Opportunities for crowdsourcing in urban flood monitoring

Alysha M. Helmrich, Benjamin L. Ruddell, Kelly Bessem, Mikhail V. Chester, Nicholas Chohan, Eck Doerry, Joseph Eppinger, Margaret Garcia, Jonathan L. Goodall, Christopher Lowry, Faria T. Zahura

Research output: Contribution to journalReview articlepeer-review

Abstract

Flooding is the most common natural hazard, leading to property damage, injuries, and death. Despite the potential for major consequences, urban flooding remains difficult to forecast, largely due to a lack of data availability at fine spatial scales and associated predictive capabilities. Crowdsourcing of public webcams, social media, and citizen science represent potentially important data sources for obtaining fine-scale hydrological data, but also raise novel challenges related to data reliability and consistency. We provide a review of literature and analysis of existing databases regarding the availability and quality of these unconventional sources that then drives a discussion of their potential to support fine-grained urban flood modelling and prediction. Our review and analysis suggest that crowdsourced data are increasingly available in urban contexts and have considerable potential. Integration of crowdsourced data could help ameliorate quality and completeness issues in any one source. Yet, substantial weaknesses and challenges remain to be addressed.

Original languageEnglish (US)
Article number105124
JournalEnvironmental Modelling and Software
Volume143
DOIs
StatePublished - Sep 2021

Keywords

  • Crowdsourced data
  • Data integration
  • Flooding
  • Urban hydrology

ASJC Scopus subject areas

  • Software
  • Environmental Engineering
  • Ecological Modeling

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