Is Forest Change Confounding the Influence of Climate on Canada's Reference Hydrometric Network?
Bibliographic record
Abstract
ABSTRACT Climate change is altering streamflow regimes with potential impacts to water resources, drinking water supply, and aquatic ecosystems. Monitoring hydrologic response to climate variability is crucial for informing effective management and adaptation strategies. In Canada, the Water Survey of Canada (WSC) maintains the Reference Hydrometric Basin Network (RHBN), a subset of gauged basins selected for their minimal human impacts and long streamflow records, which are routinely used for assessing hydrologic response to climate change. The WSC states that changes in forest cover, such as those caused by timber harvesting, were not considered in the selection of reference basins. However, changes in forest cover can have a profound influence on hydrologic processes and accounting for potential forest change could be important for identifying and drawing robust conclusions about climate change effects on streamflow regimes. We quantified the amount of stand‐replacing forest change for 454 RHBN watersheds across Canada during the period of 1984–2019 and analysed whether forest change could have influenced long‐term trends in annual water yield. Overall, these basins have seen an average change in percent forest cover of 9% and some basins have experienced up to a 50% change. For many basins included in the RHBN, variability and trends in annual water yield can be primarily accounted for by climate; however, we estimated that streamflow trends for as much as 15% of the watersheds may have been strongly influenced by forest change. Our results highlight that forest cover within many of the RHBN watersheds across Canada is more dynamic than was assumed when the reference basins were selected. These changes in forest cover may attenuate or amplify streamflow responses to climate change and must therefore be considered in tandem with climate change effects.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".