This is a Preprint and has not been peer reviewed. This is version 3 of this Preprint.
Downloads
Authors
Abstract
Floods, droughts, and rainfall-induced landslides are hydro-hazards that affect millions of people every year. Anticipation, mitigation, and adaptation to these hazards is increasingly outpaced by their changing magnitude and frequency due to climate change. A key question for society is whether the research we pursue has the potential to address knowledge gaps and to reduce potential future hazard impacts where they will be most severe. We use natural language processing, based on a new climate hazard taxonomy, to review, identify, and geolocate out of 100 million abstracts those that deal with hydro-hazards. We find that the spatial distribution of study areas is mostly defined by human activity, national wealth, data availability, and population distribution. Hydro-hazard events that impact large numbers of people lead to increased research activity, but with a strong disparity between low- and high-income countries. We find that 100 times more people need to be affected by hazards before low-income countries reach comparable research activity to high-income countries. This "Wealth over Woe" bias needs to be addressed by enabling and targeting research on hydro-hazards in highly impacted and under-researched regions, or in those sufficiently socio-hydrologically similar. We urgently need to reduce knowledge base biases to mitigate and adapt to changing hydro-hazards if we want to achieve a sustainable and equitable future for all global citizens.
DOI
https://doi.org/10.31223/X5D687
Subjects
Computer Sciences, Hydrology, Nature and Society Relations, Sustainability
Keywords
disaster risk reduction, flood, drought, Landslide, natural language processing
Dates
Published: 2024-01-12 15:20
Last Updated: 2024-08-30 20:04
There are no comments or no comments have been made public for this article.