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Looking at the countless examples of flooding events during 2022, including in the United States, Australia, South Africa, and Pakistan, they all highlight the devastating effects that flooding poses to the affected communities.
Examining the floods in Pakistan from June through to October 2022, some 33 million citizens were impacted, over 1,700 people were killed, and over 2.2 million properties were destroyed or damaged.
A true global peril, floods can occur at any time, anywhere there is precipitation, and events can be frequent and certainly do not respect geographic borders or limit their effects to those located close to water bodies such as rivers.
The impact of climate change on flood events is complex – for instance, warmer temperatures mean that the atmosphere retains more moisture resulting in extreme precipitation events. At the same time, drier soils, shorter duration rainfall, and less snowmelt could minimize flood impacts.
Assessing flood risk is challenging and becoming more urgent than ever for groups such as asset owners and managers, and real estate investors. Flood risk requires the use of highly granular data to help understand peril risk at an individual asset level.
Integration of RMS Global Flood Maps
Moody’s RMS is the world’s leading provider of natural catastrophe models and has over 30 years of experience providing granular, asset-specific data to help inform the assessment and quantification of flood risk.
The Moody’s RMS Climate on Demand version 2 application, planned for release in January 2023, will now incorporate RMS Global Flood Maps (GFM) as part of a longer-term integration to support the quantification of climate impacts through the addition of impact and financial metrics.
The data used in the GFM is derived from RMS probabilistic risk models. These are event-based models and account for precipitation as well as topography-related factors that can influence whether rainfall will lead to flooding.
For instance, the RMS models consider how rain enters river networks (and any overtopping that might cause), as well as the more localized ’ponding‘ of flood risk that can occur in low-lying elevation areas – even those that are not located near surface waters.
One of the key benefits of using the RMS models is that they leverage insights from both a spectrum of observed historical events and a stochastic event set that reflects the full range of possible events in space and time, based on 50,000 years of simulation to capture extremes. From this, views of risk are generated to include hazard return period outputs that better capture flood risk.
For countries such as the U.S. where we have an established inland flood model, users can see how our GFM aligns with or supplements the FEMA view of flood risk. For further information on how Moody’s Climate on Demand’s global flood perspective using the RMS GFM integration compares to FEMA flood maps, read this blog.
Developing Consistent Global Flood Risk Data
For many countries, the data required to develop a functioning hydrological model is incomplete or insufficient. A core part of the development of the new RMS GFM was to work out how to overcome the shortcomings in data availability while enabling an assessment of flood risk that is globally consistent.
Launched in 2021, the RMS GFM incorporates a new, unique methodology that combines best-in-class science with advances in technology to develop flood extents and depths globally.
RMS already has established inland flood models for countries such as the U.S., Europe, and Japan, and these models were used to train a machine learning model that can enable a representation of flood extents not only in these modeled countries but also in territories that lack the high-quality data that can be used to create a full hydrological model.
The result is a leveling-out of flood risk analysis, with representations of flood risk for developed countries using established models as well as in those countries, where the data needed to develop models is limited.
Unifying a level of flood risk analysis across all countries ultimately means that flood risk can be accessed at both the regional and global levels consistently across portfolios.
Accounting for Flood Mitigation
More recently, RMS has developed a global view of the Standard of Protection to assess infrastructure that helps mitigate flood risk. This view was developed to understand the implications of levees, banks, and other man-made structures that alter flood risk potential at a site.
This again helps to unify data quality across different regions and to account for the shortcomings in publicly available data for flood defenses which can be incomplete and outdated.
The result is access to high-quality, high-resolution views of flood risk globally that enable more informed flood risk assessment. The GFM data provides both the extent of inundation and the flood depth at 30-meter resolution, across multiple return periods (from 10 to 1,000 years), from high to low exceedance probabilities that consider the implications of defenses.
Incorporating Future Flood Risk
As Moody’s Climate on Demand integrates RMS flood modeling capabilities and data, it will provide users with a forward-looking, consistent and granular view of flood risk drivers.
The Climate on Demand approach will integrate RMS GFM together with peer-reviewed global climate model estimates that capture changes in worldwide precipitation, along with scenario analysis views available through 2100.
Assets showing the highest flood risk in Climate on Demand are highly susceptible to flooding and/or potentially exposed to changes in extreme precipitation that further exacerbate flood risk as the climate warms.
Users will be able to expose and unpack the primary drivers of an individual asset’s estimated flood risk, with Climate on Demand distilling these complex analytics into simple and easy-to-use metrics that enable effective forward-looking flood risk assessment.
