bloomWatch

The bloomWatch (Tier 1) program, a component of the Cyanobacteria Monitoring Collaborative, utilizes a crowdsourced mobile application to photographically document the spatial and temporal occurrence of cyanobacteria and harmful algal blooms (HABs). Recognizing that these blooms are often highly variable and short-lived, the initiative leverages the presence of citizen scientists and local stakeholders to capture real-time data that state officials might otherwise miss due to logistical constraints. By engaging the public through common smartphone technology, the program aims to address the significant under-reporting of bloom conditions while simultaneously educating the community on water quality risks. The bloomWatch App guides users through a structured four-screen data entry process designed to provide a "weight of evidence" for potentially harmful conditions. Users input general location data and environmental observations—such as weather and lake surface conditions—using familiar spatial references like "tennis courts" to quantify bloom size. A critical requirement of the submittal process is the capture of three distinct, geo-located images from a single vantage point: a large-scale landscape photo to show the bloom's extent, a mid-range photo (10 to 30 feet) to document surface scums or shoreline accumulation, and a close-up macro image (3 feet or less) to help verify the specific nature of the cyanobacteria. Once submitted, the data is automatically uploaded to a public crowdsourcing database and dashboard at Cyanos.org, while simultaneously notifying pre-determined state or local contacts for immediate action. While personal contributor information remains protected, the aggregated data is made available for public exploration via an interactive dashboard, allowing for a better understanding of bloom patterns across various spatial and temporal scales. Ultimately, bloomWatch transforms individual observations into a robust, community-driven monitoring network that enhances the management and predictability of harmful algal blooms