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Record W4392652130 · doi:10.5194/egusphere-egu24-20064

Constraining stream water source dynamics in a high-latitude catchment using tracer-aided modeling

2024· preprint· en· W4392652130 on OpenAlexaff
Andrea Popp, David Gustafsson, Hjalmar Laudon, Charlotta Pers, Benjamin Fischer, Tricia Stadnyk

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTRACEREnvironmental scienceLatitudeHydrology (agriculture)Drainage basinGeographyGeologyPhysicsCartographyGeodesy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.022
GPT teacher head0.250
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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