Businesses are already benefiting from Moody’s Climate on Demand physical risk scoring application that provides a forward-looking view of an asset’s exposure to a range of physical climate risks including floods, heat stress, hurricanes and typhoons, sea level rise, water stress, and wildfires.
Today, Moody’s Climate on Demand application can evaluate the forward-looking climate hazard exposure of a portfolio of assets anywhere in the world in real-time, allowing users to examine specific risk drivers and explore underlying indicators that capture various dimensions of hazard risk.
As the need to understand climate risk grows ever more urgent, asset managers, lenders, corporates, and businesses all need to be confident that their climate risk models can capture the complexity of climate and weather events – in order to satisfy their regulators, boards, and shareholders.
Moody’s Climate on Demand has led the way in the provision of climate risk analytics, and during 2023 this innovative solution will deliver new risk metrics that capture the financial impacts of climate risk by integrating the expertise of Moody’s RMS market-leading climate risk modeling capabilities.
But how can users distinguish what makes a good climate risk model versus an inadequate one? Let’s take the case study of Hurricane Ian in 2022 to examine how well a climate risk model can reflect the reality and complexity of climate and weather events both now and in the future.
Major Hurricane Ian was an extremely large and devastating “all perils” Category 4 hurricane that struck Florida in September 2022 and will be ranked as one of the costliest hurricanes to ever affect the U.S.
Its size and intensity brought significant damage to Florida’s manufacturing, agriculture, tourism, and distribution sectors. Transportation continues to be affected months after as infrastructure is repaired, and the cost of property repairs will be one of the highest ever – and not all will be covered by insurance.
To accurately model the impact of extreme weather events such as Hurricane Ian and how they will change in the future requires a tried-and-tested, sophisticated and multi-dimensional approach.
Only Moody’s RMS delivers forward-looking climate risk models which combine best-in-class catastrophe models from the (re)insurance industry together with climate model outputs and the latest peer-reviewed scientific consensus. This helps capture the full range of possible events and their impacts that can occur now and in the future.
Impact of Hurricane Ian
Residential street in Fort Myers, Florida, after Hurricane Ian
Ian was the latest in a series of hurricanes that rapidly intensify immediately before landfall, bringing extensive rain and flooding across Florida on top of severe wind and storm surge damage in the landfall area; all trends which are expected to continue due to climate change.
Storms such as Ian in 2022, Ida in 2021, Harvey and Irma in 2017, Sandy in 2012, Ike in 2008, and Katrina in 2005 show the importance of utilizing climate risk models which account for all drivers of impact across multiple hazards.
The models can then establish the impact of these hazards on different asset types, and incorporate the current background of economic stress and inflation, the amplifying effects of extensive infrastructure damage on business interruption and downtime, together with the compounding effect on the overall loss from the many assets and businesses all being affected at the same time across the state.
Moody’s RMS models account for all these factors due to our physical climate risk modeling framework which has led the way in assessing the financial impacts of physical climate risk for the past 30 years in the (re)insurance industry.
These models are now being embraced by other sectors, which recognize that understanding the impact of events such as Ian requires the use of sophisticated risk models which capture the complexity of weather events and how climate change will affect them in the future.
By climate conditioning our catastrophe models and re-simulating the hazard to account for future climate impacts, for example, sea level rise, ocean waves, and coastal flooding, our climate risk models bring the best of both worlds to the industry.
While Ian made landfall in a similar area as Hurricane Charley in 2004, the storm was more than double Charley’s size with four times the destructive potential, making Ian's impacts significantly more material and demonstrating the impossibility of predicting future damage from past storms.
Moody’s RMS models account for the full range of possible hurricanes that can strike the U.S. in terms of location and strength, and how climate change may affect these factors in the future. In addition, our impact scores and financial loss metrics account for all aspects of hazard, such as the impacts of tornadoes, rainfall ingress through damaged roofs, and wind-blown debris as well as the major driving factors of wind, storm surge, and flooding.
As well as catastrophic damage to properties, Ian caused destruction to large amounts of infrastructure such as roads, bridges, and power networks. More than 2.6 million people were without power across Florida following the storm's landfall.
The considerable infrastructure damage, particularly in the hardest-impacted areas like Fort Myers and Cape Coral, will slow down the recovery and increase repair costs and losses, especially for islands disconnected from the mainland due to bridges and piers being washed out, such as Sanibel Island. The full recovery could take a few years in these areas, and some businesses and residents may never return, as witnessed after Hurricane Katrina hit New Orleans in 2005.
On top of such a destructive hurricane, the impact of recent inflationary trends will further increase losses. Shortages of materials, qualified contractors, and insurance claims adjusters in Florida add to the near-record inflationary trends being experienced in both the domestic and global economy.
