ABSTRACT:

This HydroShare resource presents an integrated hydrologic analysis of future inflow scenarios to Lake Mead and their downstream implications for Salton Sea restoration efforts and water management strategies by the Imperial Irrigation District (IID). Three inflow scenarios, severe drought, moderate drought, and baseline/normal conditions, are developed based on historical records, regulated outflows from Lake Powell, and anticipated variability under climate stressors. Using Python-based statistical methods and time-series analysis, we assess year-to-year inflow variability, average annual flow trends, and potential deficits in water delivery commitments.

The study aims to inform adaptive management by exploring how upstream inflow variability to Lake Mead could cascade through the Colorado River system, affecting water availability for the IID and restoration inflows to the Salton Sea. Key performance indicators such as annual water delivery volumes, deviation from QSA thresholds, and potential for conservation transfers are evaluated under each scenario.

All code, datasets, and analytical scripts used in this study are included in this resource to ensure full reproducibility and transparency for stakeholders and researchers. The resource provides a framework that can be updated with new data or adapted for scenario planning in other parts of the Colorado River Basin.

ABSTRACT:

This project developed a comprehensive data management system designed to support collaborative groundwater research across institutions by establishing a centralized, structured database for hydrologic time series data. Built on the Observations Data Model (ODM), the system stores time series data and metadata in a relational SQLite database. Key project components included database construction, automation of data formatting and importation, development of analytical and visualization tools, and integration with ArcGIS for geospatial representation. The data import workflow standardizes and validates diverse .csv datasets by aligning them with ODM formatting. A Python-based module was created to facilitate data retrieval, analysis, visualization, and export, while an interactive map feature enables users to explore site-specific data availability. Additionally, a custom ArcGIS script was implemented to generate maps that incorporate stream networks, site locations, and watershed boundaries using DEMs from USGS sources. The system was tested using real-world datasets from groundwater wells and surface water gages across Utah, demonstrating its flexibility in handling diverse formats and parameters. The relational structure enabled efficient querying and visualization, and the developed tools promoted accessibility and alignment with FAIR principles.

ABSTRACT:

A Jupyter notebook presenting a workflow for using the "dataretrieval" package to access USGS data. This notebook is presented to fulfill the participation assignment associated with Lecture 27 for the Spring 2025 Hydroinformatics class taught at USU.