Abstract Flooding is one of the costliest weather-related disasters. In a warmer, wetter world, future flood events are expected to become more frequent and severe. Previous research shows that flooding devalues property in England, but the effect on length of home ownership is unexplored. We analysed 27 million residential property sales over 28 years in England to examine how flood zoning, nearby flooding, or inundation affected ownership length and property values. We find that flooded properties typically lose 3% of their value immediately, rising to a 10% reduction after 15 years. This aggregate £5.6 billion loss persists because floods are not forgotten, suggesting the market has moved beyond amnesia-driven rebound effects towards sustained repricing of flood risk. Moreover, flood-affected properties are owned on average for a decade longer than those in unaffected areas, especially among lower-priced homes. Our results have implications beyond England for housing markets, mortgage lending and insurers.

ABSTRACTInsurers and risk managers for critical infrastructure such as transport or power networks typically do not account for flooding and extreme winds happening at the same time in their quantitative risk assessments. We explore this potentially critical underestimation of risk from these co‐occurring hazards through studying events using the regional 12 km resolution UK Climate Projections for a 1981–1999 baseline and projections of 2061–2079 (RCP8.5). We create a new wintertime (October–March) set of 3427 wind events to match an existing set of fluvial flow extremes and design innovative multi‐event episodes (Δt of 1–180 days long) that reflect how periods of adverse weather affect society (e.g., through damage). We show that the probability of co‐occurring wind‐flow episodes in Great Britain (GB) is underestimated 2–4 times if events are assumed independent. Significantly, this underestimation is greater both as severity increases and episode length reduces, highlighting the importance of considering risk from closely consecutive storms (Δt ~ 3 days) and the most severe storms. In the future (2061–2079), joint wind‐flow extremes are twice as likely as during 1981–1999. Statistical modelling demonstrates that changes may significantly exceed thermodynamic expectations of higher river flows in a wetter future climate. The largest co‐occurrence increases happen in mid‐winter (DJF) with changes in the North Atlantic jet stream an important driver; we find the jet is strengthened and squeezed into a southward‐shifted latitude window (45°–50° N) giving typical future conditions that match instances of high flows and joint extremes impacting GB today. This strongly implies that the large‐scale driving conditions (e.g., jet stream state) for a multi‐impact ‘perfect storm’ will vary by country; understanding regional drivers of weather hazards over climate timescales is vital to inform risk mitigation and planning (e.g., diversification and mutual aid across Europe).

Around midnight on 1/2 November 2023, Jersey (Channel Islands) was impacted by a supercell storm which produced both a tornado, rated T6/IF3, and very large hail. This article presents details of that remarkable hailstorm and places it into context by investigating severe hail events during the October-March cool season in the UK and Crown Dependencies of Jersey, Guernsey, and the Isle of Man. The storm is compared to previous 'dual-hazard' events during the cool season, where both a tornado and severe hail were produced.

Severe or large hail, with diameter >= 20 mm, is a hazard associated with severe convective storms that can cause significant damage. In the UK, the rarity and small footprint of severe hail events makes obtaining welldocumented hail reports difficult, and the reports are spread across multiple databases. In this study, three databases of UK severe hail reports are merged for the first time. The combined event set (1979-2022), comprising >800 reports, is used to investigate interannual variability and the seasonal, spatial and size distributions of severe hail. The seasonal cycle peaks in early-mid summer, and the peak month has shifted from June to July since around 2005. The distribution of reported hail size is exponential, with a slower decay (larger hail) during summer. The time of day, basic convective mode (isolated, clustered or linear), and presence or absence of supercellular characteristics are assessed for 274 of the reports since 2006, using composite radar rainrate data. The diurnal cycle is strong year-round, peaking during the late afternoon (1500-1800 UTC). 53% of severe hail events are associated with isolated cells, 33% with clusters, and 14% with linear storms. Around 35% of severe hail-producing storms are probable supercells, increasing to 70% for storms producing >= 40 mm hail. This demonstrates that the prevalence of supercells producing very large hail extends to temperate maritime climates. These results may be of relevance in other regions with a relatively low incidence of severe hail in the present climate. This comprehensive analysis of severe and potentially impactful hail in the UK provides novel insight into its characteristics, enabling improved assessment of climate risk from this hazard.

Extreme wind is the main driver of loss in North-West Europe, with flooding being the second-highest driver. These hazards are currently modelled independently, and it is unclear what the contribution of their co-occurrence is to loss. They are often associated with extra-tropical cyclones, with studies focusing on co-occurrence of extreme meteorological variables. However, there has not been a systematic assessment of the meteorological drivers of the co-occurring impacts of compound wind-flood events. This study quantifies this using an established storm severity index (SSI) and recently developed flood severity index (FSI), applied to the UKCP18 12 km regional climate simulations, and a Great Britain (GB) focused hydrological model. The meteorological drivers are assessed using 30 weather types, which are designed to capture a broad spectrum of GB weather. Daily extreme compound events (exceeding 99th percentile of both SSI and FSI) are generally associated with cyclonic weather patterns, often from the positive phase of the North Atlantic Oscillation (NAO+) and Northwesterly classifications. Extreme compound events happen in a larger variety of weather patterns in a future climate. The location of extreme precipitation events shifts southward towards regions of increased exposure. The risk of extreme compound events increases almost four-fold in the UKCP18 simulations (from 14 events in the historical period, to 55 events in the future period). It is also more likely for there to be multi-day compound events. At seasonal timescales years tend to be either flood-prone or wind-damage-prone. In a future climate there is a larger proportion of years experiencing extreme seasonal SSI and FSI totals. This could lead to increases in reinsurance losses if not factored into current modelling.

