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Compound hazards refer to two or more different natural hazards occurring over the same time period and spatial area. Compound hazards can operate on different spatial and temporal scales than their component single hazards. This article proposes a definition of compound hazards in space and time, presents a methodology for the spatiotemporal identification of compound hazards (SI–CH), and compiles two compound-hazard-related open-access databases for extreme precipitation and wind in Great Britain over a 40-year period. The SI–CH methodology is applied to hourly precipitation and wind gust values for 1979–2019 from climate reanalysis (ERA5) within a region including Great Britain and the British Channel. Extreme values (above the 99 % quantile) of precipitation and wind gust are clustered with the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, creating clusters for precipitation and wind gusts. Compound hazard clusters that correspond to the spatial overlap of single hazard clusters during the aggregated duration of the two hazards are then identified. We compile these clusters into a detailed and comprehensive ERA5 Hazard Clusters Database 1979–2019 (given in the Supplement), which consists of 18 086 precipitation clusters, 6190 wind clusters, and 4555 compound hazard clusters for 1979–2019 in Great Britain. The methodology's ability to identify extreme precipitation and wind events is assessed with a catalogue of 157 significant events (96 extreme precipitation and 61 extreme wind events) in Great Britain over the period 1979–2019 (also given in the Supplement). We find good agreement between the SI–CH outputs and the catalogue with an overall hit rate (ratio between the number of joint events and the total number of events) of 93.7 %. The spatial variation of hazard intensity within wind, precipitation, and compound hazard clusters is then visualised and analysed. The study finds that the SI–CH approach (given as R code in the Supplement) can accurately identify single and compound hazard events and represent spatial and temporal properties of these events. We find that compound wind and precipitation extremes, despite occurring on smaller scales than single extremes, can occur on large scales in Great Britain with a decreasing spatial scale when the combined intensity of the hazards increases.

Multi-hazard events have the potential to cause damages to infrastructures and people that may differ greatly from the associated risks posed by singular hazards. Interrelations between natural hazards also operate on different spatial and temporal scales than single natural hazards. Therefore, the measure of spatial and temporal scales of natural hazard interrelations still remain challenging. The objective of this study is to refine and measure temporal and spatial scales of natural hazards and their interrelations by using a spatiotemporal clustering technique. To do so, spatiotemporal information about natural hazards are extracted from the ERA5 climate reanalysis. We focus here on the interrelation between two natural hazards (extreme precipitation and extreme wind gust) during the period 1969-2019 within a region including Great Britain and North-West France. The characteristics of our input data (i.e. important size, high noise level) and the absence of assumption about the shape of our hazard clusters guided the choice of a clustering algorithm toward the DBSCAN clustering algorithm. To create hazard clusters, we retain only extreme values (above the 99% quantile) of precipitation and wind gust. We analyse the characteristics (eg., size, duration, season, intensity) of single and compound events of rain and wind impacting our study area. We then measure the impact of the spatial and temporal scales defined in this study on the nature of the interrelation between extreme rainfall and extreme wind in the UK. We therefore demonstrate how this methodology can be applied to a different set of natural hazards.

Key Words: Compound events; Multi-hazards; Industry application; Multivariate extreme value theory.Abstract:Resilience of pre-existing and new-build infrastructure to natural hazards is of key interest for many different industries (e.g. energy, water, transport). In most situations, studies analyzing the risk posed by single natural hazards have already been undertaken and relevant protection measures have been implemented. However, when considering the potential impacts of compound events or multi-hazards there can be less confidence in which combinations need to be considered and how to estimate the risks associated to these multi-hazard scenarios. Certain industries (e.g. nuclear) have already undertaken several projects on the occurrence and risks posed by multi-hazards (e.g. ASAMPSA-E, NARSIS), whereas other industries are still trying to understand their risks and which questions need to be posed. The EDF Energy R&D UK Centre are part of an industry scheme funded by NERC called the Environmental Risks for Infrastructure Innovation Programme (ERIIP) which aims to connect academics to industrial organisations and undertake translational research. One of the key topics of further research identified by this group is the topic of compound events and multi-hazards. A recent review of knowledge on multi-hazards was undertaken by the British Geological Survey (BGS) which highlighted the state of knowledge across UK infrastructure owners. This presentation will start by summarizing this review to pull out some key themes for future research in this area. Then, two different ongoing research projects will be outlined which look to address the key themes coming out of the review. One project is attempting to better understand the different multivariate statistical methods that are available for assessing the probability of multi-hazards. The application of the different models outlined in this work will be shown on an example of extreme precipitation and wind speed. The other project aims to better understand the overarching meteorological conditions that can lead to compound flooding at coastal sites around the UK. This focuses less on estimating joint probabilities, but more on producing clear visualisations for end-users.

