Assessing the hydrologic and environmental impacts of climate change with an integrated groundwater and surface water model
Bibliographic record
Abstract
There has been increased public awareness of the potential impacts of climate change on water resources in the Spencer Creek Watershed in Hamilton. This study, commissioned by Hamilton Conservation Authority and the City of Hamilton was completed as a pilot study to evaluate not only potential impacts to surface water hydrology, but explore how an integrated model can provide a thorough assessment on those groundwater and surface water interactions relating to environmental flows and wetlands. The team for this project consisted of Matrix Solutions Inc., McMaster University, and the Ontario Climate Consortium (OCC). This pilot project received funding support from the Royal Bank of Canada (RBC) Blue Water Project, the City of Hamilton, Hamilton Conservation Authority, and Mitacs. 1. The study goals included the following: 2. Characterize local future climates and create future climate change scenarios with which to evaluate infrastructure and environmental vulnerabilities in the Hamilton area. 3. Apply possible future climates to an integrated groundwater and surface water model of the Spencer Creek watershed. 4. Compare baseline watershed and hydrologic and hydrogeologic conditions and future conditions against multiple impact indicators to assess vulnerabilities to climate change in the Spencer Creek watershed. 5. Recommend adaptation and monitoring measures to address the risks posed to the watershed by both current and future climate scenarios. Future projected climate changes include increases in mean annual temperature, maximum temperatures, growing degree-days, and evapotranspiration. In addition, the models predict potential increases in total annual precipitation, more days with substantial rainfall and more long duration events. All of these potential changes may affect the form and function of the environmental features in the watershed. The effect of these changes on environmental features may affect water levels and soil moisture in wetlands and warmer stream temperatures. These hydrologic changes may then result in shifts in wetland species and more habitat for invasive species. For coldwater streams, these changes could mean loss of brook trout habitat; and for forest habitats, vernal pools supporting amphibian reproduction may be lost. Warming and drying of forests may lead to shifts in vegetation and loss of rare species.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".