Hydrological Modelling to Inform Forest Management: Moving Beyond Equivalent Clearcut Area
Bibliographic record
Abstract
Forest disturbance can alter the hydrologic conditions of a watershed, including the frequency, magnitude, and timing of peak and low flows. Equivalent Clearcut Area (ECA) has been routinely used in watershed assessments to estimate hydrologic alteration due to forest disturbance. ECA analyses typically rely on broad regional assumptions, qualitative observations, and/or expert judgement, making it difficult to provide accurate quantitative estimates of hydrologic change. Process-based hydrological models offer an improved approach since they replicate watershed processes, can simulate land cover and climate change scenarios, and provide quantitative estimates of hydrologic change, including at ungauged points of interest. A workflow using a regionally calibrated hydrological model to investigate forest disturbance and future climate change scenarios is demonstrated. Results are contrasted with ECA-based outputs and emphasize that in addition to the amount of forest disturbance, watershed physical characteristics and the location of disturbance within a watershed influence the hydrologic response. This approach provides forest managers with quantitative outputs that support risk-based forest management decisions and presents a substantial improvement over ECA-based methods. Keywords: hydrological modelling, ECA-based analysis, watershed assessment, forest disturbance, cumulative effects, risk-based forest management
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".