Globally consistent assessment of economic impacts of wildfires in CLIMADA v2.2

Abstract. In light of the dramatic increase in economic impacts due to wildfires over recent years, the need for globally consistent impact modelling of wildfire damages is ever increasing. Insurance companies, individual households, humanitarian organisations and governmental authorities, as well as investors and portfolio owners, are increasingly required to account for climate-related physical risks. In this study we present a globally consistent and spatially explicit approach to modelling wildfire impacts using the open-source and open-access risk modelling platform CLIMADA (CLImate ADAptation). All input data is free, public and globally available, ensuring applicability in data-scarce regions of the Global South. The model was calibrated at resolutions of 1, 4 and 10 kilometers using information on past wildfire damage reported by the disaster database EM-DAT. Despite the large remaining uncertainties, the model yields sound damage estimates with a model performance well in line with the results of other natural catastrophe impact models, such as for tropical cyclones. To complement the global 10 perspective of this study, we conducted two case studies on the recent mega fires in Chile (2017) and Australia (2020). The model is made available online as part of a Python package, ready for application in practical contexts such as disaster risk assessment or physical climate risk disclosure.

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Globally consistent assessment of economic impacts of wildfires in CLIMADA v2.2

Geoscientific Model Development ◽

10.5194/gmd-14-7175-2021 ◽

2021 ◽

Vol 14 (11) ◽

pp. 7175-7187

Author(s):

Samuel Lüthi ◽

Gabriela Aznar-Siguan ◽

Christopher Fairless ◽

David N. Bresch

Keyword(s):

Climate Adaptation ◽

Economic Impacts ◽

Model Performance ◽

Global Perspective ◽

Insurance Companies ◽

Risk Disclosure ◽

Humanitarian Organizations ◽

Societal Challenges ◽

Impact Modelling ◽

Consistent Assessment

Abstract. In light of the dramatic increase in economic impacts due to wildfires over recent years, the need for globally consistent impact modelling of wildfire damages is ever increasing. Insurance companies, individual households, humanitarian organizations, governmental authorities, and investors and portfolio owners are increasingly required to account for climate-related physical risks. In response to these societal challenges, we present an extension to the open-source and open-access risk modelling platform CLIMADA (CLImate ADAptation) for modelling economic impacts of wildfires in a globally consistent and spatially explicit approach. All input data are free, public and globally available, ensuring applicability in data-scarce regions of the Global South. The model was calibrated at resolutions of 1, 4 and 10 km using information on past wildfire damage reported by the disaster database EM-DAT. Despite the large remaining uncertainties, the model yields sound damage estimates with a model performance well in line with the results of other natural catastrophe impact models, such as for tropical cyclones. To complement the global perspective of this study, we conducted two case studies on the recent megafires in Chile (2017) and Australia (2020). The model is made available online as part of a Python package, ready for application in practical contexts such as disaster risk assessment, near-real-time impact estimates or physical climate risk disclosure.

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Experiences with virtual schools on “Climate data for impact assessments”

10.5194/ems2021-353 ◽

2021 ◽

Author(s):

Janette Bessembinder ◽

Judith Klostermann ◽

Rutger Dankers ◽

Vladimir Djurdjevic ◽

Tomas Halenka

Keyword(s):

