Displays

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? 

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.

How to cite: Dixon, R., Franklin, S., Shaffrey, L., and Clifford, D.: Climate change, historical data and catastrophe modelling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20647, https://doi.org/10.5194/egusphere-egu2020-20647, 2020.

Ten years ago, we studied 101 years (1910-2010) of wind observations at five stations spread throughout the Netherlands, and representative of a wider area in Europe containing regions of dense exposure. The raw wind speed data were homogenised using detailed station metadata to account for changes in observation practises, then processed to form a windstorm loss index timeseries. Our analysis found large changes in annual storm losses at multidecadal timescales, with two minima occurring in the 1960s and the 2000s. The more recent minimum was three to four times lower than the century-scale peak of indexed losses in the 1980s and early 1990s and primarily driven by the reduced rate of occurrence of damaging storms.

We recently extended the storm loss timeseries up to 2019 and results confirmed what most of us expected: the lull continues. A recent industry survey indicated the ongoing quiet period is the top science issue for European windstorms, presumably because its large amplitude dwarfs other uncertainties in storm loss climate. The burning question for re/insurance is: what to expect over the next few years? What roles will natural climate variability and anthropogenic forcings play in the medium-term future evolution of our storm climate? Researchers have begun to supply some answers, finding strong empirical links between Arctic sea-ice, the state of the North Atlantic Ocean, and European winter climate, backed up by process-based studies connecting these variables. We will review their findings in the context of storm loss variability, then identify questions which could be key to anticipating the storm activity over the next few years.

How to cite: Cusack, S. and Panosetti, D.: Europe Windstorm variations: past, present and future, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7462, https://doi.org/10.5194/egusphere-egu2020-7462, 2020.

Tropical cyclones (TCs), also referred to as hurricanes or typhoons, are amongst the deadliest and costliest natural disasters, affecting people, economies and the environment in coastal areas around the globe when they make landfall. In 2017, Hurricanes Harvey, Irma and Maria entered the top-5 costliest Atlantic hurricanes ever recorded, with combined losses estimated at $220 billion. Therefore, to minimize future loss of life and property and to aid risk mitigation efforts, it is crucial to perform accurate TC risk assessments in low-lying coastal regions. Calculating TC risk at a global scale, however, has proven to be difficult, given the limited temporal and spatial information on landfalling TCs around much of the global coastline.

In this research, we present a novel approach to calculate TC risk under present and future climate conditions on a global scale, using the newly developed Synthetic Tropical cyclOne geneRation Model (STORM). For this, we extract 38 years of historical data from the International Best-Track Archive for Climate Stewardship (IBTrACS). This dataset is used as input for the STORM algorithm to statistically extend this dataset from 38 years to 10,000 years of TC activity. Validation shows that the STORM dataset preserves the TC statistics as found on the original IBTrACS dataset. The STORM dataset is then used to calculate global-scale return periods of TC-induced wind speeds at 0.1°resolution. This return period dataset can then be used to assess the low probabilities of extreme events all around the globe. Moreover, we demonstrate the application of this dataset for TC risk modeling on small islands in e.g. the Caribbean or in the South Pacific Ocean.

How to cite: Bloemendaal, N., Haigh, I., de Moel, H., Muis, S., and Aerts, J.: Estimation of Global Synthetic Tropical Cyclone Hazard Probabilities using the STORM dataset, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2568, https://doi.org/10.5194/egusphere-egu2020-2568, 2020.

PRIMAVERA is a European Union Horizon2020 project about creating a new generation of advanced and well-evaluated high-resolution global climate models, for the benefit of governments, business and society in general. The project has been engaging with several sectors, including finance, transport, and energy, to understand the extent to which any improved process understanding arising from high-resolution global climate modelling can – in turn – help with using climate model output to address user needs.

In this talk we will outline our work for the finance and (re)insurance industries.  Following consultation with members of the industry, we are using PRIMAVERA climate models to generate a European windstorm event set for use in catastrophe modelling and risk analysis.  The event set is generated from five different climate models, each run at a selection of resolutions ranging from 18-140km, covering the period 1950-2050, giving approximately 1700 years of climate model data in total.  High-resolution climate models tend to have reduced biases in storm track position (which is too zonal in low-resolution climate models) and windstorm intensity.  We will compare the properties of the windstorm footprints and associated risk across the different models and resolutions, to assess whether the high-resolution models lead to improved estimation of European windstorm risk.  We will also compare windstorm risk in present and future climates, to see if a consistent picture emerges between models.  Finally we will address the question of whether the event sets from each PRIMAVERA model can be combined to form a multi-model event set ensemble covering thousands of years of windstorm data.

How to cite: Lockwood, J., Palin, E., Guentchev, G., and Roberts, M.: Using PRIMAVERA high-resolution global climate models for European windstorm risk assessment in present and future climates for the (re)insurance industry, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7676, https://doi.org/10.5194/egusphere-egu2020-7676, 2020.

Consistent information on fluvial flood risks in large river basins is typically sparse. This is especially true for the Danube River basin covering up to 14 countries and creating a patchwork of flood risk information across a populous and flood-prone region. As climatic changes have shown to increase flooding in the future, consistent basin-scale assessments prove vital to the insurance industry as well as municipal and infrastructural planning. The Future Danube Model (FDM) was designed to fill this gap complying to both insurance industry and climate science standards. That is, allowing for a reasonably detailed model scale (based on a 25m digital elevation model), stochastic sampling to create a large number of extreme events and flood event footprints (10k years), a thorough calibration and validation as well as the use of an ensemble of climate model output to drive the model under scenario conditions. The model is here used to assess the impact on critical infrastructure across the basin. Results indicate a marked increase in flood risk has already occurred when comparing the current climate period (2006-2035) to the reference period (1970-1999). Further increases are projected under a moderate and a business as usual scenario for the next climate period (2020-2049) and the end of the century (2070-2099). In large parts of the basin, the historical 100-year flood level, often used as a critical protection level for infrastructure, is projected to be equalled or exceeded every 50–10 years, while areas with a 100-year flood risk are projected to increase by 6-19%.

How to cite: Wortmann, M. and Schröter, K.: Flood recurrence under climate change: a probabilistic flood risk assessment of critical infrastructure in the Danube basin, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18439, https://doi.org/10.5194/egusphere-egu2020-18439, 2020.

