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Maggi Kraft Dissertation Proposal

October 21 @ 3:00 pm - 5:00 pm MDT

Maggi Kraft

Snow Accumulation and Melt Under Heterogeneous Vegetation Cover in Southern Idaho.

Mountain snowpack provides water for 60 million people in the western United States and approximately one-sixth of the world’s population. With a decreasing snowpack due to climate change, it is essential to quantify and predict snow accumulation and melt for current and future water resources. Snowmelt is dominated by shortwave and longwave radiation but challenging to estimate in heterogenous vegetation structure and terrain due to spatial and temporal variations in net radiation. The overall objective of the proposed research is to understand how vegetation influences the distribution of snowmelt at the plot and watershed scale. I will incorporate remote sensing sourced terrain, vegetation parameters, and snow distribution with ground measurements of the snow surface energy balance and snow depth to evaluate how vegetation structure and type influences snowmelt. I will model watershed scale snow water equivalent (SWE) and the snow surface energy balance. I will compare measured plot scale results with modeled watershed scale results in different vegetation types and structures, and assess what environmental parameters primarily influence snowmelt. Next, I will use modeled SWE in combination with MODIS fractional snow cover to develop snow depletion curves in different elevation zones, vegetation structures, and years. I will analyze the spatial and temporal variability of snow depletion and streamflow timing and evaluate changing patterns of snowmelt due to landcover or climate change. Lastly, I will compile sap flux, soil moisture, and NDVI data in the Dry Creek Watershed. I will analyze how vegetation growing season length, and productivity are related to snow distribution and what environmental parameters primarily influence vegetation productivity. To accomplish this, I will compare peak productivity, transpiration, and soil water availability to snowmelt timing, peak SWE, spring precipitation, and air temperature.

The proposed research will provide insight into the temporal and spatial variability of the snow surface energy balance in variable vegetation and terrain and subsequent snowmelt timing, streamflow, and vegetation water availability. Combining remote sensing data with ground measurements will highlight uncertainties when upscaling point measurements to the watershed scale and improve watershed scale snowmelt and streamflow prediction.

Advisor: Jim McNamara

Co-Advisors: HP Marshall, Alejandro Flores, Nancy Glenn

When: October 21, 2020
Time: 3:00 PM
Where: Zoom Meeting ID: 917 7513 1878