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Enregistrement W4210803857 · doi:10.1111/jfr3.12780

Flood resilience—A time for cathedral‐based thinking and action!

2022· article· en· W4210803857 sur OpenAlexaboutno aff
Chrissy Mitchell

Notice bibliographique

RevueJournal of Flood Risk Management · 2022
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueFlood Risk Assessment and Management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésResilience (materials science)Flood mythAction (physics)GeographyHydrology (agriculture)Environmental resource managementEnvironmental scienceArchaeologyGeologyGeotechnical engineering

Résumé

récupéré en direct d'OpenAlex

Recently, Tellman et al. (2021) analysed satellite images for 913 major floods worldwide from 2000 to 2018. They estimated that 32 countries have ‘continuously increasing’ flood exposure, and it is expected that a further 25 will join this list by 2030. This means that in less than 10 years, a third of all nations and an additional 179.2 million people will be at risk of flooding (from floods with a 1% annual chance of occurring; Tellman et al., 2021). Perhaps most alarming here is the number of nations just starting their journey with increasing flood risks. As those who work in this field appreciate, the notable drivers such as heavy rainfall, tropical storms, tidal surges, snow and ice melt and dam failure are complex to predict, manage and respond to. Sharing good practice has never been more important. Climate-related extreme weather events have continued to dominate the news over the past year. Malaysia evacuated 30,000 people from their homes in December 2021, and whilst used to the stormy monsoon season, this event caused many to be stranded as rivers overflowed and cut off access roads. The summer's European floods (July 2021) highlighted the importance of predictive modelling, where a low-pressure system named ‘Bernd’ sat over Central Europe a few weeks after a run of severe thunderstorms. This event alone resulted in 243 deaths, predominantly in Germany. What is most disheartening is that this is in an area where much of the infrastructure withstood the flooding and emergency support was quick to act. Each flood event is a reminder that societal well-being requires financial support. By covering the basic needs of people in the event of a disaster, the resilience is strengthened, and poverty becomes less likely. In Germany, most people flooded had access to social protection and will have received governmental financial support where insurance coverage was not available. However, flooding is becoming more extreme in both intensity and frequency, which may mean support of this nature becomes a greater challenge. The Insurance Bureau of Canada estimated that the British Columbia Floods (November–December 2021) was the costliest natural disaster in British Columbia's history. Finding sustainable solutions to fair, accessible, available and adequate financial assistance requires further attention (Aleksandrova et al., 2021). Understanding the cost and benefits of investing in long-term resilient solutions is increasingly important. In 2021, Greta Thunberg talked about “cathedral thinking” that the urgency of the climate emergency means we must lay the first stone without knowing exactly how to construct the ceiling. This resonates with the research presented in this journal, where novel approaches often form just one part of a vaguely sketched architecture. The most successful research considers the connection to practice from the outset (Samuels, 2021), but it is recognised that it is challenging to do in this growing field of uncertainty. Real integration and provision of well-tested adaptive solutions are what practitioners crave to underpin and strengthen their decision-making. Practical trials offer an effective solution and are increasingly used to demonstrate how new approaches can be applied and then replicated elsewhere. There are many examples such as India's six small-scale adaptation projects in diverse regions of the country, ranging from mangrove restoration to the use of short-duration crops that mature in 70 days to adapt to late sowing conditions. In the United Kingdom, a £200-million (~$265 m) flood and coastal resilience innovation programme is allowing new ideas to be tested on-site. One location is trialling the ‘Sponge City’ concept used in China (Qi et al., 2020), addressing surface water flood risk with permeable surfaces and green infrastructure such as green roofs. At another location, subtidal habitats are trialling other nature-based solutions such as sea kelp and oyster reefs restoration, which protect against coastal erosion and flooding. Behind all these trials, there is also the added benefit of establishing networks to develop and share knowledge. Approximately, a quarter of the Netherlands sits below average sea level, which increases the pressure for innovative and resilient approaches. The ‘Delta Plan’ outlines how the large variety of infrastructure works together as a well-balanced solution. It is constantly reviewed, and adaptive methods are included to encourage environmental benefits (Jeuken et al., 2015). Alongside this, the ‘Valuing Water Initiative’ provides an important insight into the many requirements of the water itself, including the need to raise public awareness and enable more inclusive participation. An outcome reflected in the UK Pathfinder research, which looked to understand how to improve community resilience (Twigger-Ross et al., 2015). Community inclusion has a proven history of success, and it means that the natural flooding situation is accepted at every level and is not purely seen as a government problem. Unsurprisingly, the attention has, in recent years, been diverted towards the global Covid-19 pandemic that has left so many in an even more difficult position. It has been a stark reminder of the considerable challenges that developing nations face, where access to the tools and financial means of developed nations is simply not as attainable. At the Conference of Parties (COP26) in May 2020, President Alok Sharma said ‘I ask Ministers from developed nations to imagine what it is like for communities on the frontline of climate change. Struggling to deal with a crisis they did next to nothing to create. To feel what it is like, to see developed countries invest trillions overnight to address the Covid-19 pandemic, whilst the $100 billion a year that we have promised to support developing countries with remains uncertain’. To add to this, in recent years, we have learnt that just 3% of private finance mobilised under the legally binding international treaty on climate change, known as ‘the Paris Agreement’ (entered into force in 2016), went towards adaptation, with over 95% going to flood mitigation (United Nations Framework Convention on Climate Change, 2015). As we grapple to slow the impact of climate change, sharing low-cost flooding solutions with developing nations is increasingly important. The good news is that many nations have flood initiatives in place already that lead with words such as ‘adaptation’ and ‘resilience’. The vast majority of these emphasise the need to set up for a sustainable future and not to just react to the current day at a local scale. However, when bringing countries together from across the North Seas region, it emphasised that not everyone is using the same meaning of these words, and perhaps part of the solution is finding an international common language (Sayers, 2020). Whilst some progress is being made, more needs to be done. As we begin to ‘talk a good talk’ about adaptation and resilience, we need to pick up the pace from our cathedral thinking on floods and translate the research into reality so that all countries, communities and individuals can be more resilient in the face of an uncertain future.

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 candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,610
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)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
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 Opus0,008
Tête enseignante GPT0,240
Écart entre enseignants0,233 · 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

Citations8
Publié2022
Routes d'admission1
Résumé présentoui

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