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Record W3170325316 · doi:10.5194/egusphere-egu21-1567

A Bayesian mixing model framework for quantifying temporal and spatial variation in source of sediment to lakes across hydrological gradients of floodplains

2021· article· en· W3170325316 on OpenAlexaffabout
Mitchell L. Kay, Heidi K. Swanson, Jacob Burbank, Tanner J. Owca, Lauren A. MacDonald, Cory A. M. Savage, Casey R. Remmer, Laura K. Neary, Johan A. Wiklund, Brent B. Wolfe, Roland I. Hall

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsWilfrid Laurier UniversityUniversity of Waterloo
Fundersnot available
KeywordsFloodplainSedimentFlood mythHydrology (agriculture)Environmental scienceSpatial variabilityRange (aeronautics)Drainage basinSTREAMSPhysical geographyPaleolimnologySampling (signal processing)Surface runoffGeologyEcologyGeographyGeomorphology

Abstract

fetched live from OpenAlex

Episodic flood events are critical for recharging water balance of floodplain lakes and maintaining their ecological integrity, yet are subject to alteration in frequency and magnitude by natural and anthropogenic processes that operate over a range of spatial and temporal scales. To evaluate roles of potential stressors, paleolimnological reconstructions are used to obtain insights into hydrological variability of dynamic floodplain lakes. However, spatial and temporal integration is often underdeveloped because different paleolimnological measurements must be applied across lakes due to the wide range of energy conditions that impart marked differences in sediment composition. Here, we use a linear discriminant analysis to identify 10 significant elemental concentrations in surveyed sediment from multiple sampling campaigns that distinguish the geochemical fingerprints of three end-member sources in lakes at the Peace-Athabasca Delta (PAD; Canada): the Athabasca River, the Peace River and local catchment runoff. Over 90% of the sediment samples were correctly classified into the original groups after cross-validation due to the distinctiveness of the three end members, which permits development of a robust Bayesian mixing model to discern the relative contributions of sediment from the three sources. We evaluate the mixing model at two adjacent lakes in the Athabasca sector of the PAD and demonstrate its effectiveness to discriminate three known hydrological phases during the past 300 years. Notably, the model infers ~60% of the sediment originated from the Peace River during the largest ice-jam flood event on record (1974), which was unrecognized by other methods. We then applied our model to sediment records from 18 lakes spanning the hydrological gradients across the 6000 km2 PAD to further probe the hydrological evolution during the past ~150 years. Results demonstrate decline in frequency of flooding from both the Athabasca and Peace rivers and lake-level drawdown since the early 20th century and align remarkably well with prior interpretation of conventional paleohydrological records of individual lakes. We advocate our approach provides a universal method that can be applied across the full range of sediment composition to quantify change in source, frequency and magnitude of river floodwaters to lakes and is transferable to other dynamic floodplain landscapes where variation of sediment composition challenges efficacy of other approaches.

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)0.005
metaresearch head score (Gemma)0.006
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: Methods · Consensus signal: Methods
Teacher disagreement score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.282
Teacher spread0.256 · 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
GenreMethods

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
Published2021
Admission routes2
Has abstractyes

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