Water quality variables such as temperature, salinity, dissolved oxygen, chlorophyll-a, and apparent oxygen utilization are important biogeochemical measures of coastal water health, and accurate monitoring and modeling is vital in order to characterize the processes driving seasonal and geographic patterns. Using harmonic analysis, a method that fits sine and cosine functions to seasonally varying data, this project seeks to investigate the differences in harmonic elements (date and value of minimum/maximum and seasonal amplitude) on a spatial and temporal scale, specifically comparing subsampled short term high frequency (4-6 years of 15 minute resolution) to long term low frequency (30 years of quarterly sampled resolution) sites. At high frequency sites, error variability between successive years of simulated quarterly sampling are compared to full model values using novel methods of logarithmic regression. This information allows us to understand baseline seasonal patterns, and investigate deviations of those through anomalies. Diurnal anomalies are examined in both magnitude and directional changes due to weather factors based on a storm and seasonal scale. These correlations can inform us of potential disturbances in water quality that may become more severe and/or frequent as the amount and severity of storms and flooding increase due to global warming and sea level rise. This is a vital study due to the lack of modern research using harmonic analysis in coastal Virginia, as well as few papers regarding storm impacts on coastal water quality anomalies.

Primary Presenter: Emma Brahmey, University of Virginia (eb2vw@virginia.edu)

Authors:

Emma Brahmey, University of Virginia  (eb2vw@virginia.edu)

Scott Doney, University of Virginia  (scd5c@virginia.edu)