Summer School ◽

Climate Modeling ◽

Virtual Schools ◽

Virtual School ◽

Climate Data ◽

Climate Services ◽

Impact Modelling ◽

Set Up ◽

Impact Models

The provision of climate services to users is a fast developing field. In support of this development, the IS-ENES3 project, funded within the EC Horizon2020 program, organized three schools on &#8220;Climate data for impact assessments&#8221; in 2020 and 2021. In an Autumn school, a Spring school and a Summer school, climate scientists and impact scientists were brought together. An important aim of the schools was&#160;to enhance interaction between Vulnerability-Impact-Adaptation (VIA) researchers, climate services providers and climate researchers. Another aim was to provide an overview of information on climate modeling, climate data, impact modelling and climate services based on the work of the IS-ENE3 project.In the first three weeks a series of lectures was given, covering topics such as climate data and modelling, impact models, portals for accessing and processing climate data, setting-up impact assessments, and communication of results to stakeholders. In the last three weeks the participants worked in small groups of one climate scientist with one impact scientist on a case study under the guidance of the course lecturers. Impact and climate researchers were combined on purpose to let them experience how they could help each other.Originally the schools were planned to take place on-site (e.g. in Prague) during one week; however, due to COVID-19 the schools had to be transformed to virtual schools with two weekly sessions during six weeks. Although the virtual set-up had some disadvantages (e.g. less possibilities for networking), there were also some advantages (e.g. the possibility to record the lectures and make them available to a broader audience; more time to explore and work with climate data in between the sessions, no CO2 emissions for travelling). During this presentation we will present the set-up of the schools and the conversion to a virtual school. We will focus on the lessons learnt and the evaluation of the virtual schools by the participants and give some recommendations for similar schools and how to link the climate and VIA research communities .

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Comparing an insurer's perspective on building damages with modelled damages from pan-European winter windstorm event sets: a case study from Zurich, Switzerland

Natural Hazards and Earth System Science ◽

10.5194/nhess-21-279-2021 ◽

2021 ◽

Vol 21 (1) ◽

pp. 279-299

Author(s):

Christoph Welker ◽

Thomas Röösli ◽

David N. Bresch

Keyword(s):

Open Source ◽

Return Period ◽

Model Building ◽

Information Service ◽

Source Model ◽

Insurance Companies ◽

Risk Models ◽

Risk Modelling ◽

Modelling Approach

Abstract. With access to claims, insurers have a long tradition of being knowledge leaders on damages caused by windstorms. However, new opportunities have arisen to better assess the risks of winter windstorms in Europe through the availability of historic footprints provided by the Windstorm Information Service (Copernicus WISC). In this study, we compare how modelling of building damages complements claims-based risk assessment. We describe and use two windstorm risk models: an insurer's proprietary model and the open source CLIMADA platform. Both use the historic WISC dataset and a purposefully built, probabilistic hazard event set of winter windstorms across Europe to model building damages in the canton of Zurich, Switzerland. These approaches project a considerably lower estimate for the annual average damage (CHF 1.4 million), compared to claims (CHF 2.3 million), which originates mainly from a different assessment of the return period of the most damaging historic event Lothar–Martin. Additionally, the probabilistic modelling approach allows assessment of rare events, such as a 250-year-return-period windstorm causing CHF 75 million in damages, including an evaluation of the uncertainties. Our study emphasizes the importance of complementing a claims-based perspective with a probabilistic risk modelling approach to better understand windstorm risks. The presented open-source model provides a straightforward entry point for small insurance companies.

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NUKLEUS - User-relevant and applicable kilometer-scale climate information for Germany

10.5194/egusphere-egu21-12137 ◽

2021 ◽

Author(s):

Kevin Sieck ◽

Bente Tiedje ◽

Hendrik Feldmann ◽

Joaquim Pinto

Keyword(s):

