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Record W4385658832 · doi:10.1111/jfr3.12936

Preparing for severe flooding: Flood risk management research leading to better flood preparedness

2023· article· en· W4385658832 on OpenAlexaffabout
Andrew Binns

Bibliographic record

VenueJournal of Flood Risk Management · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsFlood mythFlooding (psychology)PreparednessDamagesFlood mitigationGeographyChinaWater resource managementEnvironmental scienceArchaeology

Abstract

fetched live from OpenAlex

Catastrophic flooding events across the world are occurring with escalating consequences. This year, we have already seen severe flooding in New Zealand, China and Chile, to name just a few. Flood damages, exacerbated by changes in climate impacting weather patterns and alterations in land use, are escalating dramatically. The urgent need for a community to be well-prepared for the next flood event is paramount across society to reduce the devastating consequences and increase resiliency to flooding. Ten years ago, a catastrophic flood event in June 2013 across the province of Alberta, Canada, and in particular, the City of Calgary, brought upwards of 350 mm of rainfall in the hardest hit region (Pomeroy et al., 2016). A combination of heavy rainfall and snowmelt in the mountains resulted in flood levels along the Bow and Elbow rivers reaching 100-year and 200-year levels, respectively (O'Donovan, 2023). The flood resulted in loss of life of five people, displacement of 100,000 people across the province, and an estimated economic impact of $6 billion (CDN) in flood damages (Tanner & Árvai, 2018). Since this devastating event, efforts within Alberta and the City of Calgary have been focused on preparing for a 1000-year flood event. In the past decade, this has sparked a huge investment in developing both structural and non-structural flood mitigation measures, including construction of an off-stream reservoir to temporarily store floodwaters, raising the height of the gates at a dam to double reservoir storage capacity, installation of flood barriers to protect vulnerable areas, and updating flood inundation and hazard maps and floodplain land use policies (City of Calgary, 2022). Efforts in Alberta and elsewhere around the world have highlighted the urgent need for research into critical areas of flood risk management to prepare for future severe flooding. Effective flood preparedness relies on a greater understanding of flood-vulnerable areas to better anticipate flooding events, increase flood warning capabilities and guide in flood risk infrastructure investment. Several aspects of flood vulnerability are explored in this issue, including an investigation into the effect of flood diversions on the timing of downstream flood hazards in a flood-vulnerable area (Miller, 2023), and consideration of the data quality, data sources and fundamental relevant processes required for effective flood risk assessments (Sieg et al., 2023). New approaches are also being continually applied and developed to help identify flood-vulnerable areas. Davis et al. (2023) applied an index-based method to identify buildings at risk to flooding in vulnerable historic urban centres and examined a cost–benefit approach for implementation of flood adaptation measures. Machine learning techniques, applied by Vojtek et al. (2023), evaluate riverine flood potential and identify areas at the highest risk of riverine flooding. In addition, understanding the anticipated effects of climate change on precipitation and streamflow, as explored by Nihei et al. (2023) in this issue, aids in predicting the potential escalation of flood impacts and identification of new areas vulnerable to future floods. Development and optimization of tools and approaches towards understanding flooding potential and evaluating flood risk management approaches are also required to improve flood preparedness. This is exemplified in this issue by several studies into new approaches for numerical modelling tools in flood simulation, including the effect of cross-sectional orientation in a one-dimensional hydrodynamic model on variability in model flood simulations (Jesna et al., 2023), incorporation of spatially distributed precipitation into a two-dimensional hydraulic model for a vulnerable, steep-terrain catchment (Godara et al., 2023), integration of local-scale features into a hydraulic modelling framework to improve model representation of local urban flood defence infrastructure (Massam et al., 2023), and the relationship between spatiotemporal catchment hydrological processes and the calibration process in hydrological–hydrodynamic models (David et al., 2023). A new modelling approach for critical infrastructure networks that can be disrupted by flooding is explored by Schotten and Bachmann (2023). Other studies in this issue seek to develop important datasets for flood risk management applications, including the development of an extensive dam failure database to examine the probability distribution of earthfill dam breach parameters (da Silva & Eleutério, 2023), and the development of high-precision and high-resolution precipitation data using satellite estimate products (Zhuang et al., 2023). Focus on structural and non-structural flood mitigation approaches in research is also contributing towards increased flood resiliency and better flood preparedness for vulnerable communities around the world. In this issue, Chahinian et al. (2023) evaluate the effectiveness of an early flood warning system with an examination into a 2019 flood event in Bamako, Mali. Other studies in this issue investigate the optimization of investments for reliability of various flood defence systems (Mooyaart et al., 2023) and asset management of flood defences and stakeholder cooperation (den Heijer, Podt, et al., 2023; den Heijer, Rijke, et al., 2023). The adaptive behaviour of individuals and psychosocial factors to reduce the potential for adverse impacts of flooding events are examined by Jacob et al. (2023). Research into natural flood risk management approaches is also providing improved guidance to modelling practitioners (Hill et al., 2023) and identifying limitations in their applications to situations in vulnerable hydrological settings (Badjana et al., 2023). As communities prepare for more extreme and severe flooding events, effective flood risk management relies upon critical research, like the studies in this issue, to increase resiliency and flood preparedness and implement appropriate and robust flood mitigation approaches. In light of the growing challenges of climate change and increases in urbanisation, combined with flood events that are resulting in greater damages and impacts to human life, these efforts are critical to ensure society is as best prepared for the next catastrophic floods.

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)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.314
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.029
GPT teacher head0.330
Teacher spread0.301 · 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 designNot applicable
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
Published2023
Admission routes2
Has abstractyes

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