Date of Award

Spring 5-9-2025

Degree Type

Thesis

Degree Name

Doctor of Philosophy (PhD)

Department

Earth Science

Committee Chair

Xiaobing Zhou,

First Advisor

Glenn Shaw

Second Advisor

Liping Jiang

Third Advisor

Raja Nagisetty

Fourth Advisor

Martha Apple

Abstract

Identification of new optimal hydropower locations provides an opportunity to improve mankind's green energy portfolio by developing new hydroelectric plants, thereby decreasing the carbon footprint. Preliminary analysis of locating the optimal hydropower sites is generally the first step towards construction of a new hydropower plant. In this dissertation, I developed a parsimonious, yet robust tool to perform the preliminary analysis to locate optimal hydropower locations in any watershed using the remotely sensed Digital Elevation Model (DEM). Simulating the snowmelt-derived streamflow is crucial for water resources management in snow-dominated watersheds. The Snowmelt Runoff Model (SRM) is a lumped-parameter, temperature-index model that enables simulation of snowmelt runoff driven by remotely sensed hydrological products. However, the SRM is currently applicable only for pristine watersheds, while more and more watersheds are now being regulated. I expanded the applicability of the SRM over regulated watersheds by integrating it with a parsimonious regulation-correction methodology to recover the natural streamflow otherwise altered by the reservoir operations. Finally, I developed an integrated snowmelt derived hydropower predicting model to perform the hydrological and hydropower predictions of these locations by using remotely sensed and in-situ observed hydrological and hydropower data. My area of research was the Morony watershed, which comprises headwaters of the Missouri River up to Great Falls, Montana. I developed a novel Parsimonious Multi-dimensional Moving Window (PMMW) algorithm to locate new optimal hydropower sites, and located 12 sites between Helena and Great Falls, Montana for potential Run-of-River (ROR) hydropower plants. Then I developed the Expanded SRM (E-SRM) by incorporating significant automation (multi-year batch-processing, nested iterator, and seasonal divider algorithms) for continuous multi-year hydrological predictions. I found that naturalizing the streamflow by regulation-correction provides significantly more accurate predictions from the E-SRM. In addition, I developed a Snowmelt Runoff Driven Hydropower Model (SRDHM) that predicts the hydropower from ROR hydropower plants by using remotely sensed and in-situ hydrological and hydropower data. I compared the hydropower prediction results between PMMW and SRDHM and determined that both models predicted nearly identical potential hydropower capacities at the 12 newly located hydropower plant sites, indicating the robustness of this methodology.

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