Severe hydro-meteorological hazards have been increasing during recent decades and, as a consequence of global change, more frequent and intense events are expected in the future. Climate informed planning of adaptation actions needs both consistent and reliable information about future risks and associated uncertainties, and appropriate tools to support comprehensive risk assessment and management. 
The Future Danube Model (FDM) is a multi-hazard and risk model suite for the Danube region which provides climate information related to perils such as heavy precipitation, heatwaves, floods and droughts under recent and future climate conditions. FDM has a modular structure with exchangeable components for climate input, hydrology, inundation, risk, adaptation and visualisation. FDM is implemented within the open-source OASIS Loss Modelling Framework, which defines a standard for estimating ground-up loss and financial damage of disaster events or event scenarios. 
The OASIS lmf implementation of the FDM is showcased for the current and future fluvial flood risk assessment in the Danube catchment. We generate stochastic inundation event sets for current and future climate in the Danube region using the output of several EURO-CORDEX models as climate input. One event set represents 10,000 years of daily climate data for a given climate model, period and representative concentration pathway. With this input, we conduct long term continuous simulations of flood processes using a coupled semi-distributed hydrological and a 1.5D hydraulic model for fluvial floods. Flood losses to residential building are estimated using a probabilistic multi-variable vulnerability model. Effects of adaptation actions are exemplified by scenarios of private precaution. Changes in risk are illustrated with exceedance probability curves for different event sets representing current and future climate on different spatial aggregation levels which are of interest for adaptation planning.

How to cite: Schröter, K., Wortmann, M., Lüdtke, S., Hayes, B., Drews, M., and Kreibich, H.: Current and future flood risk assessment in the Danube region, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8410, https://doi.org/10.5194/egusphere-egu2020-8410, 2020.

Extreme precipitation events often lead to flash floods, in particular in urban environments dominated by impervious surfaces. Likewise, excessive rainfall over an extended period or heavy snowmelt may lead to extreme river floods, which historically have caused loss of many lives, extensive damages to human and natural systems, and displacement of millions of people. Risk assessments generally consider the potential hazards from pluvial and fluvial floods as separate events. This can lead to a significant underestimation of the risks. Thus, the physical processes (e.g. precipitation) that drive these extreme events may interact and/or exhibit a spatial or temporal dependency, which could lead to an intensification of the hazard or modify the associated vulnerability and/or exposure. This is, e.g., the case of Budapest, where the urban drainage system relies on gravity flows. At about 3 m above the normal water level, rain water is not able to drain into the river without pumping, changing the operational conditions of the drainage system and potentially increasing the risk of urban flooding if this is coincident with an extreme precipitation event.

Here, we analyse the coincidence of compound pluvial and fluvial flood events for both a current and future climate, including the potential physical links between extreme precipitations events, and larger scale rainfall in the Danube catchment. For this analysis, we use the Future Danube Model (FDM), representing a full catastrophe model compliant with the insurance industry standards. The model considers four members of the Euro-CORDEX regional climate model ensemble and their historical and future simulations- 30-year time slices (e.g. 2071-2100) are extracted from each simulation, which are first bias-corrected and then statistically inflated using the IMAGE weather generator to yield spatially distributed daily time series covering 10.000 model years with the same overall statistical properties as the underlying Euro-CORDEX model but with an enhanced representation of rare (precipitation) extremes across the entire catchment of the Danube. This time series feed into a detailed hydrological/ hydrodynamic model for the river catchment, based on a combination of the SWIM eco-hydrological model and a modified version of the CaMa-Flood hydrodynamic model, from where we estimate the discharge levels and fluvial flood risk at the location of the city of Budapest. For the pluvial flood modelling, we use a modified version of the approach described in Kaspersen et al. (2017), forced by the same four Euro-CORDEX models as used in the SWIM hydrological model, to infer recurrence periods and intensities for present and future heavy to extreme rainfall events.    

Considering the seasonality of the pluvial and fluvial flood risk, respectively, we find a significantly enhanced risk of compound events happening during the summer period, and that for most periods the compound risk is exacerbated by climate change. Given that the urban drainage system in Budapest already today is worn down and lacking the necessary capacity to deal with major flash floods, this suggests that new potentially nature-based approaches for dealing with storm water should be considered and that significant investments in updated urban drainage infrastructure is urgently needed.

How to cite: Drews, M., Schröter, K., Wortmann, M., and Larsen, M. A. D.: Compound risk of extreme pluvial and fluvial floods , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19300, https://doi.org/10.5194/egusphere-egu2020-19300, 2020.

Flood insurance coverage can enhance financial resilience of households to changing flood risk caused by climate change. However, due to increasing risk in many areas, premiums are likely to rise, which may cause insurance to become unaffordable for low-income households. This issue can become especially prominent in high-risk areas, when premiums are risk-reflective. Consequently, increasing premiums can reduce the demand for insurance coverage when this is optional, as individuals often underestimate the flood risk they face. After a flood, uninsured households then have to rely on private savings or ex-post government disaster relief. This situation is suboptimal as households may not save sufficiently to cover the damage, and government compensation can be uncertain. Using a modeling approach we simulate unaffordability and uptake of various forms of flood insurance systems in EU countries. To do this, we build upon and advance the “Dynamic Integrated Flood Insurance” (DIFI) model, which integrates flood risk simulations, with an insurance sector and a consumer behavior model. We compute the results using various climatic- and socio-economic scenarios in order to assess the impact of climate- and socio-economic change for flood insurance in the EU. Furthermore, we assess the impact of remote natural disasters on flood insurance premiums in EU countries, which occurs through the global reinsurance market. More specifically, after large natural disasters or compound events occurring outside the EU, which are likely to occur more often due to climate change, reinsurance premiums can temporarily rise as a result of a global “hard” capital market for reinsurers. The higher cost of capital for reinsurers is then transferred to households in the EU through higher flood insurance premiums. We find that rising average, and higher variance, of flood risk towards the end of the century can increase flood insurance premiums, and cause higher premium volatility resulting from global reinsurance market conditions. The rise in premiums increases unaffordability of insurance coverage and results in declining demand for flood insurance. A proposed policy improvement is to introduce a public reinsurance system for flood risk, as governments can often provide cheaper reinsurance coverage and are less subject to volatility on capital markets. Besides this, we recommend a limited degree of premium cross-subsidization to limit the growth of premiums in high-risk areas, and insurance purchase requirements to increase the level of financial protection against flooding.  

How to cite: Tesselaar, M., Botzen, W. J. W., and Aerts, J. C. J. H.: Impacts of Climate Change and Remote Natural Catastrophes on EU Flood Insurance Markets, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19644, https://doi.org/10.5194/egusphere-egu2020-19644, 2020.

