From people to protected areas : how disaggregated, high resolution data expose valuable climate and environmental insights
Abstract/Contents
- Abstract
- As climate change presents an increasing threat to living systems globally, research that seeks to understand regional vulnerabilities at spatial scales that impact people and ecosystems has been increasingly valuable for understanding climate risks. This can range from understanding spatial variability in public health risks to representing and estimating the exposure of biodiverse regions to future changes in climate extremes. Robust assessment of climate impacts relies on analysis of historical, present, and future climate across diverse spatial scales. In this dissertation I develop novel methods for integrating high-resolution datasets to characterize and analyze human-climate systems. These analyses were made possible by several diverse datasets that include earth observations, socio-economic data, geo-spatial data, and global climate model simulations. In Chapter 2, I uncover patterns in local, daily mobility in the San Francisco Bay Area during the COVID-19 pandemic and develop a model to establish a causal relationship between temperature and human mobility. In Chapter 3, I present a novel method for calculating global progress towards protecting biodiversity based on local-scale criteria. I do so by creating a robust, reproducible approach, and I show that calculation and aggregation decisions can drastically change reported values. In Chapter 4, I build on this new methodology by combining measures of future extreme climate events with biodiversity protection progress data. Through this project I quantify the spatial variation in extreme climate exposures across important biodiversity areas in South Africa. I also present a strategy for identifying biodiverse areas at high risk of near-term climate shocks. Through this analysis, we discover a substantial gap between areas with a climate threatened designation and areas with higher relative exposures to extreme climate. Collectively, this set of research can provide insight into the advantages of integrating diverse datasets to answer questions critical to understanding climate impacts and risk, and the impact of spatial scale on data interpretation. .
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 | Ly, Amina |
|---|---|
| Degree supervisor | Diffenbaugh, Noah S |
| Thesis advisor | Diffenbaugh, Noah S |
| Thesis advisor | Burke, Marshall |
| Thesis advisor | Lambin, Eric F |
| Thesis advisor | Wong-Parodi, Gabrielle |
| Degree committee member | Burke, Marshall |
| Degree committee member | Lambin, Eric F |
| Degree committee member | Wong-Parodi, Gabrielle |
| Associated with | Stanford Doerr School of Sustainability |
| Associated with | Stanford University, Department of Earth System Science |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Amina Ly. |
|---|---|
| Note | Submitted to the Department of Earth System Science. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/hd204xg8909 |
Access conditions
- Copyright
- © 2023 by Amina Ly
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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