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Enregistrement W4385658832 · doi:10.1111/jfr3.12936

Preparing for severe flooding: Flood risk management research leading to better flood preparedness

2023· article· en· W4385658832 sur OpenAlexaffabout
Andrew Binns

Notice bibliographique

RevueJournal of Flood Risk Management · 2023
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueFlood Risk Assessment and Management
Établissements canadiensUniversity of Guelph
Organismes subventionnairesnon disponible
Mots-clésFlood mythFlooding (psychology)PreparednessDamagesFlood mitigationGeographyChinaWater resource managementEnvironmental scienceArchaeology

Résumé

récupéré en direct d'OpenAlex

Catastrophic flooding events across the world are occurring with escalating consequences. This year, we have already seen severe flooding in New Zealand, China and Chile, to name just a few. Flood damages, exacerbated by changes in climate impacting weather patterns and alterations in land use, are escalating dramatically. The urgent need for a community to be well-prepared for the next flood event is paramount across society to reduce the devastating consequences and increase resiliency to flooding. Ten years ago, a catastrophic flood event in June 2013 across the province of Alberta, Canada, and in particular, the City of Calgary, brought upwards of 350 mm of rainfall in the hardest hit region (Pomeroy et al., 2016). A combination of heavy rainfall and snowmelt in the mountains resulted in flood levels along the Bow and Elbow rivers reaching 100-year and 200-year levels, respectively (O'Donovan, 2023). The flood resulted in loss of life of five people, displacement of 100,000 people across the province, and an estimated economic impact of $6 billion (CDN) in flood damages (Tanner & Árvai, 2018). Since this devastating event, efforts within Alberta and the City of Calgary have been focused on preparing for a 1000-year flood event. In the past decade, this has sparked a huge investment in developing both structural and non-structural flood mitigation measures, including construction of an off-stream reservoir to temporarily store floodwaters, raising the height of the gates at a dam to double reservoir storage capacity, installation of flood barriers to protect vulnerable areas, and updating flood inundation and hazard maps and floodplain land use policies (City of Calgary, 2022). Efforts in Alberta and elsewhere around the world have highlighted the urgent need for research into critical areas of flood risk management to prepare for future severe flooding. Effective flood preparedness relies on a greater understanding of flood-vulnerable areas to better anticipate flooding events, increase flood warning capabilities and guide in flood risk infrastructure investment. Several aspects of flood vulnerability are explored in this issue, including an investigation into the effect of flood diversions on the timing of downstream flood hazards in a flood-vulnerable area (Miller, 2023), and consideration of the data quality, data sources and fundamental relevant processes required for effective flood risk assessments (Sieg et al., 2023). New approaches are also being continually applied and developed to help identify flood-vulnerable areas. Davis et al. (2023) applied an index-based method to identify buildings at risk to flooding in vulnerable historic urban centres and examined a cost–benefit approach for implementation of flood adaptation measures. Machine learning techniques, applied by Vojtek et al. (2023), evaluate riverine flood potential and identify areas at the highest risk of riverine flooding. In addition, understanding the anticipated effects of climate change on precipitation and streamflow, as explored by Nihei et al. (2023) in this issue, aids in predicting the potential escalation of flood impacts and identification of new areas vulnerable to future floods. Development and optimization of tools and approaches towards understanding flooding potential and evaluating flood risk management approaches are also required to improve flood preparedness. This is exemplified in this issue by several studies into new approaches for numerical modelling tools in flood simulation, including the effect of cross-sectional orientation in a one-dimensional hydrodynamic model on variability in model flood simulations (Jesna et al., 2023), incorporation of spatially distributed precipitation into a two-dimensional hydraulic model for a vulnerable, steep-terrain catchment (Godara et al., 2023), integration of local-scale features into a hydraulic modelling framework to improve model representation of local urban flood defence infrastructure (Massam et al., 2023), and the relationship between spatiotemporal catchment hydrological processes and the calibration process in hydrological–hydrodynamic models (David et al., 2023). A new modelling approach for critical infrastructure networks that can be disrupted by flooding is explored by Schotten and Bachmann (2023). Other studies in this issue seek to develop important datasets for flood risk management applications, including the development of an extensive dam failure database to examine the probability distribution of earthfill dam breach parameters (da Silva & Eleutério, 2023), and the development of high-precision and high-resolution precipitation data using satellite estimate products (Zhuang et al., 2023). Focus on structural and non-structural flood mitigation approaches in research is also contributing towards increased flood resiliency and better flood preparedness for vulnerable communities around the world. In this issue, Chahinian et al. (2023) evaluate the effectiveness of an early flood warning system with an examination into a 2019 flood event in Bamako, Mali. Other studies in this issue investigate the optimization of investments for reliability of various flood defence systems (Mooyaart et al., 2023) and asset management of flood defences and stakeholder cooperation (den Heijer, Podt, et al., 2023; den Heijer, Rijke, et al., 2023). The adaptive behaviour of individuals and psychosocial factors to reduce the potential for adverse impacts of flooding events are examined by Jacob et al. (2023). Research into natural flood risk management approaches is also providing improved guidance to modelling practitioners (Hill et al., 2023) and identifying limitations in their applications to situations in vulnerable hydrological settings (Badjana et al., 2023). As communities prepare for more extreme and severe flooding events, effective flood risk management relies upon critical research, like the studies in this issue, to increase resiliency and flood preparedness and implement appropriate and robust flood mitigation approaches. In light of the growing challenges of climate change and increases in urbanisation, combined with flood events that are resulting in greater damages and impacts to human life, these efforts are critical to ensure society is as best prepared for the next catastrophic floods.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,008
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,314
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0080,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,000
Communication savante0,0000,001
Science ouverte0,0020,003
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,002

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,029
Tête enseignante GPT0,330
Écart entre enseignants0,301 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2023
Routes d'admission2
Résumé présentoui

Explorer davantage

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