Large scale climatic patterns and river network topology have an important impact on the space-time structure of floods. For example, in a recent study we showed that the effect of the North Atlantic Oscillation (NAO) is visible in the structure of economic losses at the European scale. The analysis revealed that in Northern Europe the majority of historic winter floods occurred during a positive NAO state, whereas the majority of summer floods occurred during a negative state. Through the application of a state-of-the-art flood catastrophe model, we also observed that there exists a statistically significant relationship between economic flood losses and the NAO. In this study we further advance the analysis by exploring the correlation structure of flood losses in Europe during different seasons and for different NAO states.  Flood loss correlation is measured in terms of “loss synchrony scale” (LSC), a metric formalized for this study following the definition of “flood synchrony scale” in Berghuijs et al. (2019). For an individual event and an individual CRESTA region, the LSC is defined as the maximum radius around the CRESTA, within which at least half of the other CRESTA regions experience a loss due to the same event. We analyse the LSC across Europe, as produced by the loss model, and check   for consistency with the data-based flood synchrony scale in Berghujs et al. (2019). We further explore how the LSC changes between different seasons, and between NAO states. This analysis can help improve financial preparedness to catastrophic floods as a better understanding of the correlation structure of the flood events allows for a better distribution of resources as well as a more efficient application of mitigation measures.

Berghuijs W R, Allen S T, Harrigan S and Kirchner J W 2019 Growing spatial scales of synchronous river flooding in Europe Geophys. Res. Lett. 46 1423–8

How to cite: Zanardo, S., Smith, R., Nicotina, L., Assteerawatt, A., and Hilberts, A.: Correlation structure of economic losses due to floods across Europe, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19222, https://doi.org/10.5194/egusphere-egu2020-19222, 2020.

To quantify risk from natural hazards and ensure a robust decision-making process in the insurance industry, uncertainties in the mathematical models that underpin decisions need to be efficiently and robustly captured. The complexity and sheer scale of the mathematical modelling often makes a comprehensive, transparent and easily communicable understanding of the uncertainties very difficult.  Models predicting flood hazard and risk have shown high levels of uncertainty in their predictions due to data limitations and model structural uncertainty. Moreover, uncertainties are estimated to increase with climate change, especially for higher warming levels.

Global Sensitivity Analysis (GSA) provides a structured approach to quantify and compare the relative importance of parameter, data and structural uncertainty. GSA has been implemented successfully in tools such as the Sensitivity Analysis For Everybody (SAFE) toolbox, which is currently used by more than 2000 researchers worldwide. However, tailored tools, workflows and case studies are needed to demonstrate GSA benefits to practitioners and accelerate its uptake by the insurance industry.

One such case study has been the collaboration between the University of Bristol and JBA Risk Management on JBA’s new Global Flood Model, whose technology and flexibility has allowed to test a catastrophe model in ways not possible in the past. JBA has gained great insight into the sensitivity of modelled losses to uncertainties in the model datasets and analysis options. This has helped to explore the key sensitivities of the results to the assumptions made, for example to visualise how the distribution of modelled losses varies by return period and explore which parameters have the biggest impact on loss for the part of the Exceedance-Probability curve of interest. This information is essential for insurance companies to form their view of risk and to empower model users to adequately communicate uncertainties to decision-makers.

How to cite: Noacco, V., Pianosi, F., Wagener, T., Styles, K., and Hutchings, S.: Improving the robustness of flood catastrophe models in insurance through academia-industry collaboration , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10037, https://doi.org/10.5194/egusphere-egu2020-10037, 2020.

The (re)insurance industry is alarmed that trends resulting from changing climate extremes may not be correctly reflected in its models, which are typically calibrated on past data. However, depending on the region and the peril, these trends vary in direction, magnitude and confidence level. A climate risk score framework has been developed that allows to identify regions or insurance portfolios which are particularly exposed to the consequences of climatic changes. In addition, the score also highlights a portfolio's contribution to climate change which eventually translates into a transitional risk – the risks emerging from the transition to a low-carbon economy.

The climate risk score is based on several sub-scores which reflect expected changes in mean and extreme precipitation and temperature as well as in mean sea level rise. It is computed using data output from several CMIP5 models – the models that lay the data foundation of the recent IPCC reports. In addition, the Swiss Re proprietary storm surge zones as well as its pluvial and fluvial flood zones are incorporated, allowing for a risk view in high-resolution (30 m). The contribution to climate change is displayed qualitatively, based on the occupancy of the individual sites of the portfolio.

Using this framework, the climate risk exposure of individual insurance portfolios can be assessed over time, across different RCP scenarios, or against an overall market portfolio. These insights can amongst others be used to steer a portfolio, or to judge past and expected changes in portfolio profitability and may thus also influence underwriting decisions. This may be particularly relevant for portfolios that are exposed to so-called secondary perils, i.e. high-frequency loss events of low-to-medium severity. Furthermore, regions can be identified, where uncertainties are particularly high and a more in-depth analysis of existing models might be required.

How to cite: Lüthi, S., Gloor, M., and Walz, M.: Climate risk score – a framework to quantify an insurance portfolio's exposure and contribution to climate change, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9877, https://doi.org/10.5194/egusphere-egu2020-9877, 2020.

In 2018 worldwide natural catastrophe losses were estimated at around USD $155 billion, resulting in the fourth-highest insurance payout on sigma records, and in 2020 JBA Risk Management (JBA) estimate 2 billion people will be at risk to inland flooding. By 2100, under a 1.5°C warming scenario, the cost of coastal flooding alone as a result of sea level rise could reach USD $10.2 trillion per year, assuming no further adaptation. It is therefore imperative to understand the impact climate change may have on global flood risk and insured losses in the future.

The re/insurance industry has an important role to play in providing financial resilience in a changing climate. Although integrating climate science into financial business remains in its infancy, modelling companies like JBA are increasingly developing new data and services to help assess the potential impact of climate change on insurance exposure.

We will discuss several approaches to incorporating climate change projections with flood risk data using examples from research collaborations and commercial projects. Our case studies will include: (1) building a national-scale climate change flood model through the application of projected changes in river flow, rainfall and sea level to the stochastic event set in the model, and (2) using Global Climate Model data to adjust hydrological inputs driving 2D hydraulic models to develop climate change flood hazard maps.

These tools provide outputs to meet different needs, and results may sometimes invoke further questions. For example: how can an extreme climate scenario produce lower flood risk than a conservative one? Why may adjacent postcodes' flood risk differ? We will explore the challenges associated with interpreting these results and the potential implications for the re/insurance industry.

How to cite: Jones, S., Raven, E., and Toothill, J.: Assessing the global risk of climate change to re/insurers using catastrophe models and hazard maps, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5323, https://doi.org/10.5194/egusphere-egu2020-5323, 2020.

The cost of future catastrophes will depend on changes to the hazard, exposure and vulnerability. Previous work has shown how climate change could affect the financial losses from damaged buildings by altering the frequency, severity and other characteristics of the hazard, but has not shown how socioeconomic trends could affect losses by altering the total number, spatial distribution and vulnerability of buildings.

