Climate Change and Six Nations
Bibliographic record
Abstract
Warming climate will affect communities across Canada. Many of these communities do not have the adaptive capacity to deal with climate change related challenges. Indigenous communities are believed to be disproportionally affected by climate change because of the lack of adequate infrastructure, and historical and political obstacles that limit their overall adaptive capacity. Therefore, climate change data and information are required to understand the full extent to which these communities are exposed to climate risks. Many past studies in the literature have outlined the effects of climate change at large spatial scales. While these studies are important for understanding the broad effects of climate change, they are not useful for community or local adaptation planning. Ultimately, climate change impacts will be felt at a local level. Hence, high resolution climate change impact studies are urgently needed to capture the realities of these effects in greater detail and to provide relevant data and information at local and community levels, in particular for marginalized and Indigenous communities. Using observed meteorological and hydrologic data, high-resolution downscaled future climate simulations, and a process-based hydrologic model, this thesis explored the physical impacts of climate change on the Six Nations of the Grand River (Six Nations) reserve, which is the largest (by population) Indigenous community in Canada and the seventh largest in the United States and Canada. Changing climate conditions and extreme climate trends in the Six Nations reserve were explored using the widely used ETCCDI (Expert Team on Climate Change Detection and Indices) extreme climate indices. Results indicated a warming and wetting trend in Six Nations, with the temperature rising by 3°C to 6°C by the end of the century and changes in seasonal precipitation. Extreme high temperature and precipitation indices will increase, causing potential human health impacts and increased flooding hazards for the community. A warming climate directly impacts the hydrological cycle and patterns. Analysis conducted using the Coupled Groundwater and Surface-Water Flow Model (GSFLOW) found that the McKenzie Creek - an important water provider for Six Nations - is sensitive to climate change due to its reliance on precipitation. Furthermore, study results showed that winter precipitation and streamflow are projected to increase, and snowpack water content is expected to decrease. These changes in streamflow will cause earlier winter-spring flooding events. Furthermore, agricultural production may be affected by reduced spring soil moisture recharge. Additionally, GSFLOW projected little to no change in late spring and summer streamflow which resulted in low water availability (Ptot-ET) during the growing season. Water availability was further examined by assessing future Blue Water (BW) and Green Water (GW) scarcity in the McKenzie Creek watershed. The water footprint method was used to calculate BW and GW scarcity. Study results showed that under current levels of water usage, BW scarcity would be “low” in the future. However, BW scarcity would increase to “significant” levels in the future, if water users started to withdraw more water for consumption, assuming maximum water withdrawal allocation (i.e., 0.47 m3s-1). This level of BW scarcity has the potential to cause ecological degradation and exacerbate water quality issues in the McKenzie Creek watershed. GW scarcity showed a steadily increasing trend throughout the 21st century due to climate warming. Spatial analysis showed that the western portion of the McKenzie Creek watershed may experience slightly higher levels of GW water scarcity in the future because of the lower water holding capacity of the soil. This may cause water users to withdraw more BW resources in western upstream areas, thereby decreasing BW available for downstream communities, including the Six Nations. Such disparity in water use among Indigenous and non-Indigenous communities may affect community relationships and social cohesion in the area. This thesis provides decision makers in Six Nations and more broadly in the McKenzie Creek watershed area with relevant climate change impact data and information that can be used in future climate change adaptation planning, disaster risk mitigation, and water resources management. Moreover, the results highlight the need for a comprehensive climate change vulnerability assessment of the Six Nations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".