-6 hours