Emerging Understanding
Climate Change and Water Governance Challenges in the Great Lakes Region: The Value of Baseline Knowledge and Indicators for Resilience Responses
Paper Presentation in a Themed Session Carolyn Johns, Amanda Shankland
As one of North America's most significant transboundary water regions, the Great Lakes face significant climate-related water challenges at various scales. This paper provides a historical and current overview of the climate change and water governance challenges in the Lake Ontario region, outlines the climate and water changes anticipated by climate scientists, and focuses on how community-based responses can improve governance frameworks for building climate change readiness and resilience. An adapted version of the Baseline Resilience Indicators for Communities (BRIC) framework is used, including a broader range of environmental, political and social factors and both quantitative and qualitative data at the regional and community scales to outline the significance of baseline research related to resilience challenges and responses. The paper uses the Lake Ontario region as an illustrative case study of the need to integrate technical, political and social dimensions at various scales to address challenges at the climate change and water change interface. The paper outlines how this modified framework provides a foundation for understanding how baseline research, inclusive knowledge generation, and community engagement can enhance governance and policy transformations.
Assessing Climate Vulnerabilities in the Western Upper Peninsula: Ecosystem Impact, Social Vulnerabilities and Policy Responses
Paper Presentation in a Themed Session Mercedes Asamani
The Western Upper Peninsula (WUP) of Michigan is one of the places in the world currently experiencing the impacts of climate change. This study uses a systematic content analysis of secondary data sources, such as scholarly publications, official reports, and climate projections, to investigate the WUP's vulnerability to climate impacts. The paper evaluates several sectors, including ecosystems, public health, and infrastructure, to identify critical risk areas and investigate how these vulnerabilities relate to social and economic issues. The findings reveal the variety of climate threats that the WUP faces, especially regarding public health, energy systems, and resilient infrastructure. The socioeconomic vulnerabilities of the area, such as an aging population and a dependence on extractive industries, further exacerbate the problems brought on by climate change. The study recommends that regional adaptation strategies incorporate renewable energy transitions, sustainable development practices, and active community engagement to address these vulnerabilities.
Forecasting Climate Change Impacts on Tibetan Plateau Lakes Using Remote Sensing, SAR Altimetry, and Deep Learning
Paper Presentation in a Themed Session Atefeh Gholami
The Tibetan Plateau holds 57.2% of China's lake area and serves as a vital freshwater source for millions in Asia. Its remote lakes, minimally impacted by direct human activity, are sensitive indicators of climate change, with accelerated glacial melt and shifting precipitation patterns posing significant threats to water resources. This study integrates Sentinel-3A satellite altimetry with the Subwaveform Retracking Method and ERA5 climate models to track historical lake level changes. To predict future hydrological shifts, we developed deep learning neural network models for each lake, utilizing CMIP6 climate projections (RCP 4.5 and 8.5) and regional climate data. These neural networks allowed us to generate accurate, lake-specific forecasts of water level dynamics through the 21st century. Key findings reveal that 9 out of 10 lakes exhibit upward trends in water levels, with an average increase of approximately 0.3569 m/yr. The most significant rises were observed in Migriggyangzham Lake (+0.5259 m/yr) and Lexie Wudan Lake (+0.4895 m/yr), while Langacuo Lake showed a decline (-0.2404 m/year). The findings underscore the role of runoff from glacial melt as a consistent driver of rising water levels in northern lakes, whereas eastern lakes are mainly influenced by precipitation and runoff. In contrast, western lakes show higher sensitivity to temperature-induced glacial melt. This is the first large-scale application of deep learning to predict future lake levels on the Tibetan Plateau. By integrating historical and future climate data, this study provides key insights for water resource management and policy development to mitigate the impacts of climate change.