We extend and apply urban scaling theory to model the spatiotemporal evolution of exposure using population projections that are consistent with Shared Socioeconomic Pathways (SSPs). The exposure sets are integrated with hazard catalogues that are consistent with Representative Concentration Pathways to give five views of UK windstorm risk for the year 2100.

SSPs describe five plausible futures where socioeconomic trends have made mitigation of, or adaptation to, climate change harder or easier. For example, one SSP describes a global panacea of co-operative, sustainable development while another describes a fragmented, under-developed world heavily-reliant on fossil fuels. AIR’s present-day exposure set, representative of all insurable properties in the UK, is perturbed by the SSPs to create an ensemble of plausible exposure sets for the year 2100. This ensemble is run through the AIR Extratropical Cyclone model for Europe with four stochastic event-based catalogues that represent the present hazard and three plausible future hazards posed by 1.5°C, 3°C and 4.5°C increases in global temperature.

Previous work found that global warming of 1.5°C to 4.5°C would increase the Average Annual Loss (AAL) from UK windstorms by 11% to 25%. We find that changes in exposure alone, dictated by the SSPs, lead to a wider range of changes in AAL. Urbanisation occurs under all SSPs resulting in exposure concentrating in cities and regional-level variation in AAL. Changes in AAL will further widen when integrated with the future hazard catalogues.

The results can help governments and public bodies to decide on a strategy for future urban and rural development, and how much to invest in protective measures against catastrophes. The framework can be extended to other perils in other countries adapting to climate change.

How to cite: Clarke, A., Koch, A., Robinson, E., Cipullo, M., Latchman, S., and Sousounis, P.: Integrating Climate and Socioeconomic Pathways to Calculate the Future Cost of Catastrophes, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3058, https://doi.org/10.5194/egusphere-egu2020-3058, 2020.

Climate risk, and related impacts, are determined by a variety of natural, climatological and socio-economic factors. In its fifth Assessment Report, the Intergovernmental Panel on Climate Change has adapted the concept and terminology in this respect. The challenge is: How can relevant influencing factors be identified and integrated? And, how can these factors be represented spatially and integratively in order to provide decision makers with a sound basis for adaptation measures? The central starting question is: Where do I do what (and when)? Within the Austrian ACRP project 'RESPECT', a novel climate change risk analysis for the natural hazard 'flooding' was developed. Special attention is paid to the modelling of socio-economic and physical vulnerability and its integration into a spatially explicit climate risk analysis. As a result, spatial and thematic hotspots of social and physical vulnerability and climate risk for Austria are identified, which serve as a basis for the identification of adaptation measures.

As a result, climate risk maps are available for Austria, which show risk and vulnerability hotspots as homogeneous spatial regions, independent from administrative boundaries and traditional raster-based approaches. These hotspots are quantitatively evaluated by an index value as a measure of climate risk. In addition to the purely quantitative evaluation, it is also possible to characterise and present the spatial units qualitatively, in terms of 'problem areas' and contributing factors. This is a significant development compared to 'traditional' spatial units (grid cell based; based on administrative units). Thus the question mentioned at the beginning can be answered - where are which intervention measures necessary. The results are available for socio-economic and physical climate risk, which are flanked by corresponding hazard and vulnerability maps. Results for the present and the future have been produced using proxy indicators from the high-resolution Austrian climate change scenario data (ÖKS15). This makes it possible to identify future hot spots under the assumption of different climate scenarios. The presentations presents the adapted risk concept and methodological approach, respectively, and reflects critically on the opportunities and challenges of climate risk analysis in Austria and in general for the planning of climate change adaptation measures.  

How to cite: Kienberger, S. and Leis, J.-L.: Hot spots - cold spots - what dots? A critical reflection on integrated climate risk assessments – example flood risk in Austria, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21646, https://doi.org/10.5194/egusphere-egu2020-21646, 2020.

In large parts of the world, an increasing number of damaging events caused by previously rare extreme weather phenomena is being observed. This poses a challenge to those responsible for civil protection of how to sustain current safety levels under accelerated climate change. The aim of this study is to contribute to meeting these challenges by providing methods to determine anticipatory strategies for decades of sustainable protection. 

This endeavor requires the identification of weather-related hazard processeson on the one, and the establishment of corresponding future hazard development corridors on the other hand. The former, so-called Climate Indices (CIs), are determined by blending damage events and spatiotemporal highly resolved meteorological data for three different regions in the Austrian Alpine region and six different process categories via multivariate statistical analyses.  The derivation of hazard development corridors describing future changes in risk landscapes requires ensembles of regional climate projections, in which the occurrence of corresponding CIs is detected.

Results are incorporated into the decision-making process and processed together with experts in civil protection. The determination of optimal, sustainable protection strategies is based on decision-theoretical techniques and the application of the expected utility theory (Bernoulli principle).

The feasibility of integrating hazard development corridors into decision-making processes, as well as the satisfactory implementation of established procedures, is demonstrated by the most comprehensive civil protection project in Austria to date. The results are consistent and show significant differences between near (2036-2065) and far future (2071-2100) time periods, as well as between the threat levels corresponding to the "climate-friendly" path of humanity and those associated with the "business as usual" scenario. The results are in line with the European Floods Directive by ranking linear measures behind resettlement and retention measures.

How to cite: Enigl, K., Matulla, C., Frank, F., Schlögl, M., Schmid, F., and Schnetzer, I.: A decision-theoretic approach to sustain public protection under climate change based on ensembles of future hazard developments, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12893, https://doi.org/10.5194/egusphere-egu2020-12893, 2020.

The Financial Stability Board (FSB) published “Recommendations of the Task Force on Climate-related Financial Disclosures (TCFD)” in 2017 to assist companies in assessing climate-related risks and opportunities and financial disclosure. However, the integration between climate scenarios and the corporate risk management system and the financial quantification of climate-related risks are still the challenges for corporate practice. To collect the climate scenarios mentioned in TCFD and integrate the relevant factors in corporate operations, the study will use the framework of TCFD: Governance, Strategy, Risk management, Metrics and Targets, introduce the first three steps of the Climate Change Adaptation (CCA Steps): "identifying problems and establishing objectives", "assessing and analyzing current risk", "assessing and analyzing future risk", and use climate risk template which use  Hazard, Exposure and Vulnerability as risk assessment factors to establish a framework for the evaluation and analysis of risk. After establishing a complete method for climate risk and opportunity assessment, in response to the "financial disclosure", the study will link to the financial statement items, referring to related concepts such as “Value at Risk” and “stranded assets”, to strengthen the integrity and transparency of corporate financial disclosure. At last, the study will select a specific climate physical risk in a industry for case study by the analysis of literature, international reports and historical events and introduce a climate risk assessment framework to verify the practicality of this framework. The study's results will be applied to the risk management of business operations. At the same time, the framework of climate risk can assist companies to put climate change factors into their decisions, maintaining the sustainable competitiveness in a low-carbon economy.

