Implementation of an upscaled probabilistic fill-and-spill method to simulate wetland-dominated landscapes
Bibliographic record
Abstract
Runoff characteristics of wetland-dominated landscapes are highly influenced by the heterogeneity of wetland properties such as contributing areas, local storage deficits, and degree of connection to the wetland network. Field observations indicate the fill-and-spill process is a common and controlling process in wetland-dominated landscapes in which wetlands receive water until a threshold is satisfied and then release water to the next water bodies in a basin. The upscaled probabilistic fill-and-spill algorithm provided here aims to take the understanding from observation of the fill-and-spill process in individual wetlands at local scales to estimate the response of thousands of wetlands to precipitation or snowmelt events at a larger scale. For this purpose, a novel derived distribution approach is implemented using the probabilistic characteristics of wetlands, i.e., deficit and concentrating factor probability distribution functions. The method proposed here is a generalization of the Probability Distributed Model (PDM) and Xinanjiang probabilistic runoff models with the inclusion of the effects of wetland contributing area and cascading networks. The analytical solution of this upscaled fill-and-spill processes has been compared with a Monte Carlo solution and then implemented in RAVEN, a semi distributed hydrological modelling framework. The accuracy and ability of the model simulation has been tested on ten watersheds in the Qu'Appelle River Basin in Saskatchewan, Canada. The promising simulation results obtained from manual and automated calibration shows strength of the proposed upscaled fill-and-spill algorithm in simulation of low gradient landscapes.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".