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Enregistrement W2917238787 · doi:10.1111/jfr3.12528

Shared roles and responsibilities in flood risk management

2019· article· en· W2917238787 sur OpenAlexaboutno aff
Sally Priest

Notice bibliographique

RevueJournal of Flood Risk Management · 2019
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueFlood Risk Assessment and Management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésFlood mythRisk managementFlood risk managementFlooding (psychology)Psychological resilienceEnvironmental planningClimate riskEnvironmental resource managementBusinessDamagesResilience (materials science)Climate changeGeographyPolitical scienceEconomicsFinance

Résumé

récupéré en direct d'OpenAlex

The need for communities to be more resilient to flood risk is well recognised, and there is much debate about how this can be best achieved. In her editorial in December 2018, Journal of Flood Risk Management (JFRM) Associate Editor Burrell Montz raised the important question of what a resilient community might look like and how progress towards resilience can be measured (Montz, 2018). The importance of assisting and empowering at-risk communities to understand and manage their flooding has arguably never been greater, with increasing populations residing in flood-prone areas as well as flood risk altering due to climate and land use changes. Furthermore, as well as the long-recognised physical, social, and financial damages of flooding, there is also stronger evidence about the breadth, severity, and duration of health impacts (Waite et al., 2017), thus reinforcing the need for effective flood risk management. In reality, however, in most circumstances, the responsibility for flooding is shared, including between national, regional, and local governments; insurance markets; private individuals; and businesses at risk. Solidarity has long been present within flood risk management; however, despite collective approaches being common and longstanding, the nature of shared responsibilities in many areas is shifting. The nature of risk management has changed, with broader calls for Integrated Flood Risk Management (European Union, 2007, 2013; World Meteorological Organisation, 2009) and with flood risk closely connecting to many other sectors (e.g., water resources, agriculture, housing, incident response) and government priorities (e.g., economic growth, climate change adaptation). There is a greater emphasis on working in partnership between those with statutory or voluntary responsibilities for managing flood risk and preferred approaches being those that are intersectoral and can deliver multiple societal benefits. In many countries, flood risk management continues to become more professionalised, and official roles and responsibilities are expanding to encompass a broader suite of actors, some of whom work at the core of managing flood risk and others at the periphery. A joint approach offers many opportunities to assist those affected by flood risk and shares the burden, but presents additional challenges associated with multiagency and multilevel working and the ability to offer coordinated, rather than contradictory, actions. Many questions are raised about how to effectively and efficiently manage flood risk while accounting for the sharing of responsibilities, such as: are roles and responsibilities for flood risk management clear and understood? What are the most appropriate mechanisms for actors to cooperate? And how do we deliver best multiple benefits when there are competing priorities and timescales? There is a growing body of research that examines flood risk governance (e.g., see volume 11[3] of the JFRM; Djalante, 2012, Ward, Pauw, van Buuren, & Marfai, 2013), which begins to answer these questions and considers processes for joint working and decision-making (e.g., coproduction (Mees, Alexander, Matczak, Gralepois, & Mees, 2018), citizen-driven initiatives (Seebauer, Ortner, Babcicky, & Thaler, 2018), and bridging mechanisms (Gilissen, Alexander, Matczak, Pettersson, & Bruzzone, 2016)). Indeed, two papers in this issue relate directly to this topic; Henstra, Thistlethwaite, Brown, and Scott (2018) explore the understanding of Canadian citizens about how flood risk responsibilities are distributed, whereas Busscher, van den Brink, and Verweij (2018) present recommendations for combined objectives and codesign processes for the better integration of spatial planning and water management in the Netherlands. Flood risk management is clearly one policy area where there is the requirement for effective multilevel governance to better manage shared responsibilities. Importantly, we should be mindful of those who remain at the heart of any management decision: those living with flood risk. How well are they able to navigate this increasingly complex system of flood risk management delivery? There is a need to ensure that individuals are able to understand their own roles and responsibilities, are able to effect decision-making, and are empowered to take action to be more resilient. What is clear is that, despite differences in how flood risk responsibilities are shared and how management is organised, there are many flood risk professionals, other stakeholders, individual residents, businesses, and others tirelessly working to reduce risk and increase resilience in all aspects of flood risk management. It is this broad group that the research presented in the JFRM serves (e.g., from improving flood forecasting (Tomasella et al., 2018) to discussions of recovery support (Ge, Li, Li, & Xing, 2017)). Since the inception of the journal over a decade ago, it has encouraged practice-relevant contributions, and this remains pertinent today with the expanding flood risk management community. I challenge authors to identify and target the growing range of actors interested in flood risk management when making recommendations about the policy or practice relevance of their research.

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)0,002
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: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,143
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

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

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 Opus0,004
Tête enseignante GPT0,210
Écart entre enseignants0,207 · 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

Citations9
Publié2019
Routes d'admission1
Résumé présentoui

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