Climate Models ◽

Local Scale ◽

Easy Access ◽

Climate Information ◽

Post Processing ◽

Last Mile ◽

Wide Range ◽

Impact Modelling ◽

The Impact ◽

Impact Models

Given the current developments in climate science it becomes more a more feasible to provide climate information at the kilometer-scale from convection-permitting climate simulations. This progress will enable many users to directly feed high-resolution climate information into their impact-models for climate impact studies at the local scale. Examples include urban heat stress at street level or the design of drainage systems for future precipitation extremes. Within the RegIKlim (Regional information for action on climate change) consortium, the NUKLEUS (Actionable local climate information for Germany) project will not only provide climate information at the local scale, but also to co-develop interfaces between climate and impact models, in order to fulfil the needs of the impact modelling community as good as possible. Within the RegIKlim consortium, the impact modelling community is organised in six &#8220;model regions&#8221; across Germany, which cover a wide range of geographical and socio-economic conditions.For the NUKLEUS project, the baseline will be the latest generation of EURO-CORDEX downscaled CMIP6 simulations, which will be further refined to roughly 3&#160;km horizontal resolution and 30-year time-slices for Germany with convection-permitting climate models (ICON CLM, COSMO-CLM, REMO-NH) and statistical-dynamical downscaling approaches. A detailed analysis on the performance of the multi-model mini-ensemble is planned to assess the quality of the provided data. At the interface to the users, we will follow three different approaches to provide usable climate information at the kilometer-scale. One is to provide easy-access to data and post-processing opportunities using the FREVA system. FREVA offers various access-levels from shell to web-based, which serves different levels of user-expertise. In addition, it provides a transparent way of post-processing data by workflow sharing mechanisms. The second one is to develop appropriate additional downscaling methods for the &#8220;last mile&#8221; where needed. For this &#8220;last mile&#8221;, we will apply dynamical and statistical methods such as urban climate models and/or weather generators. With the third approach we explicitly aim at integrating a collected user-feedback into the regional modelling systems used within NUKLEUS. Specifically, we intend to identify and incorporate data processing that is best done during the simulation permanently into the models. Examples are wind speeds at rotor heights of windmills or high frequency precipitation sums. NUKLEUS is a contribution to the German research program RegIKlim funded by the Federal Ministry of Education and Research (BMBF).

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Pluvial Flooding in Utrecht: On Its Way to a Flood-Proof City

Water ◽

10.3390/w11071501 ◽

2019 ◽

Vol 11 (7) ◽

pp. 1501 ◽

Cited By ~ 3

Author(s):

Brockhoff ◽

Koop ◽

Snel

Keyword(s):

Flood Risk ◽

Financial Incentives ◽

Climate Adaptation ◽

Citizen Engagement ◽

Public Authorities ◽

Governance Capacity ◽

Pluvial Flooding ◽

Local Actors ◽

Consistent Assessment ◽

The City

Downpours are increasing in frequency and severity due to climate change. Cities are particularly susceptible to flooding from downpours because of their large share of impervious surfaces. Minimising pluvial flood risk requires all involved stakeholders to collaborate and overcome various barriers. Although an increase in citizen engagement in climate adaptation is generally preferred, experiences with inclusive decision-making are often limited. The aim of this paper is to obtain a deeper understanding of how the capacity to govern pluvial flood risk can be developed through citizen engagement. We scrutinised the capacity of local actors to govern pluvial flood risk in the city of Utrecht, the Netherlands. For the analysis of Utrecht’s problem-solving capacity, the Governance Capacity Framework provided a consistent assessment of the key governance components. The results indicate that Utrecht’s capacity to govern pluvial flooding is relatively well-developed. Collaboration between public authorities is advanced, sufficient financial resources are available, and smart monitoring enables high levels of evaluation and learning. However, citizen awareness and engagement in policy making is rather low. Accordingly, citizens’ willingness to pay for flood adaptation is limited. Stimulating flood risk awareness by combining financial incentives with more advanced arrangements for active citizen engagement is key for Utrecht and other cities.

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Make Markets Work for Resilience

Building a Resilient Tomorrow ◽

10.1093/oso/9780190909345.003.0004 ◽

2019 ◽

pp. 56-76

Author(s):

Hill and

Keyword(s):

