This year the WTW Research Network continued their long-standing partnership with National University of Singapore (NUS) Tropical Marine Science Institute (TMSI) to build on the work previously showcased in the 2021 Annual Review on ‘Filling in the gaps in model coverage in Asia’. The expansion of this project generated a broader collaboration across the public and private sector including JBA – the global leaders in flood risk modeling, Spatial Finance Initiative (SFI) – which aims to mainstream geospatial capabilities into financial decision making globally.
For the past 15 years WTW Research Network has worked with NUS to explore the influences of climate change on extreme flood and rainfall distributions that could form the basis of scenarios and future event sets. Currently the WTW Research Network is collaborating with multiple partners, utilizing TMSI’s outputs as a foundation to provide a forward-looking view of acute flood risk and chronic water stress at asset level in Southeast Asia.
JBA estimates that over 60% of the global population are at risk to inland flooding annually, and almost 50% of global flood losses are in eight countries in Asia. This highlights the importance of evaluating this region and creating a blueprint method for future regional analysis.
TMSI’s climate change impact on precipitation and temperature outputs were used to climate condition JBA’s global flood model.
Using multiple climate model outputs, downscaling them to regional level and investigating the change factors under various Representative Concentration Pathways (RCP) scenarios, NUS provided a range of possible outcomes for temperature and precipitation changes for Southeast Asia model domain.
According to their findings the future climate projections indicate that average surface temperatures over Southeast Asia are likely to increase by more than 3.5 °C by the end of the century. As for precipitation, both the mean and extreme rainfall are likely to increase but the biases in the historical simulations could contribute to larger uncertainties in the estimates of rainfall projections.
JBA’s flood model contains a global event catalogue of 15 million simulated events, 2 million of these impact Southeast Asia. Using NUS’s research outputs, JBA created climate conditioned event sets under various RCP scenarios and for near and long-term future time frames, enabling them to calculate present day and future flood risk at location level. Spatial and temporal variations projected in the precipitation and temperature changes resulted in material risk changes across the region.
The final model will be available on the Nasdaq platform which provides transparency and flexibility for users through the ability to confidently understand the key climate, hazard and exposure assumptions and model settings used.
For location level analysis, SFI provided open-transparent asset data to enable the evaluation of future flood risk for Southeast Asia. SFI provides open, global databases for physical assets in every major sector of global economy. They leverage advances in remote sensing and artificial intelligence to identify and characterize physical assets. For this pilot project SFI provided asset data for steel and cement production, both of which are water dependent activities which can have material risks from both flooding and droughts. This asset data was used as the input exposure data for the JBA flood modeling. Having this level of data helps significantly with comprehensive risk assessment. Exposure is a dynamic input; it changes every day. Therefore, a curated, open-source, consistent database is a major advantage to analyze climate change impact of key asset classes.
This is a unique project, bringing together expertise and capability from different sectors, which demonstrated the value of open-source data and the progress being made in understanding financial risks under a changing climate. Through TMSI’s regional climate outputs, SFI’s asset data and JBA’s baseline and climate conditioned projected risk modeling, the current and future risk change of real assets is possible.
