Streamflow response to headwater reforestation in the Ganaraska River basin, southern Ontario, Canada
Bibliographic record
Abstract
Abstract Increased attention is being paid to the role of reforestation in strategies for sustainable forest management, landscape restoration and carbon sequestration. Reforestation of drainage basins is generally assumed to reduce annual streamflow as well as peak and low flows. However, most studies of reforestation effects on streamflow have been conducted on small experimental basins, and the applicability of their results to larger basins is unclear. This study revisits an earlier examination of streamflow response to headwater reforestation in a 267 km 2 basin (Ganaraska River) in southern Ontario, Canada for 1945–2007. Forest cover in the basin headwaters increased from 13 km 2 in 1945 to 31 km 2 by 1990, with most of this change between 1950 and 1965. Streamflow metrics from the reforested basin and two headwater sub‐basins were compared to those from a nearby basin and sub‐basin of similar size and physiography to the Ganaraska basins but without extensive headwater reforestation. No temporal trends were found for inter‐basin differences in annual runoff or runoff ratios for the entire Ganaraska basin or its largest sub‐basin; however, reforestation appears to have reduced several metrics of peak streamflow at the basin and sub‐basin scale. Relationships between high flows classified according to generating event type and the associated precipitation depth suggest that expansion of forest cover in the Ganaraska River basin (GRB) and associated changes in microclimatic conditions have reduced the potential for frozen soil to generate surface runoff and high flows in response to rain‐on‐snow during spring snowmelt. This was accompanied by increased low flows from the GRB and its sub‐basins from 1960 to 2007 relative to the control basin, suggesting that the expanded forest cover has enhanced groundwater recharge by prolonging spring snowmelt and promoting infiltration of rainfall and snowmelt inputs relative to non‐forested areas. Some discrepancies between these results and those of other studies of streamflow response to reforestation may be attributed to the relatively large size of the GRB and the limited extent of reforestation. Nevertheless, the results highlight the importance of considering how reforestation may affect key hydrologic processes in a given landscape when predicting the resulting streamflow response. Copyright © 2011 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) | 0.002 | 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 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".