Date of Award:

5-2021

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Civil and Environmental Engineering

Committee Chair(s)

Bethany Neilson (Committee Chair), Tianfang Xu (Committee Co-chair)

Committee

Bethany Neilson

Committee

Tianfang Xu

Committee

David Tarboton

Abstract

Snow-dominated, karst watersheds present particular challenges to accurately modeling streamflow in response to differing climate conditions. This is due to the uneven distribution of snow within a basin, the varied melting rates due to terrain and climate, and the difficulty determining flow paths through karst conduits below ground. One possible solution to these challenges is to model snow at a fine scale, but climate variables are not available at these smaller spatial scales. The choices about which climate dataset to use, and how to downscale the data to a fine scale, will likely affect the accuracy of streamflow simulations. This comprises the primary goal of the thesis. For this project we simulated fine resolution snow processes using two climate datasets downscaled in two different ways and used the simulated snowmelt to feed a deep learning model that can learn patterns between the simulated snowmelt and observed streamflow to then simulate streamflow. The climate datasets differ significantly and resulted in highly different patterns of snow accumulation and melt. However, the deep learning model was able to learn the patterns with the different datasets and accurately generate streamflow for all climate datasets and downscaling methods.

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