Impacts of Forest Fires and Climate Variability on the Hydrology of an Alpine Medium Sized Catchment in the Canadian Rocky Mountains
Bibliographic record
Abstract
This study investigates the hydrology of Castle River in the southern Canadian Rocky Mountains. Temperature and precipitation data are analyzed regarding a climate trend between 1960 and 2010 and a general warming is identified. Observed streamflow has been declining in reaction to a decreasing snow cover and increasing evapotranspiration. To simulate the hydrological processes in the watershed, the physically based hydrological model WaSiM (Water Balance Simulation Model) is applied. Calibration and validation provide very accurate results and also the observed declining runoff trend can be reproduced with a slightly differing inclination. Besides climate change induced runoff variations, the impact of a vast wildfire in 2003 is analyzed. To determine burned areas a remote sensing method of differenced burn ratios is applied using Landsat data. The results show good agreement compared to observed fire perimeter areas. The impacts of the wildfires are evident in observed runoff data. They also result in a distinct decrease in model efficiency if not considered via an adapted model parameterization, taking into account the modified land cover characteristics for the burned area. Results in this study reveal (i) the necessity to establish specific land cover classes for burned areas; (ii) the relevance of climate and land cover change on the hydrological response of the Castle River watershed; and (iii) the sensitivity of the hydrological model to accurately simulate the hydrological behavior under varying boundary conditions. By these means, the presented methodological approach is considered robust to implement a scenario simulations framework for projecting the impacts of future climate and land cover change in the vulnerable region of Alberta’s Rocky Mountains.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".