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Record W6910282987 · doi:10.4224/40003402

Toward a national database on flooding events caused by river ice

2024· report· en· W6910282987 on OpenAlexaffvenueabout

Bibliographic record

VenueNPARC · 2024
Typereport
Languageen
Field
Topic
Canadian institutionsNational Research Council CanadaGovernment of Canada
Fundersnot available
KeywordsFlooding (psychology)Hydrology (agriculture)Surface runoffFlood mythShoreArctic ice pack

Abstract

fetched live from OpenAlex

Flooding along river shorelines often results from ice-related phenomena, such as ice jams. Severe ice jams and associated floods can have major socioeconomic impacts, not only on people’s safety and property but also on the security of infrastructure, transportation, inland navigation, and energy generation. The significance of ice jams to flooding at the national level and beyond has not been adequately documented, and not enough is known about whether/how their frequency is responding to the changing climate. To address these requirements, there is a need for a database of flooding events induced by river ice and ice jam floods (IJF) in Canada, including relevant information, such as year and date, extent of floods, and damage costs. This report is to assess how to best approach the makeup of such a database. It begins with an overview of river ice and the factors leading to the formation of ice jams, defined as a stationary accumulation of fragmented ice or frazil that restricts flow. The large thickness and extreme roughness of the ice under- surface can cause very high water levels and overland flooding, even with moderate river discharges. A review was conducted as part of this study on existing methods to model river ice and anticipate the formation of ice jams. These models are of two types: those that capture the physics of the processes governing river ice development, and those that are data-driven i.e., they rely on statistical data. This review allows for an appreciation of the data that could be incorporated into a database. A review was also conducted on pre-existing databases – eleven such databases are described, each addressing various aspects of river ice from a different perspective: They are: the Canadian River Ice Database (CRID), the Cold Regions Research and Engineering Laboratory (CRREL) database, the Historique d'embâcles répertoriés from the Government of Quebec’s Ministère de la Sécurité Publique (MSP), the Canadian Ice Database (CID), the Historical Flood Events database from Natural Resources Canada (NRCan) and ECCC, the River Watch Program (RWP) in Alaska, the Global Lake and River Ice Phenology Database, Alberta River Basin’s database, the Regional Municipality of Wood Buffalo’s (RMWD) database, the Russian River Ice Thickness and Duration database, and the National Ice Jam Database. A description of each is provided, as well as an explanation of the data they enclose. Three other databases, not strictly relevant for river ice but of interest nonetheless, are briefly described: HydroSHEDS’ Global River Classification (GLORIC), European Union’s Copernicus database, and University of New Hampshire’s ArcticRIMS. Database design takes into account data entry, for which little information was found, and data access, typically via the Internet. In most cases, the data can be accessed by downloading data files or by navigating interactive maps and extracting data from that exploration. Based on the foregoing, a database is seen as having a dual ‘vocation’: a) to provide data that can help understand these phenomena – the target users have expertise in river ice hydraulics and hydrology; and b) To provide information that can help prepare against these phenomena – in this case, the target users are community stakeholders. A simplified sequence of steps is proposed for the development of a new database, drawing from the previously existing ones: deciding on who would be the end users, determining what their needs are, figuring out the data sources, orchestrating database design, its content and how it would be made accessible to the users, planning the resources required (technical, timelines, material), database delivery, and database maintenance. While the database would be centered primarily on IJFs in Canada, it could enclose data from elsewhere. The database should be seen as a communication vehicle of scientific relevance, as is the case for research papers. Finally, the larger the audience for this product (by envisaging a wide user base in database planning), the more interest it would spur from organizations that are concerned with IJFs. This would maximize opportunities to leverage funding sources for everyone’s benefit.

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)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.635
Threshold uncertainty score0.725

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0130.020
Science and technology studies0.0010.000
Scholarly communication0.0020.003
Open science0.0030.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.003

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 head0.098
GPT teacher head0.348
Teacher spread0.251 · 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 designNot applicable
Domainnot available
GenreOther

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 routes3
Has abstractyes

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