Journal article
Aggregation bias and its drivers in large-scale flood loss estimation: A Massachusetts case study
Journal of flood risk management, Vol.15(4), p.n/a
12/2022
DOI: 10.1111/jfr3.12851
Abstract
Large-scale estimations of flood losses are often based on spatially aggregated inputs. This makes risk assessments vulnerable to aggregation bias, a well-studied, sometimes substantial outcome in analyses that model fine-grained spatial phenomena at coarse spatial units. To evaluate this potential in the context of large-scale flood risk assessments, we use data from a high-resolution flood hazard model and structure inventory for over 1.3 million properties in Massachusetts and examine how prominent data aggregation approaches affect the magnitude and spatial distribution of flood loss estimates. All considered aggregation approaches rely on aggregate structure inventories but differ in whether flood hazard is also aggregated. We find that aggregating only structure inventories slightly underestimates overall losses (-10% bias), and when flood hazard data is spatially aggregated to even relatively small spatial units (census block), statewide aggregation bias can reach +366%. All aggregation-based procedures fail to capture the spatial covariation of inputs distributions in the upper tails that disproportionately generate total expected losses. Our findings are robust to several key assumptions, add important context to published risk assessments and highlight opportunities to improve flood loss estimation uncertainty quantification.
Details
- Title: Subtitle
- Aggregation bias and its drivers in large-scale flood loss estimation: A Massachusetts case study
- Creators
- Adam B. Pollack - Boston UniversityIan Sue Wing - Boston UniversityChristoph Nolte - Boston University
- Resource Type
- Journal article
- Publication Details
- Journal of flood risk management, Vol.15(4), p.n/a
- DOI
- 10.1111/jfr3.12851
- ISSN
- 1753-318X
- eISSN
- 1753-318X
- Publisher
- Wiley
- Number of pages
- 16
- Grant note
- DE-SC0022141 / U.S. Department of Energy (DOE); United States Department of Energy (DOE) Boston University DE-SC0016162; DE-SC0022141 / U.S. Department of Energy, Office of Science, Biological and Environmental Research Program, Earth and Environmental Systems Modeling, MultiSector Dynamics; United States Department of Energy (DOE)
- Language
- English
- Date published
- 12/2022
- Academic Unit
- School of Earth, Environment, and Sustainability
- Record Identifier
- 9985112882602771
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