Real Estate ◽

Large Scale ◽

Insurance Companies ◽

Climate Risk ◽

Insurance Markets ◽

Risk Disclosure ◽

Climate Risks ◽

Climate Resilience ◽

Systemic Level ◽

Bond Property

This chapter looks at how more transparent disclosure of climate risks can make markets work for resilience. In a world in which climate risk is reflected in the prices of assets traded in the market, everyone will be pressured to manage the risk and protect the value of their holdings. This chapter looks at four markets where we might expect climate risk disclosure to cause prices to change most readily: equities (company stocks), debt (bonds issued by companies and governments), property (real estate), and insurance. It argues that disclosure and better risk information can propel climate resilience at a systemic level, but it can also prove highly disruptive. Fear of disruption and its consequences has led different groups to throw sand into the gears to delay a day of reckoning, but that day is coming. If communities are unprepared, investors, banks, and insurance companies could panic and pull back indiscriminately from parts of the stock, bond, property, and insurance markets. The insights learned from these markets can illustrate how each could drive resilience on a large scale.

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Comparing an insurer's perspective on building damages with modelled damages from pan-European winter windstorm event sets: a case study from Zurich, Switzerland

10.5194/nhess-2020-115 ◽

2020 ◽

Author(s):

Christoph Welker ◽

Thomas Röösli ◽

David N. Bresch

Keyword(s):

Open Source ◽

Return Period ◽

Model Building ◽

Information Service ◽

Source Model ◽

Insurance Companies ◽

Risk Models ◽

Risk Modelling ◽

Modelling Approach

Abstract. With access to claims, insurers have a long tradition of being knowledge leaders on damages caused by e.g. windstorms. However, new opportunities have arisen to better assess the risks of winter windstorms in Europe through the availability of historic footprints provided by the Windstorm Information Service (Copernicus WISC). In this study, we compare how modelling of building damages complements claims-based risk assessment. We describe and use two windstorm risk models: the insurer's proprietary model and the open source CLIMADA platform. Both use the historic WISC dataset and a purposefully-built, probabilistic hazard event set of winter windstorms across Europe to model building damages in the canton of Zurich, Switzerland. These approaches project a considerably lower estimate for the annual average damage (CHF 1.4 million), compared to claims (CHF 2.3 million), which originates mainly from a different assessment of the return period of the most damaging historic event Lothar/Martin. Additionally, the probabilistic modelling approach allows assessing rare events, such as a 250-year return period windstorm causing CHF 75 million damages. Our study emphasises the importance of complementing a claims-based perspective with a probabilistic risk modelling approach to better understand windstorm risks. The presented open source model provides a straightforward entry point for small insurance companies.

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A NEW MULTIVARIATE ZERO-INFLATED HURDLE MODEL WITH APPLICATIONS IN AUTOMOBILE INSURANCE

Astin Bulletin ◽

10.1017/asb.2021.39 ◽

2022 ◽

pp. 1-24

Author(s):

Pengcheng Zhang ◽

David Pitt ◽

Xueyuan Wu

Keyword(s):

Model Performance ◽

Insurance Companies ◽

Hurdle Model ◽

Automobile Insurance ◽

Insurance Company ◽

Computational Statistics ◽

Count Models ◽

Insurance Portfolio ◽

Insurance Business ◽

One Year

Abstract The fact that a large proportion of insurance policyholders make no claims during a one-year period highlights the importance of zero-inflated count models when analyzing the frequency of insurance claims. There is a vast literature focused on the univariate case of zero-inflated count models, while work in the area of multivariate models is considerably less advanced. Given that insurance companies write multiple lines of insurance business, where the claim counts on these lines of business are often correlated, there is a strong incentive to analyze multivariate claim count models. Motivated by the idea of Liu and Tian (Computational Statistics and Data Analysis, 83, 200–222; 2015), we develop a multivariate zero-inflated hurdle model to describe multivariate count data with extra zeros. This generalization offers more flexibility in modeling the behavior of individual claim counts while also incorporating a correlation structure between claim counts for different lines of insurance business. We develop an application of the expectation–maximization (EM) algorithm to enable the statistical inference necessary to estimate the parameters associated with our model. Our model is then applied to an automobile insurance portfolio from a major insurance company in Spain. We demonstrate that the model performance for the multivariate zero-inflated hurdle model is superior when compared to several alternatives.

