Partition of daily evapotranspiration using stable water isotope method and a modified Shuttleworth-Wallace Model for urban forest area
Bibliographic record
Abstract
Quantification of the contribution of transpiration (T) to evapotranspiration (ET) is important to understand the impact of climate change on the hydrological cycle and guide precision irrigation. So far, few studies have examined seasonal variability of T/ET and its drivers under urban area. In this study, we applied a modified Shuttleworth-Wallace (S-W) model to partition ET for a locust tree forest in jinnan district of Tianjin city. The new model considers the impact of carbon dioxide emissions on vegetation transpiration and significantly improves the performance of the original S-W model. The Eddy Covariance (EC) and stable water isotope method was used to monitor and partition ET in locust tree forest. Isotope composition of ET (δET), soil evaporation (δE) and vegetation transpiration (δT) were determined using the Keeling-plot method, Craig-Gordon model and Steady-state assumption model (SSA), respectively. The verification result suggest the modified S-W model could provide reliable prediction for ET and its components. The modified S-W model estimated T/ET ranges from 0 to 1, with a near continuous increase over time in the early growing season when leaf area index (LAI) is small and then convergence towards a stable value when LAI is larger. The results show seasonal change in T/ET can be described well as a function of LAI, implying that LAI is a first order factor affecting ET partitioning, and soil moisture also influence the ET partitioning. This study reveals the change in T/ET patterns and its controlling factors in urban woodland areas. Understanding the impact of urbanization and human activities on the urban water cycle will allow more effective water use in urban environments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".