Flood resilience through crowdsourced rainfall data collection: Growing engagement faces non-uniform spatial adoption

Abstract

Crowdsourced Personal Weather Stations (PWSs) adoption has been growing rapidly and provides the potential to fill in hyper-local rainfall observation gaps. However, current adoption patterns exhibit spatial biases that must be understood when using the data for modeling and decision-making. Here, we first examine the PWS rainfall spatial representation at HUC-12 watersheds in twelve metropolitan areas in the U.S. Furthermore, by modeling the PWS adoption using socio-economic and flood-related data at census tract level, the results suggest current adoption patterns exhibit spatial biases toward wealthier neighborhoods and flood-prone regions. The findings provide insights to inform how policies could be made to distribute resources to improve the rainfall data collection efforts in PWS-underrepresented regions. As crowdsourced data are increasingly used for decision-making by policymakers, efforts to close the gap in current non-uniform PWS spatial adoption will allow crowdsourced rainfall data to be better positioned to support decision-makers in their flood resilience efforts.