Aggregation bias and its drivers in large‐scale flood loss estimation: A Massachusetts case study
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 p...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-12-01
|
| Series: | Journal of Flood Risk Management |
| Subjects: | |
| Online Access: | https://doi.org/10.1111/jfr3.12851 |
| _version_ | 1811231095485628416 |
|---|---|
| author | Adam B. Pollack Ian Sue Wing Christoph Nolte |
| author_facet | Adam B. Pollack Ian Sue Wing Christoph Nolte |
| author_sort | Adam B. Pollack |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T10:40:24Z |
| format | Article |
| id | doaj.art-92befc66a14848349e68cf6e50b15abb |
| institution | Directory Open Access Journal |
| issn | 1753-318X |
| language | English |
| last_indexed | 2024-04-12T10:40:24Z |
| publishDate | 2022-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Flood Risk Management |
| spelling | doaj.art-92befc66a14848349e68cf6e50b15abb2022-12-22T03:36:37ZengWileyJournal of Flood Risk Management1753-318X2022-12-01154n/an/a10.1111/jfr3.12851Aggregation bias and its drivers in large‐scale flood loss estimation: A Massachusetts case studyAdam B. Pollack0Ian Sue Wing1Christoph Nolte2Department of Earth and Environment Boston University Boston Massachusetts USADepartment of Earth and Environment Boston University Boston Massachusetts USADepartment of Earth and Environment Boston University Boston Massachusetts USAAbstract 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.https://doi.org/10.1111/jfr3.12851aggregation biasflood loss uncertaintyflood risk estimationflood risk managementflood risk mapping |
| spellingShingle | Adam B. Pollack Ian Sue Wing Christoph Nolte Aggregation bias and its drivers in large‐scale flood loss estimation: A Massachusetts case study Journal of Flood Risk Management aggregation bias flood loss uncertainty flood risk estimation flood risk management flood risk mapping |
| title | Aggregation bias and its drivers in large‐scale flood loss estimation: A Massachusetts case study |
| title_full | Aggregation bias and its drivers in large‐scale flood loss estimation: A Massachusetts case study |
| title_fullStr | Aggregation bias and its drivers in large‐scale flood loss estimation: A Massachusetts case study |
| title_full_unstemmed | Aggregation bias and its drivers in large‐scale flood loss estimation: A Massachusetts case study |
| title_short | Aggregation bias and its drivers in large‐scale flood loss estimation: A Massachusetts case study |
| title_sort | aggregation bias and its drivers in large scale flood loss estimation a massachusetts case study |
| topic | aggregation bias flood loss uncertainty flood risk estimation flood risk management flood risk mapping |
| url | https://doi.org/10.1111/jfr3.12851 |
| work_keys_str_mv | AT adambpollack aggregationbiasanditsdriversinlargescalefloodlossestimationamassachusettscasestudy AT iansuewing aggregationbiasanditsdriversinlargescalefloodlossestimationamassachusettscasestudy AT christophnolte aggregationbiasanditsdriversinlargescalefloodlossestimationamassachusettscasestudy |