The risk posed by heavy rain and strong wind is now suspected to be exacerbated by the way they co-occur, yet this remains insufficiently understood to effectively plan and mitigate. This study systematically investigates the correlations between wintertime (Oct–Mar) extremes relating to wind and flooding at all timescales from daily to seasonal. Meteorological reanalysis and river flow datasets are used to explore the historical period, and climate projections at 12 km resolution are analysed to understand the possible effects of future climate change (2061–2080, RCP 8.5). A new flood severity index (FSI) is also developed to complement the existing storm severity index (SSI). Initially, Great Britain (GB) is taken as a comparatively simple yet informative study area, then analysis is extended to the full European domain.Aggregated across GB, wind gusts and precipitation correlate strongly (rs ∼0.6–0.8) at timescales from daily to seasonal, but peak around 10 days. A later peak is seen when considering correlations between wind gusts and river flows (40–60 days). This time is likely needed for catchments’ soils to saturate. A conceptual multi-temporal, multi-process model of GB wintertime flood-wind co-occurrence is proposed as a basis for future investigation. When historical analysis is extended across Europe we find the timescale of maximum correlation varies strongly between nations, likely as a result of different meteorological drivers.Impact focused correlation (FSI–SSI) is lower (rs∼0.2) but increases notably with climate change at timescales of ∼40 days (rs∼0.4). Tentatively, very severe episodes (i.e., both >99th percentile) appear heavily influenced by climate change, increasing roughly threefold by 2061–2080 (p < 0.05). The return period of such an event is 16 years historically (compared to 56 years if the two hazards were independent), reduces to 5 years in future. Such metrics provide actionable information for insurers and other stakeholders.

Large hail, with a diameter of at least 20 mm, is a hazard associated with severe convective storms (SCS) that can cause significant damage. Understanding of atmospheric environments conducive to large hail is underpinned by catalogues of past events. Because of the small footprint of hail events, these often rely on crowdsourced reports. In the UK, the relative rarity of large hail and low public awareness of SCS hazards makes obtaining a complete set of reports difficult, and in many cases the precise time of the hail is not recorded. In this study, the two major databases of UK large hail reports are merged for the first time. Composite radar reflectivity data are used to verify and enhance 260 reports since 2006. Time of the hail and the basic storm mode (isolated, clustered or linear) are visually estimated from animations. Compared to the UK’s most severe historic hailstorms (1800–2004), our quality controlled climatology of all sizes of large hail shows a diurnal cycle with a slightly broader peak. Around 55% of large hail events are associated with isolated cells, while 34% have supercellular characteristics, a much lower proportion than found in the USA. The full event set (1979–2022), comprising over 850 reports, is used to update the seasonal, spatial and size distributions of large hail in the UK. We intend that this hail event set forms part of a multi-hazard analysis of UK SCS, also including tornadoes and extreme rainfall, and its relationship to background atmospheric conditions. The effect of climate change on UK SCS will be investigated through past and future trends in these background conditions.

There is growing evidence that physical climate hazards—such as floods and wildfires—affect property prices. Climate change scenarios suggest more frequent and severe physical climate hazards in the future, coinciding with greater exposure of populations to such threats. This raises concern because changes in property prices pose risks to homeowners' financial status, as well as to the insurance and mortgage industries, bank portfolios, and thereby financial systems. We begin with a new definition of climate gentrification (CG) that captures links between physical climate hazards, perceptions of risk and resilience, and capital flows in property markets. This is followed by a structured assessment of the key drivers of CG, and an empirical case study of property data for a flood-prone UK city to demonstrate how CG depressed house price growth over the period from 2005 to 2018 by up to 50 percent in flood-exposed (relative to unexposed) locations. We then provide a discussion of ethical concerns around CG research, with suggested ways forward. Such price signals have potential ramifications for the long-term stability of real estate markets and raise policy implications for private and public sectors. We conclude with some priorities for further research into CG, recognizing key information and data gaps, and noting how existing knowledge and tools could contribute toward improved resilience to climate change. Key Words: climate gentrification, climate hazards, flood, hedonic model, property prices.

