Constraining stream water source dynamics in a high-latitude catchment using tracer-aided modeling
Bibliographic record
Abstract
Standard hydrologic model calibration and evaluation primarily rely on streamflow observations, which can hinder an accurate representation of physical processes generating streamflow. Recent studies demonstrate that using tracers such as stable water isotope data in addition to flow observations in model calibration considerably reduces parameter uncertainty and constrains stream water source dynamics (e.g., He et al., 2019; Popp et al., 2021; Stadnyk and Holmes, 2023). In this study, we demonstrate the capabilities of an isotope-aided HYPE model (Lindström et al., 2010) in the Krycklan Catchment Study in Sweden. To this end, we integrated the isoWATFLOOD model's isotope routine (https://github.com/h2obabyts/isoWATFLOOD) into the HYPE model and incorporated extensive time series of stable water isotope data collected from different water sources including precipitation, snow, and groundwater and stream water. Our goal is to deepen the process understanding of snow-dominated catchments undergoing rapid changes due to global warming.ReferencesHe, Z., Unger-Shayesteh, K., Vorogushyn, S., Weise, S. M., Kalashnikova, O., Gafurov, A., Duethmann, D., Barandun, M., and Merz, B. (2019. Constraining hydrological model parameters using water isotopic compositions in a glacierized basin, Central Asia, Journal of Hydrology, 571, 332–348, https://doi.org/ 10.1016/j.jhydrol.2019.01.048.Lindström, G., Pers, C., Rosberg, J., Strömqvist, J. and Arheimer, B. (2010). Development and testing of the HYPE (Hydrological Predictions for the Environment) water quality model for different spatial scales. Hydrology Research 41.3–4, 295-319.Popp, A. L., Pardo‐Álvarez, Á., Schilling, O. S., Scheidegger, A., Musy, S., Peel, M., ... & Kipfer, R. (2021). A framework for untangling transient groundwater mixing and travel times. Water Resources Research, 57(4), e2020WR028362.Stadnyk, T. A., & Holmes, T. L. (2023). Large scale hydrologic and tracer aided modelling: A review. Journal of Hydrology, 129177.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".