Graduate Studies

 

First Advisor

Tirthankar Roy

Degree Name

Doctor of Philosophy (Ph.D.)

Committee Members

George Hunt, Mark Svoboda, Tsegaye Tadesse

Department

Civil Engineering

Date of this Version

8-2025

Document Type

Dissertation

Citation

A dissertation presented to the Graduate College of the University of Nebraska in partial fulfillment of requirements for the degree of Doctor of Philosophy

Major: Civil Engineering (Water Resources)

Under the supervision of Professor Tirthankar Roy

Lincoln, Nebraska, August 2025

Comments

Copyright 2025, Sinan Rasiya Koya. Used by permission

Abstract

Snow is the largest natural reservoir on Earth. It accumulates water during the cold and dry months and releases it during warm and dry months. Societies, especially in the extratropical zone, heavily depend on snow as their freshwater resources, resulting in the massive economic value of snow. The recent changes in snowpacks and associated processes are concerning. We are susceptible to disruptions in snow processes, which can have disastrous implications. Yet the importance of snow is often not realized by a wider population. There are limited studies quantifying snow-related extremes, partly stemming from the limitations of existing methodologies.

This dissertation investigates different aspects of snow-related extreme monitoring by studying their mechanistic drivers, improving tools to detect them, and quantifying their impacts on water availability. Firstly, we study snow-induced flooding by implementing a prototype flood forecasting system for Nebraska using a distributed conceptual rainfall runoff model. We quantify the role of snow in flood generation, taking the historic 2019 flood in the Midwest as an example case. Secondly, we examine the hydrometeorological drivers of rain-on-snow events, a phenomenon that often leads to flash flooding. Using causal inference analysis on station measurements, we found the leading drivers of these events in North America. Thirdly, we developed a novel snow drought detection framework incorporating machine learning and information theory techniques. We validate the method on recorded events and reveal the major drivers utilizing the explainable nature of the framework. Finally, we quantify the impacts of snow droughts on the water availability in rivers worldwide. Our analysis highlights the rivers experiencing worsening snow droughts and the rivers highly susceptible to snow droughts.

The outcomes of these studies progress our current knowledge regarding snow-related extreme events. They also improve our ability to predict such events and respond to them efficiently. Additionally, our results aid efficient water resource management and policymaking, especially in snow-dependent regions. Overall, the outcomes achieve the overarching goal of this dissertation: to emphasize the significance of snow to humanity.

Advisor: Tirthankar Roy

Download

Share

COinS