17–18 Mar 2026
Musées Royaux des Beaux-Arts de Belgique
Europe/Brussels timezone

Wikimpacts 1.0: A new global climate impact database based on automated information extraction from Wikipedia

18 Mar 2026, 13:22
1m
Poster presentation Latest advances in physical climate science Poster session day 2

Speaker

Ni Li (Department of Water and Climate, Vrije Universiteit Brussel, Brussels, Belgium)

Description

Extreme climate events pose serious threats to human society and ecosystems. Measuring their impacts remains a crucial challenge scientifically. Although data linking climate hazards to socio-economic effects are crucial, their public availability is still relatively sparse. Existing open databases such as EM-DAT and DesInventar Sendai offer some impact data on climate extremes, but impact data on climate extremes also appear in newspapers, reports, and online sources like Wikipedia.

Wikimpacts 1.0, a comprehensive global database on climate impacts developed using natural language processing techniques. This database utilizes the GPT4o model for extracting information, following document selection, post-processing, and data consolidation. In this release, impact data for each event is recorded at three levels: event, national, and sub-national. Categories include the number of deaths, injuries, homelessness, displacements, affected individuals, damaged buildings, and insured or total economic damages. This dataset encompasses 2,928 events from 1034 to 2024, featuring 20,186 national and 36,394 sub-national data entries. Comparison with manually annotated data from 156 events shows that the Wikimpacts database is highly accurate in the event level for time, location, deaths, and economic damage, though details on injuries, affected individuals, homelessness, displacements, and building damage are slightly less precise. An analysis from 1900 to 2024 demonstrates that sub-national data provides more comprehensive coverage of tropical and extratropical storms, and wildfires than EM-DAT, with enhanced data on events in countries like the United States, Mexico, Canada, and Australia. Our study emphasizes the potential of natural language processing in creating open databases on climate event impacts.

If your abstract is not accepted for an oral presentation, would you be interested in presenting it as a poster instead? Yes
If accepted by the Scientific Board, I agree to have my presentation/poster and abstract published on the Belgian Climate Centre websites and social media. Yes

Primary author

Ni Li (Department of Water and Climate, Vrije Universiteit Brussel, Brussels, Belgium)

Co-author

Wim Thiery

Presentation materials