Key words: climate risk assessment, TCFD, enterprise risk management

How to cite: Liu, I. and Tung, C.: The application of climate adaptation algorithm to physical climate risk assessment and management of the TCFD, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4276, https://doi.org/10.5194/egusphere-egu2020-4276, 2020.

Taiwan belongs to the edges of sub-tropical and tropical climate zones, and has been indicated as a high risk edge area by international climate change researches. According to the Intergovernmental Panel on Climate Change (IPCC), Taiwan is threatened by global warming, changes of rainfall pattern, sea level rising and high frequency and influence of extreme weather, which will result in great impacts to agriculture industry and the future of food security. Unfortunately, along with the rapid economic development and urbanization in Taiwan since the 1960’s, agricultural land use has become less competitive to industrial, commercial, and residential types of land uses under land use competition. Therefore, to effectively enhance the resilience and conserve the agricultural lands which under the threats of climate change and the competitions of other types of land use, Taiwan’s Spatial Planning Act (promulgated on 2016/1/6) enlists Agricultural Development Zones, one of four major functional zones in National Spatial Plan, into demarcated functional zone and applying land use control. The zoning plan is expected to be completed by every city and county before the year of 2022, and one of the major issues is to consider the land use function changes of different locations. By comparing the 2007 and 2016 land utilization maps investigated by National Land Surveying and Mapping Center (Taiwan), this study is able to identify the 10-year changes of agricultural lands of northern Yilan county. To further investigate the spatial distribution of agricultural land changes, spatial analysis techniques such as multi-distance spatial cluster analysis (Ripley’s K Function) and point pattern analysis (Kernel density) are employed to analyze the spatial clustering of changes. The spatial analysis results overlays with climate change related and hazard risk maps, such as flooding, landslide, soil liquefaction, to support the decision making of future agricultural land planning and agriculture development zoning plan.

Keywords: agricultural land, land use changes, climate change, spatial analysis

How to cite: Lin, W.-Y. and Hung, C.-T.: Analyzing the Shifts of Land Use for Agricultural Land Planning under Climate Change– A Case Study of Northern Yilan County, Taiwan, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2255, https://doi.org/10.5194/egusphere-egu2020-2255, 2020.

Climate risk analysis and assessment studies are typically conducted relying on historical data. These data, however, represent just one single realization of the past, which could have unfolded differently. As an example, Hurricane Irma might had struck South Florida at Category 4 and, had it done so, damages could have been as high as 150 billion, about three times higher than damage estimated from the actual event. To explore the impacts of these potentially catastrophic near-misses, downward counter-factual risk analysis (Woo, Maynard and Seria, 2017) complements standard risk analysis by exploring alternative, plausible realization of past climatic events. As downward counter-factual risk analysis frames risk in an event-oriented manner, corresponding more closely to how people perceive risk, it is expected to increase climate risk awareness among people and policy makers (Shepherd et al., 2018).

We present a counter-factual risk analysis study of climate risk from tropical cyclones on the Caribbean islands. The analysis is conducted using the natcat impact model CLIMADA (Aznar-Siguan and Bresch, 2019). Impact is estimated based on forecasts of past tropical cyclones tracks from the THORPEX Interactive Grand Global Ensemble (TIGGE) dataset, as they all represent plausible alternative realizations of past tropical cyclones. The goal is to study whether, and to what extent, the estimated impacts from forecasts provide new insights than those provided by historical records in terms of e.g. cumulated annual damages, maximum annual damages and, in so doing, perform a worst-case analysis study to support climate risk management planning.

Aznar-Siguan, G. and Bresch, D. N.: CLIMADA v1: a global weather and climate risk assessment platform, Geosci. Model Dev., 12, 3085-3097, doi.org/10.5194/gmd-12-3085-2019, 2019.

Woo, G., Maynard, T., and Seria, J. Reimagining history. Counterfactual risk analysis. Retrieved from: https://www.lloyds.com/~/media/files/news-and-insight/risk-insight/2017/reimagining-history.pdf, 2017.

Shepherd, T.G., Boyd, E., Calel, R.A. et al.: Storylines: an alternative approach to representing uncertainty in physical aspects of climate change. Climatic Change 151, 555–571, doi.org/10.1007/s10584-018-2317-9 , 2018.

How to cite: Ciullo, A., Romppainen-Martius, O., Strobl, E., and Bresch, D.: A downward counter-factual climate risk analysis of the impact of tropical cyclones in the Caribbean islands, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9157, https://doi.org/10.5194/egusphere-egu2020-9157, 2020.

Floodings are amongst the most devastating damage-processes worldwide. Along with the increase in climate change induced extreme events, research devoted to the identification of so-called Climate Indices (CIs) describing weather phenomena triggering hazard-occurrences gains rising emphasis. CIs have a wide potential for further investigation in both research and application as e.g. in public protection and the transport and logistic industry. The appearance of specific CIs in regional climate models (i.e., ‘hazard development corridors’) can serve as an input in decision-theoretic concepts aiming to sustain current safety levels in climate change induced altering risk landscapes (Matulla et al, submitted). Enigl et al, 2019 first objectively derived hazard-triggering precipitation totals for six process-categories and three climatologically as well as geomorphologically distinct regions in the Austrian part of the European Alps.  This study aims at investigating a slightly different methodological approach for the objective determination of Climate Indices in the catchment area of the River Danube in Austria depending on catchment areas.

How to cite: Madjdi, N., Enigl, K., and Matulla, C.: Objective deviation of Climate Indices for the assessment of altered risk-landscapes driven by accelerated climate change, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17552, https://doi.org/10.5194/egusphere-egu2020-17552, 2020.

Climate change leads to rising temperatures and therefore stimulates the water cycle. As a consequence, extreme events in rainfall and associated flooding are projected to increase in frequency and severity in many regions of the world. Especially in developing countries with high population growth and often unregulated settlement, flood risk may increase due to both increased flood hazard and enhanced exposure. One such example is the megacity of Lagos, Nigeria, belonging to the largest cities in Africa. Floods within the city are recurrent and caused by storm surges from the Atlantic, heavy precipitation, and river floods. Flood risk is an issue and even expected to increase due to enhanced extreme precipitation, sea level rise, enhances storm surges, as well as illegal settlement, poor management, insufficient or blocking of drainage channels, missing early warning systems, and insufficient data.

The aim of this study is to deliver a first quantification of flood hazard for the city of Lagos based on hydrodynamic simulation with the model TELEMAC-2D. A focus is put on the use of freely available data sources and the design of reproducible workflows in order to enable local decision-makers to individually apply and refine the established workflows. The biggest challenge is the generation of the model mesh as the basis for subsequent hydrodynamic modelling due to limited data availability and the size of the model domain (about 1000 km²).

