ABSTRACT:

This study evaluates the effects of a single-reservoir stabilization alternative on performance metrics within the Colorado River Simulation System (CRSS), such as outflows from Lake Powell and Lake Mead, water elevations, and storage. The experimental alternative focuses on stabilizing water elevation in Lake Powell while maintaining a minimum protection elevation in Lake Mead. The alternative is simulated across two elevations and uses three different hydrologies to assess impacts on reservoir storage and releases. The analysis aims to determine the efficacy of more aggressive strategies to improve drought resilience and the operational feasibility of single reservoir stabilization as a potential management approach for post-2026 operations for the Colorado River.

ABSTRACT:

Flood inundation mapping is an important tool in assessing risks and preparing for potential floods. This project is meant to assist in research that uses flow velocity at different stream stages to improve flood inundation mapping. A program is used to calculate velocity using cross-section elevations. The current process for getting cross-section elevations is done manually in ArcGIS Pro. The purpose of this project is to automate the workflow in ArcGIS Pro to reduce the time required and improve consistency between stream gage sites. The process to automate the workflow consists of writing a Python script to be used in ArcGIS Pro. The Python script uses APIs to retrieve the coordinates of the United States Geological Survey (USGS) stream gage, a digital elevation model (DEM) from OpenTopography, and a stream center line from the National Hydrography database to be used as inputs in the workflow. Next, the script uses existing functions in ArcGIS Pro to draw evenly spaced cross-sections upstream and downstream of the USGS stream gage and get the x-, y-, and z- coordinate data for the points in each cross-section. Lastly, the Python script outputs the data for each cross-section in one comma-separated values (.csv) file that can be saved locally or shared on GitHub. The .csv file is formatted to be ready to use in the next steps of the research project. The result of the automated workflow is a simplified, faster, and more reproducible data collection process to assist in the flood inundation research. The automated workflow produces similar results to previous cross-section data collected manually, allowing users to gather data more efficiently, giving more time for other important tasks.

ABSTRACT:

Data Analysis Final Report Python Code (Spring 2026)

Abstract: Dissolved oxygen (DO) is a critical indicator of water quality and aquatic habitat in rivers, and its variability is strongly influenced by temperature. This study investigates the relationship between air temperature and dissolved oxygen using time‑series data from the Logan River Observatory (LRO) from 2017 to 2023. This analysis focuses on temporal structure through regression diagnostics, smoothing, autocorrelation, cross‑correlation, and hypothesis testing. The results show a strong inverse relationship between air temperature and dissolved oxygen that is more concurrent rather than delayed, with distinct seasonal persistence in both variables. Residual and time‑series analyses further suggest that while a linear model captures the dominant relationship, nonlinear and seasonal structure remains. These findings highlight the importance of temporal context when interpreting the relationship between air temperature and dissolved oxygen in river systems.

ABSTRACT:

This is a participation assignment for CEE 6110 Hydroinformatics for the "Sharing a Reproducible Analysis in HydroShare" assignment. This resource includes the necessary data and Jupyter script for a learning practice script from CEE 6660 Environmental and Hydrologic Data Analysis and Experimentation. The dataset is a time series of Dissovled Oxygen observations in the Logan River spanning from 6/20/2014 13:00 - 11/1/2022 13:00 from the Logan River Observatory from a gage near Franklin Basin.