MétaCan
Menu
Back to cohort
Record W2884556237 · doi:10.1002/hyp.13238

Testing a spatially distributed tracer‐aided runoff model in a snow‐influenced catchment: Effects of multicriteria calibration on streamwater ages

2018· article· en· W2884556237 on OpenAlexaffabout
Thea Ilaria Piovano, Doerthe Tetzlaff, Pertti Ala‐aho, Jim Buttle, Carl P. J. Mitchell, Chris Soulsby

Bibliographic record

VenueHydrological Processes · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsThe Scarborough HospitalUniversity of TorontoTrent University
FundersH2020 European Research CouncilSeventh Framework Programme
KeywordsSurface runoffSnowmeltEnvironmental scienceTRACERHydrology (agriculture)SnowCalibrationDrainage basinSoil scienceGeologyEcology

Abstract

fetched live from OpenAlex

Abstract Integrating stable isotope tracers into rainfall‐runoff models allows investigation of water partitioning and direct estimation of travel times and water ages. Tracer data have valuable information content that can be used to constrain models and, in integration with hydrometric observations, test the conceptualization of catchment processes in model structure and parameterization. There is great potential in using tracer‐aided modelling in snow‐influenced catchments to improve understanding of these catchments' dynamics and sensitivity to environmental change. We used the spatially distributed tracer‐aided rainfall‐runoff (STARR) model to simulate the interactions between water storage, flux, and isotope dynamics in a snow‐influenced, long‐term monitored catchment in Ontario, Canada. Multiple realizations of the model were achieved using a combination of single and multiple objectives as calibration targets. Although good simulations of hydrometric targets such as discharge and snow water equivalent could be achieved by local calibration alone, adequate capture of the stream isotope dynamics was predicated on the inclusion of isotope data in the calibration. Parameter sensitivity was highest, and most local, for single calibration targets. With multiple calibration targets, key sensitive parameters were still identifiable in snow and runoff generation routines. Water ages derived from flux tracking subroutines in the model indicated a catchment where runoff is dominated by younger waters, particularly during spring snowmelt. However, resulting water ages were most sensitive to the partitioning of runoff sources from soil and groundwater sources, which was most realistically achieved when isotopes were included in the calibration. Given the paucity of studies where hydrological models explicitly incorporate tracers in snow‐influenced regions, this study using STARR is an important contribution to satisfactorily simulating snowpack dynamics and runoff generation processes, while simultaneously capturing stable isotope variability in snow‐influenced catchments.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.153
Threshold uncertainty score0.623

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.247
Teacher spread0.229 · 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 teacher head, 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

Citations18
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueHydrological ProcessesSame topicHydrology and Watershed Management StudiesFrench-language works237,207