Modelling multiple hazard interrelations remains a challenge for practitioners. This article primarily focuses on the interrelations between pairs of hazards. The efficacy of six distinct bivariate extreme models is evaluated through their fitting capabilities to 60 synthetic datasets. The properties of the synthetic datasets (marginal distributions, tail dependence structure) are chosen to match bivariate time series of environmental variables. The six models are copulas (one non-parametric, one semi-parametric, four parametric). We build 60 distinct synthetic datasets based on different parameters of log-normal margins and two different copulas. The systematic framework developed contrasts the model strengths (model flexibility) and weaknesses (poorer fits to the data). We find that no one model fits our synthetic data for all parameters but rather a range of models depending on the characteristics of the data. To highlight the benefits of the systematic modelling framework developed, we consider the following environmental data: (i) daily precipitation and maximum wind gusts for 1971 to 2018 in London, UK, and (ii) daily mean temperature and wildfire numbers for 1980 to 2005 in Porto District, Portugal. In both cases there is good agreement in the estimation of bivariate return periods between models selected from the systematic framework developed in this study. Within this framework, we have explored a way to model multi-hazard events and identify the most efficient models for a given set of synthetic data and hazard sets.

In low-lying coastal regions, flooding arises from oceanographic (storm surges plus tides and/or waves), fluvial (increased river discharge), and/or pluvial (direct surface run-off) sources. The adverse consequences of a flood can be disproportionately large when these different sources occur concurrently or in close succession, a phenomenon that is known as “compound flooding”. In this paper, we assess the potential for compound flooding arising from the joint occurrence of high storm surge and high river discharge around the coast of the UK. We hypothesise that there will be spatial variation in compound flood frequency, with some coastal regions experiencing a greater dependency between the two flooding sources than others. We map the dependence between high skew surges and high river discharge, considering 326 river stations linked to 33 tide gauge sites. We find that the joint occurrence of high skew surges and high river discharge occurs more frequently during the study period (15–50 years) at sites on the south-western and western coasts of the UK (between three and six joint events per decade) compared to sites along the eastern coast (between zero and one joint events per decade). Second, we investigate the meteorological conditions that drive compound and non-compound events across the UK. We show, for the first time, that spatial variability in the dependence and number of joint occurrences of high skew surges and high river discharge is driven by meteorological differences in storm characteristics. On the western coast of the UK, the storms that generate high skew surges and high river discharge are typically similar in characteristics and track across the UK on comparable pathways. In contrast, on the eastern coast, the storms that typically generate high skew surges are mostly distinct from the types of storms that tend to generate high river discharge. Third, we briefly examine how the phase and strength of dependence between high skew surge and high river discharge is influenced by the characteristics (i.e. flashiness, size, and elevation gradient) of the corresponding river catchments. We find that high skew surges tend to occur more frequently with high river discharge at catchments with a lower base flow index, smaller catchment area, and steeper elevation gradient. In catchments with a high base flow index, large catchment area, and shallow elevation gradient, the peak river flow tends to occur several days after the high skew surge. The previous lack of consideration of compound flooding means that flood risk has likely been underestimated around UK coasts, particularly along the south-western and western coasts. It is crucial that this be addressed in future assessments of flood risk and flood management approaches.

Globally and yearly, individual hazards and hazard interrelations have the potential to result in socio-economic losses. Here, in this critical review, we use grey- and peer-review literature to identify and compare current research available for the quantification of hazard interrelations, focussing on 14 different natural hazards. We first provide a historical context for quantitative single hazard and multi-hazard assessment. We then construct a literature database with 146 references related to multi-hazard interrelations. We use our literature database to identify trends for hazard interrelation and multi-hazard and from these group hazard interrelations into five types: triggering, change condition, compound, independence and mutually exclusive. Our critical review identifies 19 different modelling methods to quantify natural hazard interrelationships which we cluster into three broad modelling approaches: stochastic, empirical, and mechanistic. We then synthesize results of our classification of quantification methods for hazard interrelationships and using two matrices illustrate this in practice for 24 different interrelations between 14 natural hazards, one for cascading hazards (temporal order in the multi-hazard event) and one for compound hazards (two or more hazards acting together). Finally, we provide examples of applications for each the three quantitative modelling approaches defined. We believe that this review will lead to a better understanding of quantification methodologies for hazard interrelations between different sub-disciplines that focus on natural hazards, thus aiding cross-disciplinary approaches for better understanding potential risk related to multi-hazard events.