How Climate Risk Models incorporate
Flood Data to put a price
on future Climate Risk

Dr Karl Mallon, Director of Climate Risk Pty Ltd, explains how climate risk models incorporate flood data like JBA’s to put a price on future climate risk.

Whilst there is growing interest in the impacts of climate change on the built environment, climate models only tell us about the possible future climate and weather. With the recent news that ratings company Moody’s will incorporate climate risk in its valuations, it’s only a matter of time before the financial impact of climate risk becomes a critical part of property investment, ownership and management.

What we need to know is the specific impact of the local hazards that cause damage and disruption to individual assets. Working over the past 7 years with engineers, scientists, financial analysts and hazard modellers like JBA, Climate Risk has developed computational engines that can turn climate impact projections into real and costed risks.

The Climate Risk Engines are used commercially by governments and industry to better understand potential risk and to allow for planning and budgeting for likely impacts. They combine engineering data, weather data, climate modelling and actuarial risk calculations to compute the future costs of damage, risks of failure and expected devaluation. The focus on physical vulnerability is a key differentiator in the field of climate change risk assessment.

Partnering with JBA

Climate Risk has partnered with JBA in order to use our weather and climate data analytics to forward project the risk of river and surface flooding across six continents.

A key result that has been attracting the attention of banks and governments is the projected cost of insurance and the number of properties that may become uninsurable. This is something that obviously worries governments -- the insurer of last resort.

Our sister company XDI: The Cross Dependency Initiative utilises the Climate Risk Engines in proprietary tools to quantify climate impacts for its work with clients in infrastructure, finance and government. Our latest innovation is EasyXDI.com, a low cost (free to academics and not-for-profits), web-based engine that allows anyone to calculate their current and future risk for 100 different asset types across seven hazards, for any place on earth.

Asset level calculations for detailed insights

The Climate Risk Engines use asset level calculations of risk rather than generalised damage curves. This means we can be very sensitive to actual designs and materials and have a more precise picture of vulnerability. Property owners need agency ; if analysis identifies they are in a future flood zone, owners need to know what can be done to mitigate that risk. Understanding which parts of a building will be damaged and why building materials are vulnerable is a vital step towards a solution.

Figure 1: Analysis on 3 adaptation pathways shows raising electrical components has maximum impact on risk cost over time. Adaptation actions are tested against base unadapted (no action) pathway to provide comparison of effectiveness vs cost.

The base-line probability that a hazard will exceed the specification of an element is computed based on the statistical distribution of associated extreme weather parameters combined with local hazard exposure mapping. Hazards covered include fluvial flooding, daily maximum temperatures, wind gusts, forest fires, subsidence and coastal inundation. These can get down to a spatial resolution of 10m x 10m.

Figure 2: XDI Platform aggregates risk by area on physical assets. Users can drill down for further information on which assets and which hazards present the greatest risk.

Forward looking estimates for risk use General Circulation Models (GCM) and Regional Climate Model (RCM) downscaling prepared by international modelling teams, though these operate at broad-scale atmospheric resolutions down to about 10km x 10km. These are used to compute the change in the probability of the event thresholds for each element and or design parameter. In the analysis pictured above the IPCC (Intergovernmental Panel on Climate Change ) Representative Concentration Pathway (RCP) 8.5 has been used to reflect current Business-As-Usual and the closest path to current emission trajectories, but different scenarios can be selected by the user.

Results of analysis are provided as projections of mean Annual Average Loss (AAL) for each property each year. These results are then aggregated to various levels including council, state or national level as required for mapping or distribution.

To test drive XDI analytics go to EasyXDI.comFor more information and further XDI capabilities: http://xdi.systems.

JBA’s global river and surface water flood data are used in the XDI Platform. For more information on JBA’s global flood data or how we work with collaborators in the fields of flood and climate risk, get in touch.

Dr Karl Mallon is Co-Director of XDI and Director of Science and Systems at Climate Risk Pty Ltd, which developed the Climate Risk Engines that underpin XDI’s cross dependent analysis. He has worked in climate change mitigation, policy and technical analysis since 1991 in Europe and Australia, and specialised in climate adaptation since 2005. Karl’s work has focused on developing systems to understand the complex play of climate change variables that impact an organisation’s physical assets, human resources and management processes.

With a strong focus on quantitative analysis, Karl and his technical team have developed processes that capture this complex challenge. A major focus has been the integration of hazard, engineering and financial data into high-speed cloud computing software to provide decision support systems for governments, corporations and financial institutions. Karl is a founding board member of The Climate Bonds Initiative (London) and author of ‘Renewable Energy Policy and Politics: A Handbook for Decision Making’ published by Earthscan (London).

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