Cloudburst catastrophe modelling: Case study – Jönköping municipality, Sweden

<p>In Europe, flash floods are one of the most significant natural hazards, causing serious risk to life and destruction of buildings and infrastructure. The intense rain causing those floods has a few different names, however, with very similar meaning. The term chosen in this study, ‘cloudburst’, was introduced by Woolley (1946) as “…a torrential downpour of rain which by its spottiness and relatively high intensity suggests the bursting and discharge of the whole cloud at once”. While these events play an important role in the ongoing flood risk management discussion, they are under-represented among flood models.</p><p>The main aim of this study is to demonstrate an approach by showing how methods and techniques can be integrated together to construct a catastrophe model for flash flooding of Jönköping municipality in Sweden. The model is developed in the framework of the ‘Oasis Loss Modelling Framework’ platform, jointly with end-users from the public sector and the insurance industry. Calibration and validation of the model were conducted by comparisons against three historical cloudburst events and corresponding insurance-claim data.</p><p>The analysis has shown that it is possible to get acceptable results from a cloudburst catastrophe model using only rainfall data, and not surface-water level as driving variable. The approach presented opens up for such loss modelling in places where complex hydraulic modelling cannot be done because of lacking data or skill of responsible staff. The Swedish case study indicates that the framework presented can be considered as an important decision making tool, by establishing an area for collaboration between academia; insurance businesses; and local authorities, to reduce long-term disaster risk in Sweden.</p><p> </p><p>Woolley, Ralf R., "Cloudburst Floods in Utah 1850-1938" (1946). Elusive Documents. Paper 55.</p>

What value do mangroves have in reducing the cost of storm surges?

<p>With coastlines becoming increasingly urbanised worldwide, the economic risk posed by storm surges to coastal communities has never been greater. Given the financial and ecological costs of manmade coastal defences, the past few years have seen growing interest in the effectiveness of natural coastal “defences” in reducing the risk of flooding to coastal properties, but estimating their actual economic value in reducing storm surge risk remains a challenging subject.</p><p>In this study, we estimate the value of mangroves in reducing annual losses to property from storm surges along a large stretch of coastline in Florida (USA), by employing a catastrophe modelling approach widely used in the insurance industry. We use a hydrodynamic coastal flood model coupled to a property loss model and a large property exposure dataset to estimate annual economic losses from hurricane-driven storm surges in Collier County, a hurricane-prone part of Florida. We then estimate the impact that removing mangroves in the region would have on average annual losses (AAL) caused by coastal flooding. We find that mangroves reduce AAL to properties behind them by over 25%, and that these benefits are distributed very heterogeneously along the coastline. Mangrove presence can also act to enhance the storm surge risk in areas where development has occurred seaward of mangroves.</p><p>In addition to looking at annual losses, we also focus on the storm surge caused by a specific severe event in Florida, based on Hurricane Irma (2017), and we estimate that existing mangroves reduced economic property damage by hundreds of millions of USD, and reduced coastal flooding for hundreds of thousands of people.</p><p>Together these studies aim to financially quantify some of the risk reduction services provided by natural defences in terms of reducing the cost of coastal flooding, and show that these services can be included in a catastrophe modelling framework commonly used in the insurance industry.</p>

Pluvial flood catastrophe modelling: a trans-disciplinary approach.

<p>Pluvial floods are one of the most significant natural hazards in Europe causing severe damage to urban areas. Following the projected increase in extreme precipitation and the ongoing urbanization, these events play an important role in the ongoing flood risk management discussion and provoke serious risk to the public as well as to the insurance sector. However, this type of flood, remains a poorly documented phenomenon. To address this gap, Swedish Pluvial Modelling Analysis and Safety Handling (SPLASH) project aims to develop new methods and types of data that improve the possibility to value flood risk in Swedish municipalities by collaboration between different disciplines.</p><p>SPLASH project allows to investigating the impact of heavy precipitation along the entire risk modelling chain, ultimate needed for effective prevention. This study presents a pluvial flood catastrophe modelling framework to identify and assess hazard, exposure and vulnerability in urban context. An integrated approach is adopted by incorporating ‘rainfall-damage’ patterns, flood inundation modelling, vulnerability tools and risk management. The project is developed in the ‘OASIS Loss Modelling Framework’ platform, jointly with end-users from the public sector and the insurance industry.</p><p>The Swedish case study indicates that the framework presented can be considered as an important decision making tool, by establishing an area for collaboration between academia; insurance businesses and rescue services, to reduce long-term disaster risk in Sweden.</p>

Including climatic variability in stochastic rainfall for flood catastrophe modelling – The effect of ENSO and SOI in China

<p>Damages from flooding in China account on average for 60-70% of the total Annual Losses derived from natural catastrophes. The extreme rainfall events responsible for these inundations can be broadly categorised in two well differentiated mechanisms: Tropical Cyclone (TC) induced, and non Tropical Cyclone induced (nonTC) precipitation. Between 2001 and 2015, inland nonTC rainfall flood events occurred roughly with double the frequency as TC events. While TC events can be highly destructive for coastal locations, over the entire China territory nonTC flooding accounted for more than half of the total economic flood loss for events with significant socio-economic impact, highlighting the importance of the nonTC flooding mechanism on the regional and national scale.</p><p>Large-scale modes of climate variability modulate in different ways TC and nonTC induced precipitation, both in the frequency and the magnitude of the events. In a stochastic rainfall generation framework, it becomes therefore useful to model these two mechanisms separately and include their differentiated long-term climatic influences in order to fully reproduce the overall observed rainfall variability. This work presents results on the effect of ENSO and Southern Oscillation Index (SOI) values on seasonal rainfall in China, and how to include this climatic variability in stochastic rainfall for flood catastrophe modelling.</p>

