Civil Engineering ETDs

Publication Date

Summer 8-1-2023


In the past decade, high-frequency water quality sondes have become more abundant in watersheds across North America and Europe and are gaining a foothold in Asia and South America. In this dissertation, three relevant topics associated with high-frequency data are investigated, i.e., the impact of winter’s precipitation on surface water quality and stream metabolism, the longitudinal propagation of wildfire disturbances through a fluvial network, and the use of machine learning with high-frequency data to estimate fluvial nutrient processing. First, we found that significant snow precipitation can cause surface water anoxia and declines in stream metabolism. Second, our data illustrate that fluvial water quality and metabolic activity degradation can propagate hundreds of kilometers downstream from a wildfire. Lastly, our work demonstrates that recurrent neural networks can outperform traditional regression methods when using atmospheric parameters to estimate nitrate uptake.


Fluvial, High-Frequency, Water Quality, Stream Metabolism

Document Type




Degree Name

Civil Engineering

Level of Degree


Department Name

Civil Engineering

First Committee Member (Chair)

Ricardo González-Pinzón

Second Committee Member

David Van Horn

Third Committee Member

José M. Cerrato

Fourth Committee Member

Laura J. Crossey

Available for download on Friday, August 01, 2025