Conceptualizing lateral preferential flow and flow networks and simulating the effects on gauged and ungauged hillslopes
Bibliographic record
Abstract
One of the greatest challenges in the field of hillslope hydrology is conceptualizing and parameterizing the effects of lateral preferential flow. Our current physically based and conceptual models often ignore such behavior. However, for addressing issues of land use change, water quality, and other predictions where flow amount and components of flow are imperative, dominant runoff processes like preferential subsurface flow need to be accounted for in the model structure. This paper provides a new approach to formalize the qualitative yet complex explanation of preferential flow into a numerical model structure. We base our examples on field studies of the well‐studied Maimai watershed (New Zealand). We then use the model as a learning tool for improved clarity into the old water paradox and reasons for the seemingly contradictory findings of lateral preferential flow of old water where applied line sources of tracer appear very quickly in the stream following application. We evaluate the model with multiple criteria, including ability to capture flow, hydrograph composition, and tracer breakthrough. We generate output ensembles with different pipe network geometries for model calibration and validation analysis. Surprisingly, the range of runoff response among the ensembles is narrow, indicating insensitivity to specific pipe placement. Our new model structure shows that high transport velocities for artificial line source tracers can be reconciled with the dominance of preevent water during runoff events even when lateral pipe flow dominates response. The work suggests overall that preferential flow can be parameterized within a process‐based model structure via the structured dialog between experimentalist and modeler.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 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".