The level and extent of disruption to water, sewage, and electricity supplies, extensive infrastructure damage, and delayed repairs due to residents unable to return to the area, all start to drive long-term consequences.
A loss of income for businesses and increased costs for residents who have to move elsewhere for weeks and months, means they are unable to start the process of repair and recovery. RMS modeling accounts for the various ways in which these costs and losses escalate within a major catastrophe through economic demand surge and super-catastrophe compounding effects.
The repair and recovery from Ian will take many months, and for some, years. However, what is clear is that without a deep understanding of all the drivers of impact and loss, you may be underestimating the risk of such events in the future.
Climate change is widely accepted as the next great integrated risk challenge. To ensure long-term economic resilience, a wholly robust and comprehensive approach for estimating climate impacts will be required, one that captures real asset losses as well as distributive business interruptions. This requires a worldwide transformation in asset management strategies to help reduce climate risk exposure.
The number of billion-dollar natural disasters has continued an upward trajectory in both frequency and severity since the 1980s. As shown below in the chart from the U.S. National Centers for Environmental Information, the number of billion-dollar climate-related natural disaster events in the U.S. averaged around three per year during the 1980s. This had grown to an average of 18 per year between 2017–2021.
Following increases in climate-driven losses, asset managers and owners urgently need to answer this question: How can I accurately assess the impact of climate and climate change on my asset portfolio to inform strategies that can help reduce my climate risk exposure?
While there are a growing number of applications on the market that attempt to provide climate risk scoring, having a reliance on predictions that lack transparency, validation, and reputational leadership in the risk industry (i.e. insurers and reinsurers) has real financial consequences – now more than ever.
RMS, a Moody’s Analytics company, in collaboration with Moody’s foundational Climate on Demand physical risk scoring application, is set to release a groundbreaking solution that meets this challenge, backed by decades of unparalleled experience and billions of dollars invested in physical climate risk assessment research and model development.
Moody’s Climate on Demand version 2 will offer financial metrics that robustly measure asset-level climate change impacts, by leveraging RMS’ position as a global leader in the (re)insurance industry.
Climate risk metrics generated in this release will be validated by a global physical climate loss experience totaling hundreds of billions of dollars, to complete a wholly reimagined market-leading application with physical climate metrics validated in the real world.
Businesses are already benefiting from Moody’s foundational Climate on Demand physical risk scoring application. This application provides a forward-looking view of an asset’s exposure to a range of physical climate risks including floods, heat stress, hurricanes and typhoons, sea level rise, water stress, and wildfires.
The current Climate on Demand application can evaluate the forward-looking climate hazard exposure for a portfolio of assets located anywhere in the world and in real time, allowing users to examine specific risk drivers and explore underlying indicators that capture various dimensions of hazard risk.
With this new version 2 release, Climate on Demand will include exciting updates that enable more accurate calculation of impact in financial terms.
Latest Generation of Moody’s Climate on Demand
Moody’s next version of Climate on Demand will enable users to gain deeper insights into the financial impact of physical climate risk, benefiting from the integration of RMS’ models, data, and technology.
Expected in early 2023, Climate on Demand version 2 will provide financial impact metrics and risk impact and hazard scores tailored to assets globally, and for multiple climate change scenarios through to the year 2100.
RMS is in a unique position to provide these unparalleled advancements in how forward-looking risk is estimated, owing to the following:
Breaking Down Our Approach to Assessing Physical Climate Change Risk
The RMS modeling approach seeks to robustly capture the full range of impacts that climate has on real assets, with an approach rooted in encapsulating the exposure to global hazards for each specific geolocation, and the individual financial risks associated with each asset’s unique exposure and vulnerability, as defined below:
For businesses, the losses from business interruption also needs to be modeled. Losses due to business downtime can easily overshadow any losses from physical damage in financial terms. RMS modeling accommodates these business interruption factors attuned to business type, e.g. a factory, retail store, or industrial complex.
Vulnerability relates to the susceptibility of an asset to damage from a given hazard, such as how a building interacts with a hurricane, as an individual building’s response varies depending on factors such as the site location or industry type. All of this is captured within the models using hundreds of detailed vulnerability and impact curves that relate hazard to financial loss.
Ultimately, RMS has continued to refine and validate its approach to estimating the complete physical risk of real assets and will now integrate its improved hazard scoring, exposure, and vulnerability methodologies into version 2 of Moody’s Climate on Demand.
In addition, Climate on Demand version 2 will also have advanced portfolio aggregation and correlation analytics to allow users to look at accumulated risks across diverse asset classes and geographies. The analytics capability of Climate on Demand will enable accurate modeling of a portfolio of assets, taking into account the correlation and interdependencies between different climate hazards and locations to give meaningful overall risk scores.