This article is an illustration of Geography in action, recounting an investigation into an industry's views of data sharing. The insurance sector is fundamentally analytics driven and based on geospatial data. One option for more effective and efficient insurance for natural hazard risks (e.g. flooding, earthquake) is, in theory, to increase the sharing of data between the various (re)insurance organisations. However, it remains unclear to what extent this is desirable or practical for commercially sensitive data. This work creates a conceptual model of data sharing in (re)insurance, focussing on loss (claims) data for natural hazards as an illustrative microcosm, including barriers and solutions to sharing. In light of this, an initial view on the future shape of insurance data sharing is given, finishing with an opinion on whether or not new external disruptors (start-ups, tech giants - e.g. Google, Amazon, Tencent) pose an existential threat to incumbent firms.

If you are a geoscientist doing work to achieve impact outside academia or engaging different audiences with the geosciences, are you planning to make this publishable? If so, then plan. Such investigations into how people (academics, practitioners, other publics) respond to geoscience can use pragmatic, simple research methodologies accessible to the non-specialist or be more complex. To employ a medical analogy, first aid is useful and the best option in some scenarios, but calling a medic (i.e. a collaborator with experience of geoscience communication or relevant research methods) provides the contextual knowledge to identify a condition and opens up a diverse, more powerful range of treatment options. Here, we expand upon the brief advice in the first editorial of Geoscience Communication (Illingworth et al., 2018), illustrating what constitutes robust and publishable work in this context, elucidating its key elements. Our aim is to help geoscience communicators plan a route to publication and to illustrate how good engagement work that is already being done might be developed into publishable research.

Extreme multi-basin fluvial flows and their relationship to extra-tropical cyclones [Abstract]

Global economic losses related to natural hazards are large and increasing, peaking at US$380 billion in 2011 driven by earthquakes in Japan and New Zealand and flooding in Thailand. Catastrophe models are stochastic event-set based computer models, first created 25 years ago, that are now vital to risk assessment within the insurance and reinsurance industry. They estimate likely losses from extreme events, whether natural or man-made. Most catastrophe models limit the level of user interaction, stereotyped as 'black boxes'. In this paper we investigate how model fusion techniques could be used to develop 'plug and play' catastrophe models and discuss the impact of open access modelling on the insurance industry and other stakeholders (e.g. local government).

Physical processes, including anthropogenic feedbacks, sculpt planetary surfaces (e.g. Earth's). A fundamental tenet of geomorphology is that the shapes created, when combined with other measurements, can be used to understand those processes. Artificial or synthetic digital elevation models (DEMs) might be vital in progressing further with this endeavour in two ways. First, synthetic DEMs can be built (e.g. by directly using governing equations) to encapsulate the processes, making predictions from theory. A second, arguably underutilised, role is to perform checks on accuracy and robustness that we dub "synthetic tests". Specifically, synthetic DEMs can contain a priori known, idealised morphologies that numerical landscape evolution models, DEM-analysis algorithms, and even manual mapping can be assessed against. Some such tests, for instance examining inaccuracies caused by noise, are moderately commonly employed, whilst others are much less so. Derived morphological properties, including metrics and mapping (manual and automated), are required to establish whether or not conceptual models represent reality well, but at present their quality is typically weakly constrained (e.g. by mapper inter-comparison). Relatively rare examples illustrate how synthetic tests can make strong "absolute" statements about landform detection and quantification; for example, 84 % of valley heads in the real landscape are identified correctly. From our perspective, it is vital to verify such statistics quantifying the properties of landscapes as ultimately this is the link between physics-driven models of processes and morphological observations that allows quantitative hypotheses to be tested. As such the additional rigour possible with this second usage of synthetic DEMs feeds directly into a problem central to the validity of much of geomorphology. Thus, this note introduces synthetic tests and DEMs and then outlines a typology of synthetic DEMs along with their benefits, challenges, and future potential to provide constraints and insights. The aim is to discuss how we best proceed with uncertainty-aware landscape analysis to examine physical processes.

In this paper we show that integrated environmental modelling (IEM) techniques can be used to generate a catastrophe model for groundwater flooding. Catastrophe models are probabilistic models based upon sets of events representing the hazard and weights their likelihood with the impact of such an event happening which is then used to estimate future financial losses. These probabilistic loss estimates often underpin re-insurance transactions. Modelled loss estimates can vary significantly, because of the assumptions used within the models. A rudimentary insurance-style catastrophe model for groundwater flooding has been created by linking seven individual components together. Each component is linked to the next using an open modelling framework (i.e. an implementation of OpenMI). Finally, we discuss how a flexible model integration methodology, such as described in this paper, facilitates a better understanding of the assumptions used within the catastrophe model by enabling the interchange of model components created using different, yet appropriate, assumptions.

Mapped topographic features are important for understanding processes that sculpt the Earth's surface. This paper presents maps that are the primary product of an exercise that brought together 27 researchers with an interest in landform mapping wherein the efficacy and causes of variation in mapping were tested using novel synthetic DEMs containing drumlins. The variation between interpreters (e.g. mapping philosophy, experience) and across the study region (e.g. woodland prevalence) opens these factors up to assessment. A priori known answers in the synthetics increase the number and strength of conclusions that may be drawn with respect to a traditional comparative study. Initial results suggest that overall detection rates are relatively low (34–40%), but reliability of mapping is higher (72–86%). The maps form a reference dataset.