Improved Regional Scale Dynamic Evapotranspiration Estimation Under Changing Vegetation and Climate
Bibliographic record
Abstract
Abstract Vegetation change can significantly alter evapotranspiration (ET), an important component of the terrestrial water balance, and consequently influences other hydrological processes. With no direct measurement techniques available at large spatial scales, the accurate estimation of ET under changing forest landscapes and climate is challenging. In this study, we used an improved method based on Fuh's equation (a functional form of the Budyko framework) to investigate ET responses to cumulative forest disturbance and climate in the snow‐dominated interior of British Columbia, Canada. First, we divided the study region into three distinct climate groups, and then related the watershed parameter m in Fuh's equation to vegetation change (represented by cumulative equivalent clearcut area (CECA)) and watershed properties, with independent calibration and validation watersheds. The validated relationships were used to examine regional ET variations (∼380,000 km2). Our results showed that ET in moderate climates had the highest sensitivity to CECA, characterized by wetness index between 1 and 2 (the ratio of precipitation to potential evapotranspiration). ET in dry climates (wetness index <1) was also significantly related to CECA, but with reduced sensitivity compared to the moderate climates. Wetter climates (wetness index >2) did not show a significant relationship with CECA, suggesting an insensitivity of ET to forest change. Simulations under future climate and vegetation disturbance scenarios demonstrated that ET would be further decreased in most of the study region. Forest management should consider reducing disturbance in the dry to moderate climate areas to mitigate negative impacts on hydrological processes and functions.
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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 | 0.001 |
| 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) | 0.000 | 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 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".