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CLIMADA v1.4.1: Towards a globally consistent adaptation options appraisal tool

10.5194/gmd-2020-151 ◽

2020 ◽

Author(s):

David N. Bresch ◽

Gabriela Aznar-Siguan

Keyword(s):

Climate Adaptation ◽

Cost Effective ◽

Interdisciplinary Studies ◽

Decision Makers ◽

Economic Framework ◽

Impact Modelling ◽

The Cost ◽

Impact Events ◽

Adaptation Options

Abstract. Climate change is a fact and adaptation to a changing environment therefore a necessity. Adaptation is ultimately local, yet similar challenges pose themselves to decision-makers all across the globe and on all levels. The Economics of Climate Adaptation (ECA) methodology established an economic framework to fully integrate risk and reward perspectives of different 10 stakeholders, underpinned by the CLIMADA impact modelling platform. We present an extension of the latter to appraise adaption options in a consistent fashion in order to provide decision-makers from the local to the global level with the necessary facts to identify the most effective instruments to meet the adaptation challenge. We apply the open-source methodology and its Python implementation to a case study in the Caribbean, which allows to prioritize a small basked of adaptation options, namely green and grey infrastructure options as well as behavioural measures, and permits inter-island comparisons. In 15 Anguilla, for example, mangroves avert simulated damages more than 4 times the cost estimated for restoration, while enforcement of building codes shows to be effective in the Turks and Caicos islands. For all islands, cost-effective measures reduce the cost of risk transfer, which covers damage of high impact events that cannot be cost-effectively prevented by other measures. This extended version of the CLIMADA platform has been designed to enable risk assessment and options appraisal in a modular form and occasionally bespoke fashion yet with high reusability of common functionalities to foster usage of the 20 platform in interdisciplinary studies and international collaboration.

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A globally consistent probabilistic wildfire risk model to assess economic damages

10.5194/egusphere-egu21-2241 ◽

2021 ◽

Author(s):

Samuel Lüthi ◽

David Bresch

Keyword(s):

Risk Assessment ◽

Random Walk ◽

Climate Adaptation ◽

Risk Model ◽

Grid Point ◽

Probabilistic Risk Assessment ◽

Insurance Companies ◽

Wildfire Risk ◽

Economic Damages ◽

Probabilistic Risk

Wildfire risk around the world is rapidly increasing, leading to dramatic impacts on ecosystems and society. Economic damages of the past seasons threaten individual households, insurance companies, brokers and governmental authorities alike. Here, we present a probabilistic wildfire risk model to assess fire and economic risk. The model creates synthetic fire seasons through probabilistic ignition and dynamic random-walk spreading of fires.The risk of natural catastrophes is commonly modeled using the three components hazard, exposure and vulnerability. This approach is used in the well-established open-source platform CLIMADA (CLIMate ADAptation). Here we show its extension for a globally consistent wildfire risk model. The model allows for the evaluation of economic damages of past and current wildfire events as well as a probabilistic risk assessment for any exposure on a seasonal basis. It is built on open and global data to ensure consistent modelling, including in data-sparse regions.The hazard component uses Fire Information for Resource Management System (FIRMS) data acquired by the MODIS and VIIRS satellite missions and provided by Earthdata. We aggregate point information of fire activity using clustering algorithms over space and time to identify separate events while allowing for different resolutions (minimum of 375 m). For the exposure component, CLIMADA&#8217;s LitPop model is used, which geographically distributes assets using data on night-light intensity and population density. To assess the vulnerability, the model has been calibrated using reported damage data. Although uncertainties remain large, error scores after calibration resemble those of well-established hazards, such as tropical cyclones. To allow for probabilistic risk assessment, synthetic fire seasons are generated using a random-walk-type stochastic fire generator, which hinges on grid-point specific fire spread probabilities combined with an overall fire propagation probability. The framework further allows for a simple integration of additional data in order to reflect climate trends.

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