Scaling relationships for event water contributions and transit times in small‐forested catchments in Eastern Quebec
Bibliographic record
Abstract
Recent studies of catchment hydrologic response are incorporating increasingly complex datasets to investigate model representation of spatial and temporal variability. In this paper, catchment rainfall‐runoff and stable isotope tracer response were modeled using a lumped conceptual model that integrates the unit hydrograph and isotope hydrograph separation methodologies. The model was applied across eight nested catchments (7 to 147 ha) for four rainstorms collected between summer and fall in 2001–2002, generating a usable 23 rainstorm datasets ranging from 1.2 to 10.3 h in length and spanning variability in environmental conditions related to storm characteristics (size and intensity) and antecedent moisture. Monte Carlo simulations were run for four model structures of varying complexity and evaluated using a Generalized Likelihood Uncertainty Estimation (GLUE) approach. We found that a model of intermediate complexity was adequate to model all catchment‐storm pairs. Relationships between the parameters of the best model and catchment and storm characteristics were sought. We found that the fraction of effective rainfall routed as event water was correlated to rainstorm size but insensitive to catchment size, indicating that it is controlled by environmental conditions such as storm intensity and size. The mean transit time of event water decreased with increasing rainstorm size, indicating increased connectivity during larger rainstorms. Finally, a linear relation was found between the mean transit time of event water and catchment size suggesting that the time it takes for event water to be transferred to the stream is directly related to catchment size, particularly for catchments greater than 30 ha.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".