Flood resilience through crowdsourced rainfall data collection: Growing engagement faces non-uniform spatial adoption
Alexander B. Chen, Jonathan L. Goodall, T. Donna Chen, Zihao Zhang
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.