Impact of Land Cover Change on Surface Water Temperature from 1985-2020 in Eastern Ontario
Bibliographic record
Abstract
Land Cover Change (LCC) has been shown to significantly impact the magnitude and trend of Land Surface Temperature (LST), locally where it occurs. Waterbodies serve as local climate moderators where nearby LCC has the potential to decrease their cooling ability. Water Cooling Islands (WCI) have demonstrated the ability to mitigate these local LST induced changes from LCC. Altered water surface temperatures can lead to altered species migration and distribution in aquatic species depending on a given species thermal boundary. Previous studies investigated the impact of these factors while only targeting either LCC impacts on LSTs or WCI impacts on LST, providing the opportunity to explore the relationship between LCC and WCI temperature trends. In this study, we investigate the role that LCC around small lakes(500m) plays on the surface water temperature trends in the Cataraqui Region Conservation Authority’s watershed, located in Eastern Ontario from 1985 -2020. The Continuous Change Detection Classification (CCDC) algorithm was used alongside the Statistical Mono-Window (SMW) algorithm to calculate LCC and LST, respectively. Results indicated a strong positive relationship (R2 = 0.81) between LCC and water temperature trends, where water temperature trends in all lakes investigated were found to be positive. The land cover type with the strongest correlation of land cover types investigated was with water temperature trends was impervious surfaces which had a medium positive relationship (r = 0.57). Individual lake size was found to have a weak positive relationship (R^2= 0.23) with water temperature trend. This 35-year study contributes to the broader understanding of water cooling islands and the impact of LCC has on surface water temperature trends.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 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 teacher head, 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".