Control of coupling mass balance error in a process‐based numerical model of surface‐subsurface flow interaction
Bibliographic record
Abstract
Abstract A process‐based numerical model of integrated surface‐subsurface flow is analyzed in order to identify, track, and reduce the mass balance errors affiliated with the model's coupling scheme. The sources of coupling error include a surface‐subsurface grid interface that requires node‐to‐cell and cell‐to‐node interpolation of exchange fluxes and ponding heads, and a sequential iterative time matching procedure that includes a time lag in these same exchange terms. Based on numerical experiments carried out for two synthetic test cases and for a complex drainage basin in northern Italy, it is shown that the coupling mass balance error increases during the flood recession limb when the rate of change in the fluxes exchanged between the surface and subsurface is highest. A dimensionless index that quantifies the degree of coupling and a saturated area index are introduced to monitor the sensitivity of the model to coupling error. Error reduction is achieved through improvements to the heuristic procedure used to control and adapt the time step interval and to the interpolation algorithm used to pass exchange variables from nodes to cells. The analysis presented illustrates the trade‐offs between a flexible description of surface and subsurface flow processes and the numerical errors inherent in sequential iterative coupling with staggered nodal points at the land surface interface, and it reveals mitigation strategies that are applicable to all integrated models sharing this coupling and discretization approach.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".