Impacts of Fire-Driven Vegetation Changes on Evapotranspiration in North American Boreal Forests
Bibliographic record
Abstract
A growing body of evidence suggests that land cover change could be impacting evapotranspiration (ET) trends more than the direct impacts of climate change (Dashti et al., 2022; Zhang et al., 2015). In boreal forests, one of the Earth’s most important carbon sinks (Kang et al., 2006), climate and disturbance-driven land cover change is progressing rapidly, threatening the resilience of conifer-dominated forests (Baltzer et al., 2021; Massey et al., 2023). The spread of deciduous trees and the changing structure of boreal forests likely have large implications for the magnitude and seasonality of ecosystem water fluxes (Young-Robertson et al., 2016). This study introduces a process-based terrestrial ET model for North American boreal forests that incorporates annual landcover (Wang et al., 2019) and estimates of vegetation characteristics derived from gap-filled optical satellite imagery (Moreno-Martinez et al., 2020) to achieve a 30-meter resolution and explicitly account for land cover change. The model was used to assess regional trends in ET and the effects of wildfire disturbance on rates of ET. Results demonstrate that ET is generally increasing across the interior boreal forests of Alaska and northwest Canada, with three quarters of the increase attributable to land cover change in recent decades. Rates of ET are also responsive to post-fire regeneration and related to corresponding trends in vegetative composition and structure. After wildfire events, ET was shown to surpass pre-fire rates after 25 years of vegetation recovery. This study provides further evidence that land cover change is driving shifts in the water cycle at higher latitudes and establishes a model for future research.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".