Alignment of tree phenology and climate seasonality influences the runoff response to forest cover loss
Bibliographic record
Abstract
Abstract Trees shape the critical zone and modulate terrestrial water storage yet observed streamflow responses to forest cover change vary. Differences in catchment area, soil water storage, management practices, tree species, and climate are among the many explanations proposed for heterogeneous hydrologic responses. We addressed evidence for the hypothesis that mean annual temperature (MAT) and the phase shift between precipitation and enhanced vegetation index (EVI) peaks, θ, explain a significant amount of the variation in hydrologic response to forest cover loss. We selected 50 catchments with daily streamflow records spanning eight nations and seven climate regions. Categorical clustering of catchments was performed with MAT, θ, minimum EVI, catchment area, and percentage forest loss. Similar storm event runoff ratio responses to deforestation were best clustered by MAT and θ. High MAT tropical monsoonal catchments (Brazil, Myanmar, and Liberia) exhibited minimal evidence of increasing runoff ratios (increases observed in 9% of catchments). Low MAT subarctic, cold semi-arid, and humid continental catchments (US, Canada, and Estonia) showed consistent runoff increases around the time of snowmelt (94%). The deforestation runoff responses of temperate and subtropical catchments with Mediterranean, humid, and oceanic climates depended strongly on θ. We observe increased runoff following forest loss in a majority of catchments (90%) where precipitation peaks followed peak growing season (max EVI) (US). In contrast, where precipitation peaks preceded the growing season (South Africa and Australia) there was less evidence of increased runoff (25% of catchments). This research supports the strategic implementation of native forest conservation or restoration for simultaneously mitigating the effects of global climate change and regional or local surface runoff.
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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 | 0.000 |
| 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) | 0.001 | 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".