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Record W1499500103 · doi:10.1111/jfr3.12104

Through Hell and High Water!

2014· article· en· W1499500103 on OpenAlexaffabout
Slobodan P. Simonović

Bibliographic record

VenueJournal of Flood Risk Management · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsWestern University
Fundersnot available
KeywordsGeographyFlooding (psychology)FoothillsDowntownPopulationFlood mythTributarySocioeconomicsHydrology (agriculture)ArchaeologyDemographyCartographyGeology

Abstract

fetched live from OpenAlex

Flooding in southern Alberta (Canada) in June 2013 resulted in four fatalities and unprecedented damage to property. More than 250 mm of rain fell over a 36 hour period in the foothills west and southwest of Calgary and began rapidly flowing east through the province's river valleys bringing destruction across southern Alberta. Areas along the Bow, Elbow, Highwood, Red Deer, Sheep, Little Bow, and South Saskatchewan rivers and their tributaries were particularly affected. Bow River experienced flows eight times higher than normal, and Elbor River experienced flows twelve times higher than normal. A total of 32 states of local emergency were declared and 28 emergency operations centres were activated as water levels rose and numerous communities were placed under evacuation orders. City of Calgary (4th largest city in Canada with population of 1.1 M) was hit very hard. Over 100,000 people were displaced throughout the region. More than 35,000 people were without power for weeks. Downtown Calgary was out of reach for two weeks. Twenty bridges were closed and many roads without access for days. Some 2,200 Canadian Armed Forces (CAF) troops were deployed to help in flooded areas. Total damage estimates exceeded C$6 billion and in terms of insurable damages, is the costliest disaster in Canadian history. Two weeks later in July 2013 the City of Toronto (the largest city in Canada with population of 4.7 M) experienced a heavy rainfall event, exceeding 126 mm over two hours (74.4 mm July average), that overwhelmed stormwater and sanitary sewer systems. At least 500,000 people were affected, 1,400 train passengers stranded for hours, all major traffic arteries flooded and over 300,000 people left without the power for a significant period of time. Urban flood losses, including damage from water and sewage that entered homes and businesses through the backup of municipal sewers, were extensive, approaching C$1 billion and making this storm the most expensive storm in the Province of Ontario. These two events prompted the Minister of Public Safety Canada to include in his ‘Report on Plans and Priorities’ to the Prime minister the following statement: …‘The rising cost of natural disasters and the financial burden on Ottawa is the country's biggest public safety risk’… Actions to prevent or reduce the risk of flood damage in Canada should include actions to address both riverine and urban flooding. Riverine floods are the most common natural hazard experienced by Canadians. The Canadian Disaster Database, for example, identifies 62 floods in Canada during the ten-year period from 2003 through 2012. In the 1960s and 1970s few Canadians experienced damage from urban flooding. However, over the past few decades there has been an alarming increase in urban flood losses. Indeed, water damage from sewers backing up into basements and other losses due to extreme rainfall in urban areas likely resulted in urban flood losses more than ten times greater than riverine flood damage. Best practices to prevent and reduce the risk of loss from riverine flooding are well known, and have been tested around the world for several decades. Prohibition of development in zones of flood risk, investments in structural flood defence and a variety of other tools are available to eliminate or reduce the expected loss from riverine flooding. The foundation for riverine flood management involves a clear determination of acceptable risk of flood damage. Best practices for reducing the risk of urban flooding have emerged over the past 25 or 30 years and are distinct from actions to reduce the risk of loss from riverine flooding. The frequency and severity of urban flood damage is determined by factors that include rainfall patterns, lot level actions by property owners and the state of the local sewer infrastructure. Every household connected to the storm or sanitary sewer system is at some risk of loss. Best practices to reduce the risk of urban flood damage include lot level actions by property owners and public investments in sewer systems. The tragic losses in southern Alberta and Toronto have opened a window of opportunity over the next 12 to 24 months for the Government of Canada and other stakeholders to take action to reduce the risk of loss from flooding. Some of the recommendations include:

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.748
Threshold uncertainty score0.549

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.003
GPT teacher head0.197
Teacher spread0.193 · 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 designNot applicable
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

Citations3
Published2014
Admission routes2
Has abstractyes

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