Integrated response and transit time distributions of watersheds by combining hydrograph separation and long-term transit time modeling
Bibliographic record
Abstract
Abstract. We present a new modeling approach analyzing and predicting the Transit Time Distribution (TTD) and the Response Time Distribution (RTD) from hourly to annual time scales as two distinct hydrological processes. The model integrates Isotope Hydrograph Separation (IHS) and the Instantaneous Unit Hydrograph (IUH) approach as a tool to provide a more realistic description of transit and response time of water in catchments. Individual event simulations and parameterizations were combined with long-term baseflow simulation and parameterizations; this provides a comprehensive picture of the catchment response for a long time span for the hydraulic and isotopic processes. The proposed method was tested in three Andean headwater catchments to compare the effects of land use on hydrological response and solute transport. Results show that the characteristics of events and antecedent conditions have a significant influence on TTD and RTD, but in general the RTD of the grassland dominated catchment is concentrated in the shorter time spans and has a higher cumulative TTD, while the forest dominated catchment has a relatively higher response distribution and lower cumulative TTD. The catchment where wetlands concentrate shows a flashier response, but wetlands also appear to prolong transit time.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".