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Record W2275911660 · doi:10.5194/hess-21-377-2017

Satellite-derived light extinction coefficient and its impact on thermal structure simulations in a 1-D lake model

2017· article· en· W2275911660 on OpenAlexafffund
Kiana Zolfaghari, Claude Duguay, Homa Kheyrollah Pour

Bibliographic record

VenueHydrology and earth system sciences · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaEnvironment and Climate Change Canada
KeywordsFlakeEnvironmental scienceSatelliteMolar absorptivityAtmospheric sciencesExtinction (optical mineralogy)Water columnHydrology (agriculture)GeologyOceanographyMineralogyMaterials science

Abstract

fetched live from OpenAlex

Abstract. A global constant value of the extinction coefficient (Kd) is usually specified in lake models to parameterize water clarity. This study aimed to improve the performance of the 1-D freshwater lake (FLake) model using satellite-derived Kd for Lake Erie. The CoastColour algorithm was applied to MERIS satellite imagery to estimate Kd. The constant (0.2 m−1) and satellite-derived Kd values as well as radiation fluxes and meteorological station observations were then used to run FLake for a meteorological station on Lake Erie. Results improved compared to using the constant Kd value (0.2 m−1). No significant improvement was found in FLake-simulated lake surface water temperature (LSWT) when Kd variations in time were considered using a monthly average. Therefore, results suggest that a time-independent, lake-specific, and constant satellite-derived Kd value can reproduce LSWT with sufficient accuracy for the Lake Erie station. A sensitivity analysis was also performed to assess the impact of various Kd values on the simulation outputs. Results show that FLake is sensitive to variations in Kd to estimate the thermal structure of Lake Erie. Dark waters result in warmer spring and colder fall temperatures compared to clear waters. Dark waters always produce colder mean water column temperature (MWCT) and lake bottom water temperature (LBWT), shallower mixed layer depth (MLD), longer ice cover duration, and thicker ice. The sensitivity of FLake to Kd variations was more pronounced in the simulation of MWCT, LBWT, and MLD. The model was particularly sensitive to Kd values below 0.5 m−1. This is the first study to assess the value of integrating Kd from the satellite-based CoastColour algorithm into the FLake model. Satellite-derived Kd is found to be a useful input parameter for simulations with FLake and possibly other lake models, and it has potential for applicability to other lakes where Kd is not commonly measured.

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)0.000
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 score0.080
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.015
GPT teacher head0.238
Teacher spread0.223 · 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

Citations18
Published2017
Admission routes2
Has abstractyes

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