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:

This project presents a Python script that is designed to retrieve, process, and visualize streamflow data from the United States Geological Survey's National Water Information System. Using the dataretrieval and nwis packages, the code extracts daily streamflow records for a selected gaging site and employs pandas and matplotlib to generate a series of plots for hydrologic analysis. One plot focuses on the annual maximum, mean, and minimum streamflow for each year over the selected date range. Another plot shows the 95th, 75th, 25th, and 5th percentile values for each day of the year over the selected time period. The final plot displays the maximum, mean and minimum streamflow for each day of the year. A specific year can be added to compare the streamflow during that year to the historical values.