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Record W4210803857 · doi:10.1111/jfr3.12780

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

2022· article· en· W4210803857 on OpenAlex
Chrissy Mitchell

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Flood Risk Management · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsResilience (materials science)Flood mythAction (physics)GeographyHydrology (agriculture)Environmental resource managementEnvironmental scienceArchaeologyGeologyGeotechnical engineering

Abstract

fetched live from 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.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.610
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.008
GPT teacher head0.240
Teacher spread0.233 · 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