Development of Novel Detection and Prediction Algorithms for Microsystis Blooms

University Of New Hampshire, Durham NH

Investigators

Abstract

Cyanobacteria-dominated harmful algal blooms (HABs) currently pose a serious health and economic risk to inhabitants and users of the Great Lakes, often producing toxins in sufficient quantity to cause severe health problems to humans and animals. The Great Lakes are a vital national resource, given they are the largest surface freshwater system on earth and provide drinking water for more than 40 million people. Current observational platforms have been focused on the nutrient and phytoplankton ecology measurements. There has been very little effort to characterize the optical variability associated with the HABs in the Great Lakes. In this project, a research team at the University of New Hampshire and the University of Wisconsin - Madison, in collaboration with the NOAA Great Lakes Environmental Research Laboratory, will develop these capabilities with state-of-the-art instrumentation aimed at detecting HAB events as they initiate while capturing the environmental conditions that promote them. There are two main objectives: (1) to develop a new quantitative detection algorithm for HABs in the Great Lakes based on remote sensing imagery by refining and augmenting currently qualitative algorithms; and (2) to develop an ecological niche model to predict the onset of HABs in the Great Lakes with inputs from remotely-sensed data products and state-of-the-art in-situ environmental measurements. One component will consist of implementing a monitoring program consisting of autonomous measurements - combined with ship measurements - of environmental parameters in western Lake Erie and Saginaw Bay critical to HAB initiation and promotion. These measurements will be used in the development of new satellite products that can provide synoptic observation of environmental signatures of these toxic algal blooms. They will combine these new satellite products with other synoptic satellite environmental products and available in-situ environmental measurements to construct an historical data set that characterizes HAB events in the context of associated environmental conditions that cultivate the blooms. This data set will guide the development of an innovative ecological niche model based on multivariate analysis that will predict the spatial and temporal distribution of imminent HAB events from real-time environmental inputs. The overall intellectual merit of the work lies in enhanced observational and detection approaches through remote sensing technology application, and an improved understanding of bloom development leading to better predictive tools. Broader Impacts. This work will add considerable observational information related to HABs for this and future studies in the Great Lakes and other freshwater systems. Secondly, it will lead to increased understanding of the ecology of these blooms that will better assist and inform water quality policy decisions. Our ultimate aim is to develop a critical set of tools for effectively managing human health risks associated with cyanobacterial blooms in water bodies around the globe JOINT FUNDING BY NSF AND NIEHS: The original proposal on which this project is based (R01 ES021929-01) was submitted to the National Institutes of Environmental Health Sciences (NIH/NIEHS) in response to Funding Opportunity Announcement RFA-ES-11-013 , "Oceans, Great Lakes and Human Health (R01)", an opportunity jointly sponsored by NSF. This project is cooperatively funded through separate awards from NSF and NIEHS.