How to cite: Pilz, T.: Quantification of flood hazard for the megacity of Lagos, Nigeria, by hydrodynamic simulation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16561, https://doi.org/10.5194/egusphere-egu2020-16561, 2020.

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.

How to cite: Farnham, M., Camacho-Suarez, V., Milne, A., Hillier, J., Yu, D., Slater, L., Whyte, L., and Baruch, A.: Correlating surface water flood damages in three Indonesia cities, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9253, https://doi.org/10.5194/egusphere-egu2020-9253, 2020.

The scientific community agrees that climate change is generating a series of direct/indirect impacts on the environment and on humans that cannot be underestimated anymore. Consequently, it becomes urgent and necessary to know how this phenomenon affects ecosystems, productive activities and human well-being in order to plan measures for mitigation and adaptation. One sector whose performance is closely related to climate trends is tourism. The influence that climate change can have on tourism determines the need for adopting appropriate strategies to guarantee the sustainability of tourist destinations.

In order to develop models and tools for the near-real-time acquisition of climate data and for spatial interpolation, visualization and communication of climate monitoring to territorial stakeholders, the INDECIS project has involved a partnership of experts in the climate sector, from 12 European countries. The INDECIS Project intends to develop an integrated approach to produce a series of climate indicators aimed at the high priority sectors of the Global Framework for Climate Services of the World Meteorological Organization (agriculture, risk reduction, energy, health, water), with the addition of tourism.

With regards to the tourism sector, the territory of the Sila National Park (Calabria, southern Italy) has been selected as a study area for the acquisition of sectorial data on tourism (in particular, attendance data and tourist arrivals) and for the realization of a Workshop useful for the identification and enhancement of climate services that should be provided to stakeholders of the tourist destination, based on their needs. The Workshop was organized with three focus groups related to the following tourism activities: snow tourism, water and lake tourism, and earth tourism. Within the focus groups, the identified stakeholders - hotel groups, local associations, tourist agencies, parks, etc. - were able to highlight their needs in relation to the climate services that the INDECIS Project intends to offer.

From the results it emerges how the stakeholders consider essential, in the case of long-term forecasts regarding both positive and negative weather conditions for their activities, to start a synergy between the institutional, the economic and social networks to undertake a joint action. In the case of a positive forecast, these actions could consist in increasing the tourist offer, building new infrastructures, planning new investments, and in the realization of promotional actions to attract further customers. In the case of negative forecasts, the stakeholders proposed the development of a new tourist destination model, as an alternative to the existing one, with new activities that could adapt to the new climatic conditions.

In this context, the local community is the key component of the destination and the main stakeholder in tourism planning. Therefore, it is essential to pay attention to communities and work in the context of tourist destinations on a local scale to encourage mitigation and adaptation to climate change.

Acknowledgments:

The Project INDECIS is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462).

How to cite: Coscarelli, R., Antronico, L., Boqué Ciurana, A., De Pascale, F., Font Barnet, A., Russo, A. P., and Saladié Borraz, Ò.: Climate trends and tourist flows: first results of the case study in the Sila National Park (southern Italy) within the INDECIS Project., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2978, https://doi.org/10.5194/egusphere-egu2020-2978, 2020.

Extreme weather events are increasingly evident and widespread around the world due to climate change. These events are driven by rising temperatures and changes in precipitation patterns, which lead to changes in flood frequency, drought and water availability. To reduce the future impacts of natural disasters, it is crucial to understand the spatiotemporal variability of social, economic and environmental vulnerabilities related to natural disasters. Particularly, developing countries are more vulnerable to climate risks due to their greater economic dependence on climate-sensitive primary activities, infrastructure, finance and other factors that undermine successful adaptation. In this concept, adaptation plays the role of anticipating the adverse effects of climate change and taking appropriate measures to prevent or minimize the damage they may cause. Thus, the insurance fund is a valuable adaptation tool for unexpected losses reimbursement, long-term impacts prevention and encouraging risk mitigation. Although this approach is successful throughout the world and major organizations support insurance as an adaptation measure, the Brazilian insurance fund only provides support for rural landowners. Thus, we will evaluate the implementation of an indexed multi-risk insurance fund integrated with water security parameters, as an instrument for adaptation to climate change. We will use the SWAT+, a hydrosedimentological model, to assess the current conditions and future scenarios (up to 2100) of water security indices considering two International Panel on Climate Change (IPCC) Representative Concentration Pathways (RCP 4.5 and RCP 8.5). Then, we will incorporate those parameters to the Hydrological Risk Transfer Model (MTRH). Our results will provide optimized premium in current and future scenarios for supporting adaptation plans to climate change. Furthermore, to contribute to technical-scientific information addressing possible effects of climate change on the hydrometeorological variables and their spatiotemporal variability.

How to cite: Gesualdo, G., Souza, F., and Mendiondo, E.: Insurance Fund as an Adaptation Measure for Increasing Water Security in Basins Under Change, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8816, https://doi.org/10.5194/egusphere-egu2020-8816, 2020.

In 2019, the Bank of England, through the Prudential Regulation Authority (PRA), became the first regulator to ask insurers how financial losses could change under prescribed climate scenarios. Insurers readily use catastrophe models to quantify the likelihood and severity of financial losses based on at least 40 years of past climate data. However, they cannot readily use these models to answer the climate scenarios posed by the PRA.

We present four novel methods for how to use existing catastrophe models to answer what-if climate scenario questions. The methods make use of sampling algorithms, quantile mapping, and adjustments to model parameters, to represent different climate scenarios.

Using AIR’s Hurricane model for the United States (US), Inland Flood model for Great Britain, and Coastal Flood model for Great Britain, we quantify the sensitivity of the Average Annual Loss (AAL) and the 100-year exceedance probability aggregate loss (100-year loss) to four environmental variables under three climate scenarios. The environmental variables include the (i) frequency and (ii) severity of major US landfalling hurricanes; (iii) the mean sea level along the coast of the US and Great Britain; and (iv) the surface run-off from extreme precipitation events in Great Britain. Each of these variables are increased in turn by low, medium and high amounts as prescribed by the PRA.

We compare each variable and rank their influence on loss. We find that the AAL and 100-year loss are more sensitive to changes in the severity of major US hurricanes than changes in the frequency. We will show whether sea level rise has a greater influence on coastal flooding losses in the US or in Great Britain, and we show how sensitive inland flooding losses are to surface run-off.     

The methods yield approximate results but are quicker and easier to implement than running Global Circulation Models. The methods and results will interest those in insurance, the public sector, and academia, who are working to understand how society best adapts to climate change.

