Hydrological impacts of climate change predicted for an inland lake catchment in Ontario by using monthly water balance analyses
Bibliographic record
Abstract
Abstract Potential hydrological impacts of climate change on long‐term water balances were analysed for Harp Lake and its catchment. Harp Lake is located in the boreal ecozone of Ontario, Canada. Two climate change scenarios were used. One was based on extrapolation of long‐term trends of monthly temperature and precipitation from a 129‐year data record, and another was based on a Canadian general circulation model (GCM) predictions. A monthly water balance model was calibrated using 26 years of hydrological and meteorological data, and the model was used to calculate hydrological impact under two climate change scenarios. The first scenario with a warmer and wetter climate predicted a smaller magnitude of change than the second scenario. The first scenario showed an increase in evaporation each month, an increase in catchment runoff in summer, fall and winter, but a decrease in spring, resulting in a slight increase in lake level. Annual runoff and lake level would increase because the precipitation change overrides evaporation change. The second scenario with a warmer, drier climate predicted a greater change, and indicated that evaporation would increase each month, runoff would increase in many months, but would decrease in spring, causing the lake level to decrease slightly. Annual runoff and lake level would decrease because evaporation change overrides precipitation change. In both scenarios, the water balance changes in winter and spring are pronounced. Copyright © 2009 John Wiley & Sons, Ltd.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".