How does rainfall become runoff? A combined tracer and runoff transfer function approach
Bibliographic record
Abstract
Hydrographs are an enticing focus for hydrologic research: they are readily available hydrological data that integrate the variety of terrestrial runoff generation processes and upstream routing. Notwithstanding, new techniques to glean information from the hydrograph are lacking. After early approaches of graphically separating streamflow components, hydrograph separations in the past two decades have focused on tracers as a more objective means to separate the storm hydrograph. These tracer‐based methods provide process‐based information; however, their implicit assumptions limit their applicability and explanatory power. We present a new method for isotope hydrograph separation that integrates the instantaneous unit hydrograph and embraces the temporal variability of rainfall isotopic composition (one of the largest impediments to the standard use of isotopes as tracers). Our model computes transfer functions for event water and preevent water calculated from a time‐variable event water fraction. The transfer function hydrograph separation model (TRANSEP) provides coupled but constrained representations of transport and hydraulic transfer functions, overcoming limitations of other models. We illustrate the utility of TRANSEP by applying it to two rainfall events from a 17 ha catchment at Maimai in New Zealand, where 18O, rainfall, and runoff data were sampled with a high temporal resolution. We explore which runoff and tracer transfer function (exponential piston flow, gamma distribution, or two parallel linear reservoirs) gave the best results for the proposed model structure and for the example data set. Uncertainty analysis was used to determine if the parameters were identifiable and if the information available for applying TRANSEP was sufficient. The results of the best performing transfer function are considered in detail to identify model performance, illustrate individual event characteristics, and interpret runoff processes in the catchment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| 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".