Climate change, historical data and catastrophe modelling

<p>This presentation will discuss climate change in the context of catastrophe modelling and tail risk. Given that the catastrophe modelling industry typical only has short historical records that provide limited information as to whether hazard is non-stationary, what are the methods and datasets that may aid the catastrophe modelling community to better understand how and whether risk is changing temporally? </p><p>The issues will be framed by using examples of output from a multi-year multi-ensemble 60km global climate simulation, where extra-tropical windstorm daily maximum gust data has been converted into yearly aggregate European insurance loss with the help of PERILS European industry exposure data. The data is used to show how reliance on single historical datasets can produce misleading trends in catastrophe losses - but also potentially point to underlying trends in risk that single historical datasets may not be able to detect.</p>

Global Flood Model: Revolutionising Flood Catastrophe Modelling

<p>Catastrophe models are well established tools, traditionally used by the re/insurance industry to assess the financial risk to insured property (“exposure”) associated with natural perils. Catastrophe modelling is challenging, particularly for flood perils over large geographical scales, for a number of reasons. To adequately capture the fine spatial variability of flood depth, a flood catastrophe model must be of high spatial resolution. To validly compare estimates of risk obtained from catastrophe models for different geographical regions, those models must be built from geographically consistent data. To compare estimates of risk between any given collection of geographical regions globally, global coverage is required.</p><p>Traditional catastrophe models struggle to meet these requirements; compromises are made, often for performance reasons.  In addition, traditional models are typically static datasets, built in a discrete process prior to their use in exposure risk assessment. Scientific assumptions are therefore deeply embedded; there is little scope for the end user to adjust the model based on their own scientific knowledge.</p><p>This research presents a new and better approach to catastrophe modelling that addresses these challenges and, in doing so, has allowed creation of the world’s first global flood catastrophe model.</p><p>JBA’s Global Flood Model is facilitated by a technological breakthrough in the form of JBA’s <strong>FLY</strong> Technology. The innovations encoded in <strong>FLY</strong> have enabled JBA to create a model capable of consistent global probabilistic flood risk assessment, operating at 30m resolution and supported by a catalogue of 15 million distinct flood events (both river and surface water). <strong>FLY </strong>brings a model to life dynamically, from raw flood hazard data, simultaneously addressing the user configurability and performance challenges.</p><p>Global Flood Model and <strong>FLY</strong> Technology will be of interest to those involved in financial, economic or humanitarian risk assessment, particularly in and between countries and regions not covered by flood catastrophe models to date. The detail of how they work will be covered here, and their power in facilitating consistent global flood risk assessment will be demonstrated.</p>

Correlating surface water flood damages in three Indonesia cities

<p>Despite a high growth rate of over 5%, the insurance penetration rate in Indonesia is low, at roughly 2.77 percent and is one of the least developed insurance market among ASEAN economies. A primary explanation for the lack of motivation for taking up insurance is due to the lack of understanding of the multitude of risks from natural hazards the Indonesian market faces, principally of flooding. The purpose of this research is to assess the flood correlation between three of the major cities (Jakarta, Semarang, and Solo) on the island of Java. These highly populated and financial centres of Indonesia are most prone to the rainfall extremes during the Monsoon Season (November – March), many of which causes flooding. All the historical rainfall events were extracted from ECMWF’s ERA-5 hourly rainfall dataset (1979 – 2018). The top 10 events for each city were selected based on peak rainfall intensity. For the selected events in a city, rainfall records of the same period were extracted for the other two cities. This results in 30 simulations per city. Using a 2D hydraulic modelling tool (FloodMap), surface water flood footprints were generated for the events. In the absence of depth-damage curves, the number of buildings flooded under each event were used as an approximation to building damages. Damage to buildings due to surface water flooding in Solo and Semarang were found to be most correlated, with a significant number of buildings flooded in both cities in 15 out of the 20 paired events. Solo and Jakarta show some correlation (7 out of 20) whilst flooding in Semarang and Jakarta are least correlated (4 out of 20). This study is an initial analysis relevant to the modelling of catastrophes in a relatively data sparse environment, providing an approximation of the correlation of flooding between three Indonesian cities. Further studies are required to develop pragmatic approaches to complement catastrophe modelling that integrate the spatial correlation between flood damages in cities.</p>

Practical guide to climate change for general insurance practitioners

Climate change is one of the biggest challenges facing the world. Scientific research points out that it is predominately driven by human activity. There are three different types of risks that arise from this change. These have been broadly grouped into physical, transition and liability risks. These risks can impact general insurers to different degrees, depending on their business areas and investment strategies. These may pose different strategic, investment, market, operational and reputational risks. This paper provides General Insurance Practitioners with an overview of different aspects of insurance operations that may be affected by climate change. It highlights the impact of these risks on pricing and underwriting, reserving, reinsurance, catastrophe modelling, investment, risk management and capital management processes.