Understanding controls on springtime evapotranspiration from jack pine (Pinus Banksiana) forest with seasonally frozen soils
Bibliographic record
Abstract
The exchanges of water energy and carbon between the land surface and the atmosphere are tightly coupled, so that errors in simulating evapotranspiration will lead to errors in simulating the water, carbon and energy cycles. This will impair water resource evaluations and numerical weather predictions. Currently, land surface schemes have shown deficiencies in the simulation of evapotranspiration at the southern edge of the boreal forest in Saskatchewan, Canada. The purpose of this research is to improve the understanding of controls on evapotranspiration from forest canopies in regions with seasonally frozen soils, by critically examining field observations and outputs from state–of–the–art models. Simulated evapotranspiration is sensitive to soil and vegetation properties, which are variable in time and space and therefore introduce large uncertainties. Seasonally frozen soils present a particular challenge due to their snowmelt–dominated hydrology and the impact of soil freezing on the soil hydraulic properties and plant root water uptake. This thesis critically assesses the performance of the Canadian Land Surface Scheme (CLASS) and the coupled Canadian Land Surface Scheme and Canadian Terrestrial Ecosystem Model (CLASS–CTEM) for simulating point–scale evapotranspiration at a mature jack pine site located at the southern edge of the boreal forest in Saskatchewan, Canada. Past models applied to this site have consistently over–predicted evapotranspiration, particularly in the period following snowmelt. This research applies sensitivity analysis to explore how evapotranspiration is controlled by soil hydraulic and plant properties (soil water retention and conductivity, root depth/distribution, leaf area index, and the canopy conductance model), with special focus on the spring melt period when transpiration commences. This investigation found that errors in the soil hydraulic properties, root distribution, or leaf area index could not individually explain the model errors although these properties do all have important impacts on the water balance. The parameterization of canopy conductance could potentially explain the model errors. Although canopy conductance and leaf area index are dependent, the bias in simulation of evapotranspiration cannot be explained by the errors in leaf area index – given the fact that at this site we have site specific estimates of leaf area index. Errors in the simulation of evapotranspiration were greatest during and just after the soil–thaw period in spring. It is recommended, therefore, to further investigate the simulation of evapotranspiration from frozen soils.
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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 | 0.000 |
| 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".