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Enregistrement W4282552644 · doi:10.1111/jfr3.12824

Good practice in risk analysis

2022· article· en· W4282552644 sur OpenAlexaboutno aff
Ben Gouldby, Karin de Bruijn

Notice bibliographique

RevueJournal of Flood Risk Management · 2022
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueFlood Risk Assessment and Management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPandemicPublic relationsParallelsPolitical scienceCoronavirus disease 2019 (COVID-19)SociologyHistoryEngineeringOperations managementMedicine

Résumé

récupéré en direct d'OpenAlex

This edition of the journal comprises papers presented at the Fouth FLOODrisk Conference. The conference was originally scheduled to be held in Budapest in June 2020. The worldwide coronavirus pandemic emerged in late 2019 and early 2020, and by the time the conference was due to take place, many countries were in ‘lockdown’, causing the postponement of the conference. The pandemic is a worldwide tragedy with millions of lives lost. In response to this threat to humanity, science was thrust to the forefront, both in terms of advice to governments on how to restrict the spread and also the remarkable speed of development and implementation of the vaccines. Previous editorials in the Journal of Flood Risk Management (De Bruijn, 2020; Montz, 2020; Priest, 2021) have highlighted parallels between the management of the pandemic and flooding and the resilience of communities and their natural ability to adapt and overcome adversity. Desk-based workers all over the world quickly adapted to virtual meetings. Although this process was not without obstacles. Who could not sympathise with, and admire the professionalism of, the US lawyer who, struggling with image filters on a video call, felt obliged to declare that he was not a cat? There will be very few readers who have not directly contributed to the rise of ‘you are on mute’ to the top of the most used phrases charts (Knorr & Schreml, 2021)! Nevertheless, so quick was the adaptation process, that this conference eventually took place in a virtual online format in June 2021. Something that would not have been considered viable pre-pandemic had become ‘the norm’ within the space of a year. There are of course ‘lessons to be learned’ from the pandemic that translates to all aspects of risk analysis and management. It is interesting to note the risk assessment process adopted by some countries has been deemed deficient (Lacobucci, 2021). The deficiency identified relates to a need to plan for a range of challenging scenarios that have not happened in the past, as well as those that have. It is well-known the risk analysis process requires consideration of the likelihood and impact of all hazardous events (e.g., Bedford & Cooke, 2001). There is no option to ignore events that are complex to characterise and evaluate. It is also a well-known pitfall there can be a tendency to rely solely on historical evidence, or archetypal design events, when defining hazardous events (HSE, 2003). In this regard, it is perhaps surprising, that the well-established approach to representing the failure of flood defence infrastructure, using probability distributions known as fragility curves (e.g., Apel et al., 2004; Ayyub et al., 2009; Schultz et al., 2010; Simm & Tarrant, 2018; USACE, 1996; Vorogushyn et al., 2010) as part of the risk analysis process, is not universally implemented. Many flooding studies still rely on methods that have, as a prerequisite, an assumption that flood defences and related infrastructure cannot fail. With a long legacy of ageing flood defence infrastructure in many countries and an expected increase in the frequency of extreme events as a result of climate change (e.g., Kay et al., 2011; Schaller et al., 2016), widespread adoption of best practice in risk analysis, within the context of flood risk analysis, would seem prudent. Since climate change and socio-economic development will increase flood risk, in the absence of future mitigation measures, it is routine to incorporate these aspects into the decision making process. There is, however, much to learn with regard to capturing the complexities and associated uncertainties in this process. This issue makes a substantial contribution in this regard. Sea-level rise will increase flood risk globally. Tiggeloven et al. (2022), explore, for the first time on a global scale, the potential for natural flood management measures to support the mitigation of these impacts. The impact of foreshore vegetation, in terms of risk reduction in the present day, is evaluated. The future impact, if the vegetation is conserved, is then evaluated under different climate change scenarios, with a significant benefit demonstrated. Also relating to sea-level rise, De Bruijn et al. (2022) consider adaptation options for the Rhine-Meuse Delta in the Netherlands. A closed, pumped, system is contrasted with an open channel diversion option. The latter is shown to be able to accommodate a greater range of potential range of sea-level rise. Given the uncertainties, this attribute of flexibility is often a vital aspect of adaptation decision making. Groeneweg et al. (2022) consider scenarios of climate changes on the wind climate. Through impacts on wave conditions (height and direction) and set-up of water levels, a first estimate is given of the effect of uncertainties in storminess on Dutch levee design. The social response is another critical component of adaptation. Dillenardt et al. (2021), use socio-psychological models of adaptive behaviour to explore the actions of different demographics in response to pluvial flood events in Germany. Allowances for future climate change are routinely made in flood risk analyses. When de-trending historical data it is, however, complex to distinguish whether observed trends relate to changes in land use, natural variability or climate change. Griffin et al. (2022), explore these aspects of observed non-stationarity with a view to refining standard climate change allowances on peak river flows. Understanding present-day risk is of course the cornerstone of the robust assessment of climate change-related impacts. Observations and data gathering of floods that have occurred, and successful numerical model simulations of past events, are crucial elements in this process. Cohen et al. (2021) explore and compare different sources of remote observation techniques relating to fluvial flooding in Finland. Provan et al. (2022) describe the successful calibration of a regional storm surge model in Canada. It is envisaged this regional model will be further developed to help support future, climate change-related, analyses. We hope this issue challenges researchers and practitioners, working in the field of flood risk management, to reflect on their methods and provides helpful insights for the further development of a climate-robust and sustainable society.

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)0,004
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,619
Score d'incertitude au seuil0,998

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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 Opus0,003
Tête enseignante GPT0,233
Écart entre enseignants0,230 · 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'étudeObservationnel
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
Publié2022
Routes d'admission1
Résumé présentoui

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Même revueJournal of Flood Risk ManagementMême sujetFlood Risk Assessment and ManagementTravaux en français237 207