Evapotranspiration in the Meso‐Scale Forested Watersheds in Minjiang Valley, West China<sup>1</sup>
Bibliographic record
Abstract
Abstract: Information on evapotranspiration (ET) can help us understand water balance, particularly in forested watersheds. Previous studies in China show that ET was relatively low (30‐40% of total precipitation) in the Minjiang Valley located in the upper reach of the Yangtze River Basin. However, this conclusion was derived from research on small‐scale watersheds (<100 km2). The objective of this paper was to present ET information on meso‐scale watersheds in the Minjiang Valley. Four meso‐scale watersheds (1,700‐5,600 km2) located in the Minjiang Valley were used to estimate ET using the water balance approach. We first generated forest vegetation variables (coniferous forest percentage, forest cover percentage, and derived forest vegetation index) using remote sensing data. Landsat 5 TM satellite images, acquired on June 26, 1994, were selected for the vegetation classification. Actual annual ET was calculated based on 11‐year estimated precipitation and measured streamflow data (1992‐2002). We also calculated potential ET (PET) using an improved Thornthwaite model for all four watersheds for the period of 1992‐1998. PET can provide additional information about potential capacity of water flux to atmosphere in the region. Seasonal (dry and rainy) PET and ET for all studied watersheds were also estimated for comparison purposes as the water balance approach, at shorter than annual scales, would likely provide inaccurate estimates of ET. The dominant vegetations in the Minjiang Valley were grasslands, conifer forests, and shrub‐lands. Our results confirmed that both ET and PET for three studied meso‐scale watersheds in the Minjiang Valley is relatively low (39.5‐43.8 and 28.2‐47.7% for ET and PET, respectively), with an exception of ET in the Yuzixi watershed being 71.1%. This result is generally consistent with previous research at small watershed scales. Furthermore, the low ET across various scales in the Minjiang Valley may be related to the unique deeply cut valley environment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".