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Record W3035210911 · doi:10.1080/07011784.2020.1772116

Subwatershed-based lake and river routing products for hydrologic and land surface models applied over Canada

2020· article· en· W3035210911 on OpenAlexaffvenueabout
Ming Han, Juliane Mai, Bryan A. Tolson, James R. Craig, Étienne Gaborit, Hongli Liu, Konhee Lee

Bibliographic record

VenueCanadian Water Resources Journal / Revue canadienne des ressources hydriques · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Waterloo
Fundersnot available
KeywordsRouting (electronic design automation)Environmental scienceHydrology (agriculture)StreamflowHydrological modellingDrainage basinSurface runoffGroundwater rechargeWatershedGeographyGeologyComputer scienceGroundwaterClimatologyEcologyCartography

Abstract

fetched live from OpenAlex

Lakes and reservoirs have critical impacts on hydrological, biogeochemical, and ecological processes, and they should be an essential component of regional-scale hydrological and eco-hydrological models. This is particularly important in Canada with its tens of thousands of lakes. Past large-scale hydrologic modelling efforts tend to either ignore the impacts of all lakes or explicitly simulate the behaviour of only the largest lakes in a watershed. This research derives a suite of Pan-Canadian subwatershed-based lake and river routing GIS products at multiple spatial resolutions (average catchment size ranges from 60 to 306 km2 while the number of lakes explicitly represented ranges from 11,000 to 87,000). These publicly available data products supply all the necessary hydrologic routing model inputs, including network topology, subwatershed geometry, channel characteristics (slope, length, roughness, and geometry), and lake characteristics (area, volume, and outlet description), and were derived primarily from the HydroLAKES, HydroBASINS and HydroSHEDS databases. All Water Survey of Canada streamflow gauging stations are used to define subwatershed outlets in the products. The routing product is used to inform a hydrologic routing model in the Raven hydrologic modelling framework. This is the first demonstration of Raven in routing-only mode. As a case study, the Hudson Bay drainage basin (∼40% of Canada) is simulated using GEM-Surf land surface model gridded runoff and recharge as inputs and includes more than 20,000 river reaches and more than 10,000 lakes explicitly represented at an hourly timestep. Uncalibrated streamflows compare reasonably well to measured streamflows at select locations. The sensitivity of the routing model prediction quality to the discretization level for represented lakes is evaluated and shows, for example, that ignoring multiple smaller lakes can have a significant impact on predictions.

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: none
Teacher disagreement score0.017
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.170
Teacher spread0.155 · 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

Citations21
Published2020
Admission routes3
Has abstractyes

Explore more

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