Temporal dynamics of catchment transit times from stable isotope data
Bibliographic record
Abstract
Abstract Time variant catchment transit time distributions are fundamental descriptors of catchment function but yet not fully understood, characterized, and modeled. Here we present a new approach for use with standard runoff and tracer data sets that is based on tracking of tracer and age information and time variant catchment mixing. Our new approach is able to deal with nonstationarity of flow paths and catchment mixing, and an irregular shape of the transit time distribution. The approach extracts information on catchment mixing from the stable isotope time series instead of prior assumptions of mixing or the shape of transit time distribution. We first demonstrate proof of concept of the approach with artificial data; the Nash‐Sutcliffe efficiencies in tracer and instantaneous transit times were >0.9. The model provides very accurate estimates of time variant transit times when the boundary conditions and fluxes are fully known. We then tested the model with real rainfall‐runoff flow and isotope tracer time series from the H.J. Andrews Watershed 10 (WS10) in Oregon. Model efficiencies were 0.37 for the 18 O modeling for a 2 year time series; the efficiencies increased to 0.86 for the second year underlying the need of long time tracer time series with a long overlap of tracer input and output. The approach was able to determine time variant transit time of WS10 with field data and showed how it follows the storage dynamics and related changes in flow paths where wet periods with high flows resulted in clearly shorter transit times compared to dry low flow periods.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".