Relative Importance of Input Parameters in the Modeling of Soil Moisture Dynamics of Small Urban Areas
Bibliographic record
Abstract
Continuous-simulation water balance models may be used to study the soil moisture dynamics of small urban areas. These models require as input many soil-texture and land-use-related parameters. Difficulties encountered in determining the values of these input parameters warrant an investigation on their relative importance. In this study, a series of global sensitivity analyses were performed to evaluate the response of selected outputs from a continuous-simulation soil moisture model to variations of specified input parameters. Using randomly generated input parameter values representing various site conditions, the soil moisture model was run with meteorological data from Toronto, Ontario, Canada. Three output statistics, namely, average soil moisture, the standard deviation, and skewness of the output daily soil moisture distributions, were determined from each model run. Four types of sensitivity indices between the output statistics and the input parameters were calculated. Based on these sensitivity indices, it was concluded that the wilting and hygroscopic-point soil moisture levels and the soil moisture level below which plants start to endure water stress are the most important input parameters for all three output statistics. The relative importance of soil’s porosity, saturated conductivity, and the runoff curve number of the study area becomes greater and almost reaches the same level as the most important parameters when the skewness of the output daily soil moisture distributions is the output statistic of interest.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".