The coast is clear : data science and observational techniques to assess the health of coastal aquatic environments
Abstract/Contents
- Abstract
- Healthy coastal waters are critical for public well-being, thriving ocean economies, and functioning ecosystems. Yet threats to coastal health are ubiquitous: pollution, invasive species, overfishing, warming, and acidification harm much of the planet's coastlines and the human and natural communities in their proximity. In order to identify and mitigate these threats, it is important to keep a pulse on the state of coastal health over time by monitoring representative parameters. If anomalous measurements are observed, coastal managers (public health agencies, fishery managers, etc.) can take preventative or remediative action. In practice, technical and operational restrictions limit coastal monitoring to coarse spatial and temporal resolutions. This is an issue because the variability of environmental parameters of interest often occurs on finer spatial and temporal scales. As a result, public health and natural resource managers have limited information about the state of coastal health because sparse measurements are rarely representative of entire systems. While there has been an increase in coastal monitoring ability in recent times, translation of these growing environmental datasets into meaning and decision support is still lacking. To make coastal monitoring more useful, it is necessary to better understand how environmental drivers influence coastal health. Knowledge of how oceanic, hydrological, and meteorological processes modulate coastal health can be leveraged for better planning, monitoring, and management of coastal systems. Through a combination of observational and data science techniques, the studies documented in this dissertation elucidate how various environmental processes affect various facets of coastal health. Moreover, this dissertation provides insight into how these relationships can be leveraged to improve coastal health monitoring and management. Environmental processes are typically unsteady (i.e. time-variant) and indicators of coastal health can vary across multiple temporal scales. It is known that fecal indicator bacteria (FIB) -- a commonly-used proxy for water quality - can vary substantially at subhourly frequencies, yet little previous work had studied natural variation of FIB in enclosed (i.e. sheltered from the open ocean) beaches. Chapter 2 presents data from a high-frequency water quality sampling (HFS) event conducted over two days at a beach within a harbor. Results show that the temporal variability of FIB at enclosed beaches can be higher than at open beaches, and that environmental drivers such as chlorophyll concentration, turbidity, and tide level partially explain this dynamic. Chapter 3 leverages the HFS methodology to yield data sufficient to calibrate predictive beach water quality models. While such models have typically been developed at beaches where extensive historical FIB and environmental datasets exist, we show that effective models can be developed with data collected at HFS events. This study thus provides a new technique that can be used to rapidly develop predictive modeling systems for 'data-poor' beaches (which make up the majority of the planet's coastlines). Chapter 4 investigates the feasibility of beach water quality forecasting (as opposed to 'nowcasting' same-day water quality, the predominant technique used in operational prediction systems). We successfully develop a novel framework that can provide FIB forecasts at beaches with up to three days lead time. This framework can be applied to provide beach managers more time to allocate resources and beachgoers more time to make decisions on where to recreate based on health risk. Chapter 5 applies similar methodologies to a different facet of coastal health. We present a high-frequency, long-term environmental DNA (eDNA) dataset collected in a coastal California stream. Along with auxiliary environmental data, this eDNA dataset is used to model temporal trends in endangered fish abundance in the stream. Results show the efficacy of eDNA as a tool for long-term biodiversity monitoring. Overall, this dissertation provides a variety of techniques to gage the health of coastal systems and its environmental drivers. Results from these field-scale and modeling studies contribute to the growing field of environmental data science, providing tools to better translate environmental data into meaning and decision support.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Searcy, Ryan Thomas |
|---|---|
| Degree supervisor | Boehm, Alexandria |
| Thesis advisor | Boehm, Alexandria |
| Thesis advisor | Fletcher, Sarah (Sarah Marie) |
| Thesis advisor | Monismith, Stephen Gene |
| Degree committee member | Fletcher, Sarah (Sarah Marie) |
| Degree committee member | Monismith, Stephen Gene |
| Associated with | Stanford University, School of Engineering |
| Associated with | Stanford University, Civil & Environmental Engineering Department |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Ryan Thomas Searcy. |
|---|---|
| Note | Submitted to the Civil & Environmental Engineering Department. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/yj936cg2659 |
Access conditions
- Copyright
- © 2023 by Ryan Thomas Searcy
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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