Simulation of Evapotranspiration and Its Response to Plant Water and CO2 Transfer Dynamics
Bibliographic record
Abstract
Abstract Evapotranspiration (ET) is controlled by atmospheric demand, plant and soil hydraulic constraints, and the plant physiological activities that determine canopy resistance. This paper introduces a new ET scheme developed for the Ecological Assimilation of Land and Climate Observations (EALCO) model that integrates these controls into one dynamic system. This scheme is based on solving the governing equation system that represents the coupled canopy energy–water–CO2 transfer dynamics, where the canopy temperature Tc, plant water potential ψc, and leaf intercellular CO2 concentrations Ci are simultaneously obtained and used in ET calculations. Modeled ET was compared with eddy correlation flux measurement at a boreal aspen forest. Results showed that the correlation coefficient (R) between modeled and measured daily ET was greater than 0.96. The average absolute error was approximately 0.3 mm day−1. Modeled ET was generally higher than measured ET by 10%. This is consistent with the energy balance closure analyses from observations that showed that turbulent energy flux was frequently less than 90% of the total available energy. The effects of the plant CO2 and water transfer dynamics on ET simulations were investigated by running the model in two additional settings. These were 1) static Ci—where the ratio of Ci to atmospheric CO2 concentration was set to a constant value, and 2) static ψc—where the ψc was linearly related to soil water potential. The dynamic CO2 transfer scheme and the static Ci scheme produced relatively small differences in ET that mainly occurred at a subdaily time scale. Differences in ET produced using the dynamic water transfer scheme and the static ψc scheme depended on ecosystem water conditions and were more significant when the plant was under water stress. Ignoring the dynamic water transfer process in the model decreased the correlation coefficient between modeled and measured ET more significantly in drier years. This implies that the dynamic water transfer scheme is of more importance for ET estimates in arid or semiarid ecosystems.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".