Modelling Evapotranspiration and Flow Processes in the Shallow Subsurface with Application to Green Roof Technology
Bibliographic record
Abstract
Mathematical models in engineering play an important role in understanding and predicting the behaviour of a system. Three different mathematical models were developed in this study, with an increasing level of complexity in the simulation of water and heat transfer in the shallow subsurface. These developed models have been used to answer several questions relevant to the application of green roof (GR) technologies in storm water management and building energy consumption, and to non-isothermal processes in bare and vegetated soils. A simplistic modelling approach (lumped model) was implemented to study the performance of a GR system in storm water reduction in various Canadian climate conditions. In the light of enhancing the water retention performance of GR technologies in each climate, further study was conducted on the GR configuration design parameters including substrate depth, substrate porosity and vegetation types. To predict substrate temperature and evaporation rates in the shallow subsurface of bare soils, a mechanistic model (coupled liquid water, water vapour, and heat transfer model) was developed. The model was validated comprehensively against several measured field data from the GR test modules. The validated model was used to study the non-isothermal processes in the shallow subsurface with the objective of enhancing the modelling results and simplifying the model. The developed mechanistic model was updated to incorporate the impact of vegetation layer on non-isothermal subsurface processes. The new model was fully validated against measured data from the field (drainage rate, evapotranspiration rates, vegetation and substrate temperature), and then used to study the thermal and hydraulic performance of GR technologies associated with different design parameters including vegetation coverage, substrate thickness. The performance of GR with climate change, during rainfall and in a shaded area were investigated as well.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".