How to cite: Latchman, S., Clarke, A., Zapatka, B., Sousounis, P., and Stransky, S.: Augmenting Catastrophe Models to Quantify Financial Losses Under Prescribed Climate Scenarios, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7850, https://doi.org/10.5194/egusphere-egu2020-7850, 2020.

The results presented in this contribution demonstrate the value of climate services for the planned construction of the new Wastewater Treatment Plant (WWTP) in Novi Sad, Serbia. In this case, climate services provided added value for the decision-making processes, in terms of enhanced effectiveness, optimized technological opportunities and minimized risks and by serving as the means of involving and better-informing end-users and stakeholders. The specific goal of the research was to improve climate change resilience of the WWTP and to facilitate better overall hygienic conditions in Novi Sad and to safeguard the potable water resources and the quality of the environment in the areas located downstream and under the influence of the Danube River.

In order to achieve it, preliminary activities were oriented on analyzing the current climate and hydrological conditions, engaging the relevant data providers, stakeholders and policy makers and evaluating what relevant local data would be useful for the study. The data collected was applied in the testing and for improving the Future Danube Multi-hazard, Multi-risk Model (FDM), a catastrophe model implemented in the OASIS Loss Modeling Framework (Oasis-LMF). The FDM is implemented for the entire Danube Basin. High-resolution components for pluvial flood risks were further implemented to the city of Novi Sad, Serbia, after successful testing in the Budapest region. Observations and model results were used in a climate change impact assessment with the purpose of identifying adaptation options, appraisal of adaptation options and integration of an adaptation action plan into the Feasibility Study of the WWTP construction. The results of the pluvial flood model for Novi Sad clearly suggested that it is important to consider pluvial flood risks and that protective measures have to be considered as part of the WWTP construction, both under current and future climate conditions. Moreover, novel estimates of drainage water intensities during heavy rains would advise the design of the simultaneously planned pumping station on the banks of the Danube. Combined, this clearly demonstrates the added value of the climate services and risk information delivered by the FDM also beyond the insurance sector, as well as its potential to support adaptation decision making with respect to infrastructural investments in Novi Sad.

How to cite: Radonić, J., Turk Sekulić, M., Ćulibrk, D., Drews, M., Dømgaard, M. L., and Wortmann, M.: Climate services for large scale investments in infrastructure and climate resilience in the Danube Basin, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20103, https://doi.org/10.5194/egusphere-egu2020-20103, 2020.

The aim of this contribution is to present the design as well as findings of a survey targeted assessing the needs of stakeholders in the transportation domain with respect to climate change driven damages. This ‘User needs survey’ is one of the major objectives of multifarious collaborations investigating anticipatory asset protection strategies under accelerated climate change. The viability of these efforts is guaranteed by pairing up the scientific community (CIT, University of Vienna, BOKU, TU Vienna) with notable stakeholders (F&L, WMO, BMNT).

The ‘User needs’ survey, was carried out in cooperation between the Climate Impact Team (CIT) the European Transport, Freight and Logistics Leaders Forum (F&L) and the World Meteorological Organization (WMO). The aim of the survey is to identify services that stakeholders in the realm of transportation themselves consider significant and beneficial. 

Therefore, findings should be of vital importance for -- (i) setting up meaningful climate services; (ii) selecting sustainable protection measures strengthening transportation system resilience in the face of future climate change; (iii) compiling the chapter on 'Land Transport' in WMO’s new Service Delivery Guide -- as they ensure the expediency of the services described.

Presented results encompass: (i) an assessment of extreme events in terms of their damaging impacts on transport, freight and logistics by stakeholders; (ii) an assessment of the vulnerability of assets in transport, freight and logistics by stakeholders; (iii) an illustration of the extent of impacts climate-change (through shifts in extremes and associated threats) has had on transport, freight and logistics over the past decades; (iv) the stakeholders' expectations regarding future developments towards advancing climate-change and (v) an evaluation of time horizons (short, medium and long term) at which stakeholders need services. 

A summary completes this contribution.

How to cite: Matulla, C., Enigl, K., Macnab, A., Evans, P., Roser, G., Muchemi, S., Fleming, G., Dabberdt, W., Grimes, S., and Leviäkangas, P.: 'Transport and Transport-Infrastructure' - key findings of a "User Needs" survey, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19161, https://doi.org/10.5194/egusphere-egu2020-19161, 2020.

The weather and its climatic evolution play the main role in generating hazard profiles of forest fires. The increased in magnitude and damage of last forest fire seasons has caused a larger concern of the insurance sector for this peril. Due to the lack of knowledge of this risk, there is a widespread low level of insurance coverage of forest fire risk. A first step forward is clearly needed to (1) propose simplified approaches showing how the risk links with its main weather drivers, and (2) re-incentivize the use of insurance by forest managers.

To answer this objective, ARIA Technologies and its partners have developed a geospatial web-based decision tool to support both forest owners and forest insurance actors in managing the vulnerability of their asset/portfolios to fire risk. RiskFP includes:

• A “realistic disaster scenarios generator module” that allows the generation of hundreds of scenarios of extreme wildfires to complete information from historical fires databases. This information can be used in damage and loss modelling to improve the estimation of the probable maximum loss (PML). In addition, the risk FP “impact module” provides to the users information on the different potential impact like the amount of biomass burnt or the economic losses.
• A precise mapping of the local forest fire risk through the graphical representation of an index including five risk levels (from low to extreme) that provides an overview of the most critical locations regarding the potential behavior of the fire in case of an hypothetical ignition.
• A forecasting/projection module to inform the users on the frequency of the severe-extreme days in the mid- and long-term horizons. It can be used by the forestry sector to better anticipate and prepare the next fire season and as a planning tool for long-term operation/investment.

At the heart of the platform lies the concept of critical landscape weather patterns (CLP), an empirical fire weather index that identifies severe-extreme weather days derived from hourly records of a representative weather station (Gellie, 2019). It could be computed from past records, seasonal forecast or climate projection allowing to provide fire risk assessment for these different time scales. The CLP module is coupled with a propagation model, the Wildfire Analyst® forest fire simulator at the resolution of about 40m, that is used to estimate the progression and behavior of the fire in space and time. It is based on the standardized and validated semi-empirical Rothermel propagation model (1972).

Acknowledgements:

We acknowledge the European Commission for sponsoring this work in the framework of the H2020-insurance project (Grant Agreement number 730381).

How to cite: Deandreis, C., Lacressonière, G., Chiapero, M., Mendes, M., Diaz Fidalgo, H., Rageade, M., Menz, C., Cottle, P., and Gellie, N.: Climate services for forest fire risk management, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11639, https://doi.org/10.5194/egusphere-egu2020-11639, 2020.

