High-resolution multi-hazard residential buildings and population exposure model for coastal areas

A case study in northeastern Italy

Bibliographic Data

ID22029002
AuthorsHazem Badreldin (0000-0002-5410-3449, National Research Institute of Astronomy and Geophysics, corresponding author), Chiara Scaini (0000-0001-9645-7918, National Institute of Oceanography and Applied Geophysics), Hany M Hassan (0000-0002-3255-7474, National Research Institute of Astronomy and Geophysics), A Peresan (0000-0001-9915-1068, National Institute of Oceanography and Applied Geophysics)
Year2025
Volume121
Pages105403
Publication date2025-04-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueInternational Journal of Disaster Risk Reduction (JOURNAL)
Journal identifiersISSN: 2212-4209
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.ijdrr.2025.105403
OpenAlexW4408541585
LanguageEN
Citations received4
References cited58

Developing high-resolution multi-hazard exposure models significantly improves risk assessment and loss estimation. In the present study, we propose and verify a methodology for developing a high-resolution exposure model for population and residential buildings that could be used for multi-hazard risk mitigation at the local scale across the globe. The methodology is applied to Lignano municipality, a coastal area located in the northern Adriatic, prone to multiple hazards such as seismically-induced tsunamis, meteorological events, coastal erosion and subsidence. The population exposure layer is developed integrating population data with demographic characteristics and socio-economic indicators. In parallel, the building exposure layer, which combines census data with digital building footprints, contains information about: geographic distribution, age of construction or retrofit, number of storeys, construction material types, average built-up area, structural replacement cost, and structural regularity. These data layers are made available at two resolutions: 100 m and 30 m, with information also provided at the census unit level. We describe the methodology developed for exposure assessment and discuss its potential use for multi-hazard risk assessment in coastal areas. • Developing a high-resolution exposure model for multi-risk reduction purposes. • Population exposure model is developed combing Meta data with census population. • Population is classified into many classes based on its demographic characteristics. • Building exposure model combines census buildings with digital building footprints. • These models are available at the census unit level, 100m and 30m resolutions

Environmental health · Geography · Hazard · Population · Environmental Science · Flood Risk Assessment and Management · Impact of Light on Environment and Health · Medicine · Wind and Air Flow Studies · Ecology

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