Impacts of El Niño Southern Oscillation on flood risk and insured losses in New Zealand

<p>Global warming is expected to enhance El Niño Southern Oscillation (ENSO) with potential impacts on rainfall and flood risk in numerous countries of the Asia-Pacific region. Modeling studies have suggested that positive and negative ENSO phases may intensify by as much as 25% under extreme climate projections. However, the influence of ENSO variability on flood risk in Asia-Pacific countries is still largely unexplored. Here, we aim to shed light into the link between ENSO, flood risk, and insured losses in New Zealand by combining rainfall observations and state-of-the-art flood risk models. We draw on 60 years of daily precipitation measurements to quantify the statistical correlations between the rainfall principal components and the ENSO historical time series. This allows us to generate 50,000 years of stochastic daily rainfall maps correlated with a long-term, synthetic ENSO time series. The stochastic precipitation maps are then used to drive streamflow and flood simulations at 20 m spatial resolution. Our results indicate that positive and negative ENSO phases increase the flood risk in different regions of New Zealand, and that extreme ENSO events tend to cause more severe flood events. We finally investigate the potential differences in economic losses during positive and negative ENSO phases by combining modeled flood footprints with exposure and vulnerability data. These results may guide the implementation of effective adaptation and mitigation strategies against the increasing risk of flood events in warming climate.</p>

Skilful predictions of multi-year US hurricane insured losses by decadal prediction systems

<p>North Atlantic tropical cyclones are the costliest natural hazard affecting the US, and are capable of causing hundreds of billions of dollars of insured losses in a single season.  Tropical cyclone activity has been observed to show considerable decadal variability, linked with variations in sea surface temperatures in regions of the North Atlantic such as the main hurricane development region (MDR) and sub-polar gyre (SPG).</p><p>In this presentation we show that a multi-model ensemble of decadal prediction systems can skilfully predict north Atlantic hurricane activity and consequent US insured losses on multi-annual timescales, with a correlation coefficient of greater than 0.7 for 5 year mean hurricane activity.  Rather than tracking tropical cyclones directly in the dynamical models, we make predictions using an index based on predicted temperatures over the north Atlantic.  The skill of the dynamical models outperforms persistence, and could aid decision making for the (re)insurance industry over the US.  As part of the Copernicus Climate Change Service, a publicly available probabilistic forecast of 5 year mean north Atlantic hurricane activity and US insured losses has been produced and will be presented here.</p>

The Effect of El Niño on Flood Damages in the Western United States

This paper quantifies insured flood losses across the western United States from 1978 to 2017, presenting a spatiotemporal analysis of National Flood Insurance Program (NFIP) daily claims and losses over this period. While considerably lower (only 3.3%) than broader measures of direct damages measured by a National Weather Service (NWS) dataset, NFIP insured losses are highly correlated to the annual damages in the NWS dataset, and the NFIP data provide flood impacts at a fine degree of spatial resolution. The NFIP data reveal that 1% of extreme events, covering wide spatial areas, caused over 66% of total insured losses. Connections between extreme events and El Niño–Southern Oscillation (ENSO) that have been documented in past research are borne out in the insurance data. In coastal Southern California and across the Southwest, El Niño conditions have had a strong effect in producing more frequent and higher magnitudes of insured losses, while La Niña conditions significantly reduce both the frequency and magnitude of losses. In the Pacific Northwest, the opposite pattern appears, although the effect is weaker and less spatially coherent. The persistent evolution of ENSO offers the possibility for property owners, policy makers, and emergency planners and responders that unusually high or low flood damages could be predicted in advance of the primary winter storm period along the West Coast. Within the 40-yr NFIP history, it is found that the multivariate ENSO index would have provided an 8-month look-ahead for heightened damages in Southern California.

