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Past and Future Coastal Water Quality: Trend Testing and Predictive Modeling Using Regulatory Shellfish Sanitation Data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Past and Future Coastal Water Quality: Trend Testing and Predictive Modeling Using Regulatory Shellfish Sanitation Data./
作者:	Chazal, Natalie Anne.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	82 p.
附註:	Source: Masters Abstracts International, Volume: 85-01.
Contained By:	Masters Abstracts International85-01.
標題:	Water quality. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30516395
ISBN:	9798379871741

Past and Future Coastal Water Quality: Trend Testing and Predictive Modeling Using Regulatory Shellfish Sanitation Data.
Chazal, Natalie Anne.

Past and Future Coastal Water Quality: Trend Testing and Predictive Modeling Using Regulatory Shellfish Sanitation Data. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 82 p.

Source: Masters Abstracts International, Volume: 85-01.

Thesis (M.Sc.)--North Carolina State University, 2023.

This item must not be sold to any third party vendors.

Water quality monitoring is essential for maintaining the health and safety of coastal communities. In states where shellfish are harvested for human consumption, Shellfish Sanitation programs implement long-term water quality monitoring strategies motivated by fishery specific water quality hazards. Fecal coliforms (FC) are specific indicators of potential pathogens in the water and can be concentrated in the tissues of bivalve shellfish. Regulatory agencies have been testing the waters for FCs since the early 1900s. While this data is generally used for day-to-day management, this thesis examined the application of these water quality datasets for both historical trend testing and short-term forecasting purposes.In the first study, we (1) analyzed spatiotemporal trends from multidecadal FC concentration observations collected by a shellfish sanitation program, (2) identified possible management and environmental drivers of FC trends, and (3) assessed the feasibility of using these monitoring data to infer long-term water quality dynamics. We evaluated trends in FC concentrations for a 20-year period (1999-2021) using data collected from spatially fixed sampling sites (n = 466) in North Carolina. Mann-Kendall trend testing along with the analysis of rates of change (Sen slope) were used to determine the degree and significance of trends in FC levels in relation to changes in environmental covariates. Trends were evaluated on full coast and smaller estuarine scales. While trends varied from site to site, findings indicated that shellfish sanitation data can be used for long-term water quality inference under certain management conditions. Further, corresponding salinity trends could be used to measure the extent of management-driven bias in FC observations collected in a particular area.In the second study, we (1) trained regression-based Random Forest models to predict FC concentrations using regulatory FC observations, (2) updated these models with forecasted rainfall inputs, and (3) identified key drivers of FC dynamics. We trained and tested Random Forest regression models for each of 5 major management areas along Florida's coast to predict mean FC concentrations across sampling stations with similar watershed characteristics. Potential predictors included rainfall, wind speed and direction, length of natural and modified waterways, land use and land cover, soil drainage, tidal stage, air temperature, river stage, and season. Using hold out testing data, our model performed with R2 values varied from 0.36 to 0.72 between the 5 management areas. Using variable importance scores, antecedent precipitation values were among the most important predictors. When the models were updated with the forecasted rainfall values, wind components became increasingly important to predicting FC concentrations on Florida's coast. Ultimately, these forecasts will enable resource managers and shellfish growers to plan ahead and maximize their resources as this industry expands.Combined, these two studies revealed how regulatory shellfish sanitation data can be leveraged for long-term water quality trend testing and short-term FC forecasting. Drivers of long and short term changes in FC concentrations were also revealed. The modeling performed in this thesis can be used by coastal water resource managers and shellfish farmers to optimize decision making processes that support coastal communities.

ISBN: 9798379871741Subjects--Topical Terms:

556913
Water quality.

Past and Future Coastal Water Quality: Trend Testing and Predictive Modeling Using Regulatory Shellfish Sanitation Data.
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500 $a Advisor: Nelson, Natalie;Leight, Andrew.
502 $a Thesis (M.Sc.)--North Carolina State University, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a Water quality monitoring is essential for maintaining the health and safety of coastal communities. In states where shellfish are harvested for human consumption, Shellfish Sanitation programs implement long-term water quality monitoring strategies motivated by fishery specific water quality hazards. Fecal coliforms (FC) are specific indicators of potential pathogens in the water and can be concentrated in the tissues of bivalve shellfish. Regulatory agencies have been testing the waters for FCs since the early 1900s. While this data is generally used for day-to-day management, this thesis examined the application of these water quality datasets for both historical trend testing and short-term forecasting purposes.In the first study, we (1) analyzed spatiotemporal trends from multidecadal FC concentration observations collected by a shellfish sanitation program, (2) identified possible management and environmental drivers of FC trends, and (3) assessed the feasibility of using these monitoring data to infer long-term water quality dynamics. We evaluated trends in FC concentrations for a 20-year period (1999-2021) using data collected from spatially fixed sampling sites (n = 466) in North Carolina. Mann-Kendall trend testing along with the analysis of rates of change (Sen slope) were used to determine the degree and significance of trends in FC levels in relation to changes in environmental covariates. Trends were evaluated on full coast and smaller estuarine scales. While trends varied from site to site, findings indicated that shellfish sanitation data can be used for long-term water quality inference under certain management conditions. Further, corresponding salinity trends could be used to measure the extent of management-driven bias in FC observations collected in a particular area.In the second study, we (1) trained regression-based Random Forest models to predict FC concentrations using regulatory FC observations, (2) updated these models with forecasted rainfall inputs, and (3) identified key drivers of FC dynamics. We trained and tested Random Forest regression models for each of 5 major management areas along Florida's coast to predict mean FC concentrations across sampling stations with similar watershed characteristics. Potential predictors included rainfall, wind speed and direction, length of natural and modified waterways, land use and land cover, soil drainage, tidal stage, air temperature, river stage, and season. Using hold out testing data, our model performed with R2 values varied from 0.36 to 0.72 between the 5 management areas. Using variable importance scores, antecedent precipitation values were among the most important predictors. When the models were updated with the forecasted rainfall values, wind components became increasingly important to predicting FC concentrations on Florida's coast. Ultimately, these forecasts will enable resource managers and shellfish growers to plan ahead and maximize their resources as this industry expands.Combined, these two studies revealed how regulatory shellfish sanitation data can be leveraged for long-term water quality trend testing and short-term FC forecasting. Drivers of long and short term changes in FC concentrations were also revealed. The modeling performed in this thesis can be used by coastal water resource managers and shellfish farmers to optimize decision making processes that support coastal communities.
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