Introduction - Climate change impact reduction can be achieved by exposure reduction and improved health care management. Adaptation strategies can be designed based on sustainable urban-infrastructure planning and warning systems (Banwell, 2018). The H2020 Insurance Project aimed to help the health insurance sector to understand the relation between the air quality, climate extremes and health conditions in a given population, quantifying potential losses associated with the current and future climate risk. Potential climate services were identified  Considering the rising demand for adaptation solutions in a climate change context, we present two test cases, applied for EU Projects (H2020 Insurance - Germany and CAMS/AIRE SALUD – Chile), to illustrate the potential of air quality and meteorological modeling for climate change adaptation.

Methods and Results - 

H2020 Insurance – Health DEMO (https://h2020insurance.oasishub.co/): Most of the sector has no detailed information regarding the baseline impact of air pollution or weather extreme events (i.e. heatwaves), neither the projection losses in the future climate. H2020-Insurance Health Work Package showcased a Risk/Impact assessment based on high-resolution air quality and meteorological databases integrated with morbidity/mortality data and provided present/future climate impact on health.

District-specific climate relative risk for COPD hospital admissions in Berlin and Potsdam, considering the period between 2012-2016. The attributable morbidity and the associated cost were calculated for the present condition. Climate change projections on air quality and heat exposure were computed and the potential future losses estimated. In parallel, a clinical trial demonstrated how specific counteracting measures (establish ideal room temperatures, telemedicine to monitor the domestic environment, etc.) can help to reduce the hospital stay and shorten recovery time.

CAMS Project – AIRE SALUD (www.airesalud.cl): For contexts, whereas risk awareness is built and strong, forecast systems are key resources to alert the population and give recommendations to reduce exposure. The AIRE SALUD system is based on a geospatial analysis of medical consultations in public emergencies recorded between 2011 and 2018 by the Department of Health Statistics and Information (DEIS) of the Ministry of Health of Chile. This integrates demographic data, socioeconomic vulnerability factors, participatory web data flows, and atmospheric variables, and allowed the development of geostatistical/machine learning algorithms to predict the increase in respiratory infections in the Santiago Metropolitan Region with a week of anticipation, with a confidence level of over 87%. 

Conclusion - The applications described present potential as a decision-making tool for adaptation plans in urban areas, improving population resilience and/or giving support on healthcare infrastructure planning strategy.

How to cite: Pinheiro, S., Deandreis, C., Lacressonniere, G., Zanutto, L., Witt, C., Hoffmann, C., Hoffmann, P., Hattermann, F., Hauf, Y., Drews, M., Dømgaard, M., Kaspersen, P. S., and Hervé, R.: Climate services to reduce human health impact associated with environmental risk factors exposure., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5605, https://doi.org/10.5194/egusphere-egu2020-5605, 2020.

We study the impact of climate change on European flood economic losses under 1.5°C global warming scenario. Climate scenarios were generated with the Community Atmospheric Model (CAM) version 5 under the protocols of the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) experiment. Present climate scenario corresponding to the years 2006-2015 includes observed forcing conditions for sea surface temperatures (SSTs) and sea-ice cover. The future 1.5°C scenario was constructed following SST warming according to the response to the RCP2.6 in CMIP5 model simulations. Each scenario comprises five 10-year long simulations that differ in the initial weather state. For each scenario we generated a 1000 years long stochastic set of precipitation based on the main modes of variability of gridded precipitation data through Principal Component Analysis applied to the monthly precipitation fields of the combined 50 simulated years. The other variables were obtained through an analogue month approach. Stochastic monthly fields were subsequently disaggregated in space and time to 3-hourly, 6 km resolution grids, and these were finally fed to a well-calibrated flood-loss model. The flood-loss model comprises a rainfall-runoff component, a flood routing scheme, an inundation component and a financial module that integrates flood hazard, buildings vulnerability, and economic exposure at location level. Prior to model evaluation, the stochastic meteorological forcing was bias-corrected with the stochastic set (based on observations) employed in the construction and calibration of the flood-loss model. The method for bias-correction preserves the ratio of quantiles of the future scenario to the present and preserves the correlation structure of the forcing variables. Average annual loss for Europe with the current-climate scenario generated by CAM is within 10-15% of the current industry estimate (based on observations), which suggests the applicability of the proposed approach. For the future scenario the model suggests a significant increase in loss (> 4 times) with respect to the present, which is in line with other studies for similar future global warming pathways.

How to cite: Sassi, M., Scudeler, C., Nicotina, L., Assteerawatt, A., and Hilberts, A.: Economic loss due to flooding in Europe at 1.5°C global warming, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9373, https://doi.org/10.5194/egusphere-egu2020-9373, 2020.

New catastrophe and disaster risk data, tools and services can often include complex science and algorithms that offer profoundly important information on understanding risk or can inform climate adaption. However, if few people know about or understand how and in what context to use these tools, they remain on the databases of academic institutions and in scientific journals across the world. How many tools that could transform the world’s understanding of risk and ways to adapt to that risk already exist or are currently under development? The answer is likely to be in the hundreds. But, how many of those tools have ever been used beyond one or two scientific case studies? The answer is likely to be, in most cases, very few.

Academic institutions often administer barriers on access to their data and tools through institutional data management and by specifically implementing non-commercial use licensing in the dissemination of tools once scientific studies are completed. In addition, very commonly, insufficient thought is put to the exploitation strategies of these tools. The gaps in understanding and trust between academia and the needs of business sometimes feel insurmountable on both sides. Is ‘custom’ defying reason in the face of the climate change crisis and the need for rapid systems transformation globally?

The Oasis family, offers new approaches around transparency, collaboration, dissemination and exploitation and the encouragement of intereoperability by providing platforms that allow for comparative approaches to scientific data and tools.

Firstly, "OASIS LMF is an open source platform for developing, deploying and executing catastrophe models to enable the “plug and play” of hazard and vulnerability modules (along with exposure and insurance policy terms) by way of a set of data standards that describe a model. It has been built in collaboration with the insurance industry (https://oasislmf.org/)." Oasis Palmtree offers support to enable access to this system.

Secondly, Oasis Hub, has designed science innovation approaches to bringing tools and data to wider, diverse audiences in collaboration with scientific institutions. We discuss "OASIS Hub, as a global window and conduit to free and commercial environmental, catastrophe and risk data, tools and services (https://oasishub.co/) as an example of a new innovation approach.

How to cite: Irvine, T.: 'Islands of excellence’ in catastrophe & disaster risk data, tools and services in the face of the climate change crisis – how can innovation systems advance stakeholder understanding and use of catastrophe and disaster sciences?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18571, https://doi.org/10.5194/egusphere-egu2020-18571, 2020.