Opportunities for crowdsourcing in urban flood monitoring. (September 2021)

• Record Type: Journal Article
• Authors: Helmrich, Alysha M.; Ruddell, Benjamin L.; Bessem, Kelly; Chester, Mikhail V.; Chohan, Nicholas; Doerry, Eck; Eppinger, Joseph; Garcia, Margaret; Goodall, Jonathan L.; Lowry, Christopher; Zahura, Faria T.
• Is Part Of: Environmental modelling & software. Volume 143(2021)
• Publication Date: 2021-09
• Subjects: Urban hydrology -- Flooding -- Crowdsourced data -- Data integration; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2021.105124
• Languages: English
• ISSNs: 1364-8152

Abstract

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. Highlights: Urban hydrological data is oftentimes spatially coarse, challenging urban flood monitoring. Public webcams, social media, and citizen science projects collect data at fine spatial scales. Crowdsourced data are readily available and, cooperatively, may support urban flood monitoring.