Modeling climate risks to infrastructure and supply chains | 9am PT Oct 17

[Grigory Bronevetsky]

Modeling Talks

Modeling climate risks to infrastructure and supply chains Jim Hall, Oxford Tuesday, Oct 17 | 9am PT Meet | Youtube Stream Hi all, The presentation will be via Meet and all questions will be addressed there. If you cannot attend live, the event will be recorded and can be found afterward at https://sites.google.com/modelingtalks.org/entry/modelling-climate-risks-to-infrastructure-and-supply-chains Abstract: Critical infrastructure (including power, water and transport) depends upon interconnectivity to deliver essential functions for society and the economy. There has been rapid progress in using Earth Observation and crowd-sourced datasets to map and model critical infrastructures. These models of infrastructure exposure can be intersected with models of extreme climatic hazards to identify hotspots of vulnerability and prioritize resilience action. This talk will overview the framework for climate risk analysis developed in the Oxford Programme for Sustainable Infrastructure Systems https://global.infrastructureresilience.org/ and will present new insights into tropical cyclone risks to power networks and multi-hazard risk analysis to ports, shipping networks and supply chains.   Bio: Professor Hall addresses how much we need to adapt to climate change, and the risks to infrastructure systems. He is internationally recognised for his research on risk analysis and decision making under uncertainty for water resource systems, flood and coastal risk management, infrastructure systems and adaptation to climate change. He is a member of the Prime Minister's Council for Science and Technology, Expert Advisor to the National Infrastructure Commission, and Chair of the Science Advisory Committee of the International Institute for Applied Systems Analysis (IIASA). He was a member of the UK independent Committee on Climate Change Adaptation from 2009 to 2019, and was a Contributing Author to the Nobel Prize-winning Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Professor Hall's group in the University of Oxford is at the forefront of risk analysis of climatic extremes and their impacts on infrastructure networks and economic systems, from local to global scales. He led the development of the National Infrastructure Systems Model (NISMOD), which was used for the UK's first National Infrastructure Assessment and for analysis of the resilience of energy, transport, digital and water networks in Great Britain. His group developed the first national water resource systems simulation model for England and Wales. More information on previous and future talks: https://sites.google.com/modelingtalks.org/entry/home

[Grigory Bronevetsky]

Video Recording: https://youtu.be/JLuHcSJSeLE

Slides: https://drive.google.com/file/d/1jfVGIem-dsXILuXuENc9yNQWBgoGohXS/view?usp=sharing

Summary:

• Natural disasters impact our critical infrastructure

  • E.g. Thailand floods hit major manufacturing facilities; insurers surprised by companies’ exposure to event

  • Insured losses have been rising over time since 1970s

• Insurers use catastrophe “Cat” models to estimate risk of extreme events

  • Event Set -> Hazard -> Vulnerability -> Financial Module

  • Small number of Cat model vendors who license models as black boxes

  • Oasis Loss Modeling Framework is attempting to open up Cat models

    • https://oasislmf.org/

    • https://oasislmf.github.io/

  • Climate change is making infrastructure more vulnerable to extreme events, which may make Cat model too optimistic

  • Insurers mostly care about the next several years since policies are typically short-term

  • Companies/societies care about next several decades (damages and premiums will rise)

  • Climate models disagree in their predictions about extreme events

• Developing system for predicting risk from climate change-driven extreme events on infrastructure

  • Hazard

  • Networks

  • Services

  • Socio-economics

  • Asset, condition, exposure -> Direct damages

  • Disruption to service -> Indirect damages

  • Macroeconomic events 

  • ===> Sum(Probability×Impact) = Risk

  • Adaptation options: reduce risk

  • https://global.infrastructureresilience.org

  • https://github.com/nismod/open-gira

• Example: cyclone risks to infrastructure

  • Cyclone tracks database

  • Global network of power networks

    • Collaboration with World Bank on GridFinder

    • https://datacatalog.worldbank.org/search/dataset/0038055

    • https://www.nature.com/articles/s41597-019-0347-4

  • Superimpose maximum wind speed from cyclone on the network to infer the number of assets that will fail

    • Use nighttime light data before/after cyclones to infer damage from different wind speeds

    • Currently it is hard to get data on the physical design of power networks (pole material, over/underground, etc.) so accounting for this is future work

  • Performed risk assessment using synthetic distributions of cyclone tracks

    • STORM: https://www.nature.com/articles/s41597-020-0381-2

    • IRIS (Sparks & Toumi)

• Example: impact on ports and supply chains

  • 1350 ports

  • Datasets used to analyze impact of extreme weather on port activity

    • OxMarTrans Transport and Trade model

    • Import/export data between sectors and countries

    • Port bill data

    • AIS Ship tracking data

  • Infer the port-to-port shipping network model

  • Makes it possible to understand the impact of a disruption at one port on other ports

  • Focused on the impact of different hazards on each port: Cyclone, Fluvial, Coastal, Pluvial, Earthquake, Operational

  • Application: Using model for decarbonization scenarios

    • E.g. adoption of green ammonia

    • Identified the points where ammonia can be generated, where it would be shipped to and how to refuel ships using it

    • Australia would be the producer for Asia, and West Africa for Europe and Chile for North America (unless US creates subsidies for domestic production)

  • Application: routing agricultural commodities through shipping network

    • Estimate cost of food delivery

    • Infer impact of food shocks

    • Optimal design of distribution network and improve its resilience

• Challenges:

  • Availability of detailed infrastructure, trade and supply chain data

  • Validation is hard because there are not that many extreme events historically and their impacts are very specific to the impacted location

  • Future socioeconomic changes are unpredictable