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Hazard exposure data processing

Approach

Flooding

Flooding data under historic and future scenarios was kindly provided by Fathom, a UK-based flood hazard intelligence company1.

A dataset was created of a 1 in 100-year event at a Representative Concentration Pathway (RCP) 8.5 scenario in 2050, representing a 'typical' flood preparation scenario, as well as a 'disaster' scenario using 1 in 1000-year modelling with the same projections. These scenarios are equivalent to those used by the UK Environment Agency for flood hazard zoning, but with a higher spatial resolution of 10m and additionally adjusted for climate change2.

Lower Layer Super Output Areas (LSOAs), UK statistical geographies covering 1,000-3,000 people each, were ranked as deciles by the aggregated proportion of buildings that experience any coastal, fluvial, or pluvial flooding greater than 10 cm, anywhere in their footprint.

Heat

Heat data was extracted from the Met Office's HadUK-Grid dataset at 1 km horizontal spatial resolution3.

This historical data was used due to improved resolution above UK Climate Projections (UKCP18) and as it fulfilled the requirements for the feasibility nature of this study. To isolate the warm season in the current climate, we limited the data to June, July, and August (JJA) in the period 2015-2019.

Datasets were created for the average JJA maximum temperature from the data to represent average summer daytime temperatures, as well as the proportion of days where the average temperature exceeded 15°C, the mean temperature above which mortality risks in Southwest England increase45.

In each case, LSOAs were ranked as deciles by the weighted average of these metrics, weighted according to the density of buildings within each LSOA.

Key files and directories

This directory contains code and data for the heat and flooding hazard exposure analysis.

  • code/

    • calculate_heat_hazard_indicators.py - Generates gridded input data for the heat hazard analysis (average maximum temperature and days exceeding threshold metrics) from HadUK-Grid data. Designed to be run in advance on a high performance computing (HPC) cluster that provides access to the HadUK-Grid dataset.

  • notebooks/

  • data/

    • input/

      • buildings/ - The Building layer from OS OpenMap - Local dataset.

        • ST_Building.dbf
        • ST_Building.prj
        • ST_Building.shp
        • ST_Building.shx

      • fathom/ - Flood level data provided under licence by Fathom.

        • UK_1.1_Bristol/10m/coastal/rcp85_2050/1in100/n51w3_C.tif
        • UK_1.1_Bristol/10m/coastal/rcp85_2050/1in1000/n51w3_C.tif
        • UK_1.1_Bristol/10m/fluvial/rcp85_2050/1in100/n51w3_F.tif
        • UK_1.1_Bristol/10m/fluvial/rcp85_2050/1in1000/n51w3_F.tif
        • UK_1.1_Bristol/10m/pluvial/rcp85_2050/1in100/n51w3_P.tif
        • UK_1.1_Bristol/10m/pluvial/rcp85_2050/1in1000/n51w3_P.tif

      • hadukgrid/ - Gridded input data derived from HadUK-Grid (see above).

        • average_daily_maximum.tif
        • days_over_threshold.tif

Footnotes

  1. Bates, P.D. et al. "A climate-conditioned catastrophe risk model for UK flooding," Natural Hazards and Earth System Sciences. 2023;23(2):891-908. ↩

  2. Department for Environment Food & Rural Affairs. Flood risk assessment in flood zones 2 and 3 guidance 2017 [29.05.2023]. https://www.gov.uk/guidance/flood-risk-assessment-in-flood-zones-2-and-3 ↩

  3. Hollis D, McCarthy M, Kendon M, Legg T, Simpson I. HadUK‐Grid—A new UK dataset of gridded climate observations. Geoscience Data Journal. 2019;6(2):151-9. ↩

  4. Lo YE, Mitchell DM, Thompson R, O’Connell E, Gasparrini A. Estimating heat-related mortality in near real time for national heatwave plans. Environmental research letters. 2022;17(2):024017 ↩

  5. Lo YE. Personal communication, 29 June, 2023. ↩