Wealth over Woe: global biases in hydro-hazard research

This is a Preprint and has not been peer reviewed. This is version 2 of this Preprint.

Add a Comment

You must log in to post a comment.


Comments

There are no comments or no comments have been made public for this article.

Downloads

Download Preprint

Authors

Lina Stein , S. Karthik Mukkavilli, Birgit M. Pfitzmann, Peter W.J. Staar, Ugur Ozturk, Cesar Berrospi, Thomas Brunschwiler, Thorsten Wagener 

Abstract

Floods, droughts, and rainfall-induced landslides are hydro-geomorphic 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 the most severe. We use natural language processing, based on a new climate hazard taxonomy, to review, identify, and geo-locate 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-hazards, which impact large numbers of people, increase research activity, but with a strong disparity between low- and high-income countries. We find that a 100 times higher impact is needed before low-income countries reach comparable research activity to high-income countries. This "Wealth over Woe" bias needs to be addressed by increasing 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 16:20

Last Updated: 2024-01-16 17:19

Older Versions
License

CC-By Attribution-ShareAlike 4.0 International