Model Based Spatial Distribution of Oxygen-18 Isotopes in Precipitation Across Canada
Bibliographic record
Abstract
Challenges inherent in mesoscale (large domain) hydrologic modelling of remote ungauged basins include validation of model results and quantification of uncertainty in the predictions. Isotope equipped hydrological models, such as isoWATFLOOD, have the ability to simulate both quantity and isotopic composition of streamflow and runoff generation processes providing more options for model validation, but first require information about isotopic composition of precipitation across the model domain. The stable water isotopic composition of precipitation (18OPPT) is available from stations sampling monthly composite precipitation, but the data are spatially and temporally discontinuous, particularly in northern areas of Canada. Here we use new data from the Canadian Network for Isotopes in Precipitation (CNIP) to create and evaluate a variety of empirical relationships to develop improved interpolations of the spatial distribution of 18OPPT across Canada. The goal of this research is to develop 18OPPT prediction models that can be incorporated directly within the isoWATFLOOD hydrological modelling framework to provide spatially variant 18OPPT patterns as forcing data for the iso-hydrological model. Comparison of model results has identified models capable of simulating annual 18OPPT distributions, but also identified seasons and areas where the geographical and climatological parameters included in this analysis were not able to simulate the measured distributions. Spring, summer and fall model results were satisfactory; however, winter model results were more variable, indicating increased complexity in the driving forces of 18OPPT patterns during this season. Overall, model results improve with the addition of time-variant climate parameters, this finding being especially significant during the winter. Improving the precipitation input fields within isotope-equipped hydrological models will provide a valuable tool for water use management within large, remote, and often ungauged Canadian rivers and will facilitate studies of both climate variability and surface hydrology in remote regions.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".