Exploring Spatial and Temporal Differences Between High and Low Frequency Water Quality Data in Coastal Virginia

Brahmey, Emma, Environmental Sciences - Graduate School of Arts and Sciences, University of Virginia
Doney, Scott, AS-Environmental Sciences (ENVS), University of Virginia

In coastal Virginia, water quality variables such as temperature, salinity, dissolved oxygen (DO), chlorophyll-a (Chl), and apparent oxygen utilization (AOU) are important biogeochemical measures of water status. Accurate monitoring and modeling of these variables 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, I investigated the differences in the date and value of minimum/maximum observations as well as seasonal amplitudes for the respective water quality variable on spatial and temporal scales. High and low frequency monitoring sites provide contrasting cases for harmonic analysis and for investigation of storm impacts on coastal water quality anomalies. On both time and space scales, subsampled short term high frequency (4-6 years of 15 minute resolution) inland sites were compared to long term low frequency (30 years of quarterly sampled resolution) environmentally variable water quality sites on the Eastern Shore of Virginia. Strong seasonal patterns were observed, with all sites being dominated by the first harmonic for temperature, DO, and AOU, and a mix of first and second harmonic dominating for salinity and Chl. Long term changes in temperature, salinity, Chl, and AOU were found in many of the sites. Specifically at high frequency sites, simulated quarterly sampling was performed and error variability was calculated between the successive years of subsampled and full model values using logarithmic regression. Above 25 years was found to be the ideal time period of low frequency monitoring to limit the year to year variability in error, with 50 years reaching a plateau in this error. This analysis provided an understanding of baseline seasonal patterns as well as anomalies. Diurnal anomalies were examined in both magnitude and directional changes due to weather factors based on a storm and seasonal scale. Correlations with water quality variables were seen across all seasons and storm types, with temperature, salinity, DO, and Chl being the most frequent with water level anomalies and precipitation. These correlations related to disturbances 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.

MS (Master of Science)
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