Date of Award

Spring 2017

Project Type

Thesis

Program or Major

Civil Engineering

Degree Name

Master of Science

First Advisor

Adam G Hunsaker

Second Advisor

Jennifer M Jacobs

Third Advisor

Majid Ghayoomi

Abstract

Over the past fifty years’ global climate change has altered various environmental processes. Due to global climate change, mid-winter snowmelt is occurring more frequently throughout much of the world (Freudiger, Kohn, Stahl, & Weiler, 2014). The increasing frequency of these events is a relatively new phenomena and is challenging the effectiveness of current water resource management and flood forecasting best practices. Early snowmelt events are caused by a brief period of unusually high air temperature, high humidity, or rain-on-snow (Semmens, Ramage, Bartsch, & Liston, 2013). This research focuses on the detection of rain-on-snow events using remote sensing approaches to identify the frequency, extent, and magnitude of these events. Early snowmelt events, driven by rainfall with the presence of snow, are identified from The Dartmouth Flood Observatory archives. Passive microwave data from the AMSR-E and SSM/I satellite instruments are compared with MODIS imagery and field observations to assess the reliability of microwave observations to capture these events. Early snowmelt detection algorithms that use passive microwave retrievals for northern latitude areas, primarily Alaska and Canada, are evaluated in the continental United States. It was determined that regional climate differences, largely variations in winter air temperature, impact the interpretation and performance of microwave snow melt detection algorithms.

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