CVEEN 6920 – Hydroinformatics
(On-going Spring 2026)

Term: Spring 2026

Tools: Python, Pandas, NumPy, Matplotlib, Jupyter Notebooks,
USGS NWIS data, hydrologic time series analysis

GitHub Link

 Overview

This civil engineering course focused on applying data science methods to water resources systems. I worked with real hydrologic datasets to analyze streamflow variability, reservoir behavior, and temporal trends across multiple scales. The emphasis was on reproducible workflows, quantitative analysis, and communicating hydrologic insight clearly.

What I worked on

• Retrieved and processed daily discharge data from USGS NWIS

• Performed temporal resampling (weekly, monthly, annual) using pandas

• Compared hydrologic behavior across regulated and unregulated basins

• Analyzed wet vs dry year variability

• Built reproducible Jupyter workflows for time series analysis

• Generated publication quality plots for interpretation and reporting

The problem

Raw hydrologic data is noisy and scale dependent. Daily discharge values alone do not reveal seasonal structure, interannual variability, or reservoir regulation impacts. Meaningful interpretation requires careful temporal aggregation, consistent indexing, and clear visualization.

My approach

• I treated the assignments like research projects rather than simple exercises.

• Standardized datetime indexing and ensured clean time series structures

• Subset stations to overlapping periods for valid comparison

• Used resampling methods to analyze flow behavior at multiple temporal scales

• Compared headwater and reservoir influenced systems to interpret regulation effects

• Documented code and analysis steps for full reproducibility

 Key contributions

• Built end to end workflows from raw CSV to analyzed and visualized results

• Applied time series resampling to reveal hydrologic patterns

• Quantified differences between wet and dry years

• Compared regulated vs natural flow regimes

• Produced clear visual outputs supporting interpretation

 Outcome

• Developed a strong foundation in hydroinformatics and hydrologic data science

• Strengthened ability to interpret streamflow dynamics across temporal scales

• Improved reproducible coding practices for environmental data

• Deepened understanding of how data analytics supports water resource decision making