MétaCan
Menu
Back to cohort
Record W4282552644 · doi:10.1111/jfr3.12824

Good practice in risk analysis

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

Bibliographic record

VenueJournal of Flood Risk Management · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicPublic relationsParallelsPolitical scienceCoronavirus disease 2019 (COVID-19)SociologyHistoryEngineeringOperations managementMedicine

Abstract

fetched live from 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.

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.

How this classification was reachedexpand

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)0.004
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.619
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.003
GPT teacher head0.233
Teacher spread0.230 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Flood Risk ManagementSame topicFlood Risk Assessment and ManagementFrench-language works237,207