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

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Past and Future Coastal Water Quality :/
Reminder of title:	Trend Testing and Predictive Modeling Using Regulatory Shellfish Sanitation Data.
Author:	Chazal, Natalie Anne.
Description:	1 online resource (82 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-01.
Contained By:	Masters Abstracts International85-01.
Subject:	Water quality. -
Online resource:	click for full text (PQDT)
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. - 1 online resource (82 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379871741Subjects--Topical Terms:

569474
Water quality.
Index Terms--Genre/Form:

554714
Electronic books.

Past and Future Coastal Water Quality : = Trend Testing and Predictive Modeling Using Regulatory Shellfish Sanitation Data.
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245 1 0 $a Past and Future Coastal Water Quality : $b Trend Testing and Predictive Modeling Using Regulatory Shellfish Sanitation Data.
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300 $a 1 online resource (82 pages)
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500 $a Source: Masters Abstracts International, Volume: 85-01.
500 $a Advisor: Nelson, Natalie;Leight, Andrew.
502 $a Thesis (M.Sc.)--North Carolina State University, 2023.
504 $a Includes bibliographical references
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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Water quality. $3 569474
650 4 $a Waterways. $3 1465175
650 4 $a Pathogens. $3 1413560
650 4 $a Precipitation. $3 1465176
650 4 $a Feces. $3 1465177
650 4 $a Fisheries. $3 1059992
650 4 $a Sanitation. $3 1434833
650 4 $a Watersheds. $3 679964
650 4 $a Estuaries. $3 676080
650 4 $a Bacteria. $3 600016
650 4 $a Shellfish. $3 1058895
650 4 $a Hydrology. $3 642066
650 4 $a Time series. $3 1413638
650 4 $a Nonpoint source pollution. $3 1465178
650 4 $a Salinity. $3 908831
650 4 $a Coasts. $3 893273
650 4 $a Water temperature. $3 1064030
650 4 $a Aquatic sciences. $3 1178821
650 4 $a Biological oceanography. $3 1178855
650 4 $a Hydrologic sciences. $3 1179176
650 4 $a Land use planning. $3 1182560
650 4 $a Water resources management. $3 1178858
655 7 $a Electronic books. $2 local $3 554714
690 $a 0792
690 $a 0416
690 $a 0388
690 $a 0536
690 $a 0595
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a North Carolina State University. $3 845424
773 0 $t Masters Abstracts International $g 85-01.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30516395 $z click for full text (PQDT)