Measuring, modeling and assessing evaporative fluxes over an \n integrated lake-wetland system in Southern Quebec
Bibliographic record
Abstract
Evapotranspiration (ET) is the second largest component of the hydrological cycle globally and a major factor in surface energy balance. Despite its importance, quantifying evapotranspiration presents high uncertainties due to limitations in available data and modeling approaches. This thesis provides – for the first time – a temporally high-resolution ET dataset (30 minutes) using Eddy Covariance method for a lake-wetland duo in Southern Québec during 2020’s growing season, during the Covid-19 pandemic lockdown. In addition, this thesis benchmarks the performance of 40 existing ET models – the largest number of empirical ET models intercompared to date – across different time scales, times of the day and times of the season. The benchmarking effort uses a non-dominated sorting framework with multiple goodness-of-fit measures to rank models. In general, the most non-falsified models in the marsh are Carpenter (aerodynamic), McMillan (aerodynamic), Kimberley-Penman (combination) and Stephens-Stewart (temperature-hybrid). In the lake, Hamon’s (temperature) equation remains non-falsified across most scenarios. Comparing continuous simulations in the two landscapes, the expected Nash-Sutcliffe Efficiency of non-falsified models is consistently higher in the marsh across all timescales from half-hour to one month and different times of the season. Considering different diurnal segments, the performance of non-falsified models becomes comparable in daytime and strictly better in the lake during nighttime. ET was better estimated during daytime and nighttime separately than full days. Overestimation of ET was observed during local temperature peaks preceded by prolonged net radiation peaks without precipitation, which potentially points at models’ inability to capture the effect of stomata closure of the canopy during heatwaves. Capturing evapotranspiration in wetlands and lakes requires more physically-based parameterizations to represent thermal and biological dynamics at weekly and finer scales. This study also provides evidence for the necessity of using multi-objective ranking to benchmark evapotranspiration and points at strategic directions for future developments.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".