Look out for the new version of Moody’s Climate on Demand in 2023, with decision-ready financial metrics, tailorable asset features, and smart portfolio management.
Our vision for Climate on Demand will also enable users to customize their solution package to increasingly granular levels of risk identification and quantification to meet their individual use case requirements. As we continue to work hard on delivering the next Climate of Demand version, please watch for deep dives into each of our upcoming feature sets.
If you’d like to dig deeper today and find out more details on the capabilities of the Climate on Demand application, click here to email us.
As the market for physical climate risk solutions grows, both the degree of divergence and uncertainty remains very high for risk scoring estimates across different applications, making it nearly impossible for customers to confidently navigate physical climate risk.
The limitations of using publicly available climate model scenarios were recently discussed in a Finance Research Letters paper published earlier this year, with the authors highlighting these scenarios as a leading cause of divergence across market-leading solutions seeking to quantify climate change risks.
In addition, if most market solutions are all employing the same publicly available climate scenario data, then why is there such a range of predicted hazard risk outcomes?
The answer is that each application has a unique approach to applying this somewhat coarse publicly available model data within their high-resolution and customized forward-looking time horizons – which are essential for estimating climate risk for real assets.
With this puzzling background, if your business is a consumer of climate risk data, my hope is that after reading this blog you will be more confident when deciding which methodologies you want to apply in your selected climate risk solution – and why it matters to your financial management decisions.
Climate Risk Modeling Uncertainties
Climate models allow us to simulate the effect that increasing greenhouse gas concentrations will have on temperatures, humidity, precipitation, windspeeds, and other climate variables. However, they come with uncertainties.
For example, although modeling resolutions are improving, there are still limitations in their ability to capture all meaningful processes, and therefore cannot be used to directly assess how extreme weather events such as hurricanes or severe convective storms will change in the future.
Therefore, relying directly on downscaled climate model output for quantification of future physical climate risk can lead to erroneous results and poor decisions.
And although there is strong scientific consensus on how climate change is affecting some physical variables such as global mean surface temperatures, consensus for climate change signals in extreme weather varies significantly between perils and regions.
To tackle these issues, RMS combines the benefits of our best-in-class catastrophe models from the (re)insurance industry with the latest climate change science, for both acute and chronic physical risks.
Climate change science is an ever-evolving discipline, and as new data, studies, and modeling projects become available, we update our models frequently to incorporate the latest science.
Forward-Looking View of Climate Risk
If done correctly, combining the catastrophe model framework together with publicly available climate data sets lends itself perfectly to developing forward-looking views of climate risk. One of the common misconceptions about catastrophe models is that they are backward-looking and only represent a historical view.
However, by directly climate conditioning the physical variables that drive these events, and the relevant interactions between these variables, we can fully capture the change in risk that may occur in the future, through both time and space.
Additionally, the RMS climate risk models are underpinned by comprehensive catalogs of 100,000s of possible weather events globally, with each event incorporating detailed hazard footprints that capture wind patterns, precipitation, storm surge, wildfire, droughts, heatwaves, and so on, throughout the full lifecycle of an event.
For example, we account for spatially varying changes in precipitation by climate conditioning our flood models on high-resolution rainfall patterns, rather than just altering water depths in existing flood hazard footprints.
Within the RMS climate risk models, these climate-conditioned event sets are then combined with detailed vulnerability curves that are built on US$100s billion of real-world loss experience to translate the hazard to losses for a multitude of different property and business types across the world. The models can quantify financial loss potential for a range of IPCC Representative Concentration Pathway (RCP) scenarios and time horizons out to 2100.
Integrating Climate-Conditioned Catastrophe Models
As part of Moody’s Analytics, the RMS climate-conditioned catastrophe models will be integrated within the Moody’s Climate on Demand application in 2023, to deliver forward-looking, physical climate risk quantification to banks, investors, asset managers, and many other organizations who need to understand and better manage their risk and find new opportunities for growth.
I hope this blog has provided a snapshot view of climate risk modeling at RMS. It is undeniable that modeling the impacts of climate change is challenging, computationally intensive, and filled with uncertainties, but our extensive experience in modeling real-world climate impacts provides customers with a robust platform to make decisions with confidence.
With a successful track record of more than 30 years serving the highly regulated insurance industry, our clients know that they are accessing climate-risk intelligence from a tried-and-tested organization and that we understand the rigor, accuracy, and validation required in order for models to be trusted for high stakes financial decision making.
RMS is excited to tackle the next most urgent integrated risk challenge of our time with you. To learn more about our market-leading solutions today contact us here.