A Software System for Multi-Hazard Assessment of Vulnerability, Risks and Potential for Economic Loss due to Weather and Climate Variability

William Clark


Climate and weather related disasters are some of the greatest threats to the global economy. These hazardous events cause negative economic outcomes including damaged infrastructure, business disruption, and public health issues; they can be induced by human practices and natural climate variability. Research in the way of geographical impact of these environmental catastrophes is fairly substantial. However, research is still needed to fiscally account for impending severe natural hazard circumstances. Research and tools focusing on the methodology for assessing and estimating the potential economic loss from natural disasters is currently lacking. Thus, the purpose of this case study is to present a methodology in creating such tools for analysis. Using the theory behind the structure of geographic information systems (GIS), spatially enabled programming languages such as Python and Structured Query Language (SQL) can be utilized to create a multi-hazard assessment of economic loss on the county level. The weather and climatic phenomena modeled in this study are wildfires, landslides, and different flooding scenarios (e.g. precipitable flooding and coastal flooding brought on by storm surges). Given data that includes property information, historic climate data and future climate model data, GIS processes are implemented to spatially join these hazard data on to the property level data and extract information about the infrastructure. Once the impacted infrastructure data is created, an assessment of loss can be made by taking summary statistics of the property values. These summary statistics show the total direct economic impact that multiply hazards on infrastructure in United States Dollar (USD) amount. These types of model outputs can be used in impact assessment and resilience planning efforts on the local, state, and federal scales


spatial programming, automated systems, catastrophe modeling

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