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Comp Exam Presentation - Ahmad Hojatimalekshah

November 30 @ 11:00 am - 12:00 pm MST

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Title: How do forest canopies affect snow depth and extent, and how do these relationships differ across scales?

Abstract: Snowpack is one of the main freshwater supplies in mountainous regions. Understanding the role of different controls on snow properties (depth, distribution, and snow water equivalent (SWE)) and processes (accumulation and ablation) is important to predict stream flow. Snow processes vary in respect to the predominant local controls in different landscapes. In many mountainous landscapes, controls on snow properties and processes are highly correlated with vegetation properties. Quantifying the relationship between snow and vegetation can be achieved with physical, or the use of numerical models. The main purpose of physical and numerical models is to examine how well they can predict snow properties and model snow processes. Physical modeling approaches utilize vegetation as an input into physical governing equations in order to predict snow properties. In contrast, numerical models directly investigate the relation between vegetation metrics and snow, as well as finding effective vegetation covariates. In this review paper, I identify and discuss several of the mechanisms in which vegetation affects snow properties, and their physical and numerical representation. I also review how model inputs can vary by different scales. Finally, I explore retrieval of snow and vegetation properties used in snow models with remote sensing. This review shows that physical and numerical models can both underestimate and overestimate the role of vegetation on snow respectively. This is primarily due to the lack of high-resolution vegetation data and our limited knowledge of snow-vegetation physical relationships. In addition, vegetation metrics are highly correlated, which can make it difficult to use the proper metric in a model. The scale of influence is another issue that needs to be considered for selecting model inputs. A combination of waveform lidar, synthetic aperture radar (SAR), and machine learning show promise in overcoming these challenges.