High-impact thunderstorms of the Brisbane metropolitan area

Accurate thunderstorm warnings in the hours-to-minutes preceding impact are often limited by the complex evolution of the mesoscale atmospheric environment. To accurately capture these complexities, analysis of observations remained central to operational short-term nowcasting predictions of thunderstorms. Over the past 40 years, multiple highimpact thunderstorm events have impacted the Brisbane Metropolitan Area (BMA) of South East Queensland resulting in significant insured losses. Four of these high-impact events were the focus of the following work. These cases included three events that resulted in the greatest insured losses for the BMA, exceeding AU$4 billion (2017) (18 January 1985, 16 November 2008 and 27 November 2014) and a fourth significant event (24 December 1989). Synthesis of previous work indicates that the four high-impact cases occurred during a south-easterly change with strengthening winds ahead of the change, suggesting commonalities may exist that can be exploited for forecasting. This paper provides a detailed observational analysis of the environment and convective storms from the four BMA events to explore discriminating characteristics that may improve the skill of nowcasting. For the four BMA events, significant deep convection was observed along the change for the hours prior to the change’s arrival at the Brisbane Airport, potentially acting as an early indicator of favourable conditions for high-impact thunderstorms. It was found that the timing of the south-easterly change through Brisbane was also highly correlated for all events, occurring within a 90-min window during the mid-afternoon convective heating maximum. Despite the destructive severe weather, upper air conditions were marginal for supporting organised thunderstorms, highlighting the importance of capturing mesoscale processes, such as the south-easterly change. To further understand possible discriminators of the four high-impact BMA cases, a 10-year climatology of the mesoscale and synoptic environment associated with south-easterly change events was developed for the warm season months of November to January. It is shown that although only a small number of events are associated with high-impact BMA thunderstorms, these events share a set of conditions relating to the prechange wind shift, timing of the south-easterly change and radar signatures.

Modelling of Insured Losses of Natural Catastrophes Using Block Maxima Model

Catastrophic events have a huge impact on society as a whole. Insurance, or reinsurance is one way of reducing the economic consequences of catastrophic events. By Sigma Swiss Re criteria the event can be noted as a catastrophe when the economic losses, insured claims or casualties associated with an event exceed just one of the thresholds. These thresholds are updated every year. We can observe a growing trend in both the number of catastrophic events as well as in total economic losses and insured losses too. Risk management of insurance and reinsurance companies have to have available relevant information for estimation and adjusting premium to cover these risks. The aim of this article is to present one of the useful method – block maxima method. This method uses information from historical events about insured losses of natural catastrophes and estimates future insured losses. These estimates are very important for actuaries and for risk managers as it is one of the bases for calculating and adjusting premiums of products covering these types of risks.

Hail and Hailstorms

Hail has been identified as the largest contributor to insured losses from thunderstorms globally, with losses costing the insurance industry billions of dollars each year. Yet, of all precipitation types, hail is probably subject to the largest uncertainties. Some might go so far as to argue that observing and forecasting hail is as difficult, if not more difficult, than is forecasting tornadoes. The reasons why hail is challenging are many and varied and reflected by the fact that hailstones display a wide variety of shapes, sizes and internal structures. There is also an important clue in this diversity—nature is telling us that hail can grow by following a wide variety of trajectories within thunderstorms, each having a unique set of conditions. It is because of this complexity that modeling hail growth and forecasting size is so challenging. Consequently, it is understandable that predicting the occurrence and size of hail seems an impossible task. Through persistence, ingenuity and technology, scientists have made progress in understanding the key ingredients and processes at play. Technological advances mean that we can now, with some confidence, identify those storms that very likely contain hail and even estimate the maximum expected hail size on the ground hours in advance. Even so, there is still much we need to learn about the many intriguing aspects of hail growth.

Financing losses from natural and man-made disasters by use of crowdfunding

The level of insured losses from natural and man-made disasters occurred in the world from 2012 to 2016 did not exceed 30-45%. Therefore, it is necessary to study another perspective source of financing losses refunding from natural and man-made disasters. The objective of this research is to consider financing losses from natural and man-made disasters by use of crowdfunding, especially in emerging countries. It was defined that the most appropriate model of crowdfunding for financing losses from natural and man-made disasters is donation model with reward-based and donation-based business models. Stimulus for individuals to take part in crowdfunding for financing losses from natural and man-made disasters can be different depending on their location and business model. Sets of assessments based on four categories of questions and method of results visualization were used to examine a country’s readiness for crowdfunding on the example of Ukraine. Complete level of Ukraine’s readiness for crowdfunding was defined. It shows that reward-based crowdfunding is the first stage towards crowdfunding implementation and development. Further research should be done to investigate the mechanism of using a tax discount in case of implementation of the reward-based crowdfunding for financing losses from natural and man-made disasters.