Identification of nonlinearity in rainfall‐flow response using data‐based mechanistic modeling
Bibliographic record
Abstract
Data‐based mechanistic (DBM) modeling is an established approach to time series model identification and estimation, which seeks model structures and parameters that are both statistically optimal and consistent with plausible mechanistic interpretations of the system. This paper describes the application of the DBM method to 10 min, relatively high precision, rainfall‐flow data, including observations of both surface flow and subsurface flow. For a generally wet winter period, the preferred surface flow model is nonlinear in flow generation and linear in routing, while the preferred subsurface flow model is linear in flow generation and nonlinear in routing. These models have mechanistic interpretations in terms of mass balance, hydrodynamics, and conceptual flow pathways. The four‐parameter surface and subsurface flow models explain 91% and 96% of the variance of the corresponding observations. Other plausible models were identified but were less parsimonious or were more reliant on prior perceptions. For a wet summer validation period, the models performed as well as in the calibration period; however, when a long dry spell was included, the performance deteriorated. It is speculated that this is because of complex wetting‐drying dynamics and potential nonstationarity of the soil properties that are not sufficiently revealed in the available data. Conceptual models informed by the DBM results matched the DBM model performance for subsurface flow but gave poorer performance for the more complex surface flow responses. It is concluded that the DBM method can identify nonlinearity in both flow generation and routing and provide conceptual insights that can go beyond prior expectations.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".