An Upscaled Model of Fill‐And‐Spill Hydrological Response
Bibliographic record
Abstract
Abstract The hydrology of wetland‐dominated landscapes is often controlled by a fill‐and‐spill mechanism, whereby surface depressions retain water and release it once a deficit is filled. The response of these systems to precipitation/snowmelt events is influenced by the local storage deficit and connectivity between storage features. To estimate runoff generated from a heterogeneous wetland complex, a closed‐form analytical upscaled probabilistic model is developed. The mathematical solution requires information on the distribution of initial deficits and wetland local contributing areas, which may be estimated via a combination of spatial analysis and field observation. The model is used to explore the influence of spatial heterogeneity of wetland properties including deficit depth, local contributing area, and cascade depth (the number of wetlands in‐series within a cascade) on runoff response. It is also used to clarify “gatekeeper” storage features role at large scales and for systems with shallow wetland cascade depths. The proposed solution is shown to be a generalization of the well‐known Probability Distributed Model and Xinanjiang runoff models, augmented to include information about local contributing areas and wetlands connectivity. The closed form probabilistic mathematical solution is verified by comparing results with Monte Carlo simulations. The proposed runoff model has been implemented in Raven, a hydrologic model, to test the method performance in lumped runoff simulation of wetland‐dominated basins influenced by fill‐and‐spill hydrology. This study can contribute to our understanding of wetland characteristics distribution on landscape hydrology, and compensate for insufficiently resolved elevation data in flat terrains where threshold criteria are hard to estimate.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".