U.S. Geological Survey (USGS)

Tracking Blooms from the Sky

By Kacey Fitzpatrick

Image of a map created with the new app.

Water quality managers can drop location pins in their water bodies of interest and the pins change colors depending on user settings.

With help from partners, EPA is going above and beyond the agency’s traditional methods of monitoring harmful algal blooms in water. EPA has joined NASA, NOAA, and the U.S. Geological Survey (USGS) to use satellite data to monitor algal blooms and develop an early warning indicator system for toxic and nuisance blooms.

Algal blooms have caused extensive problems in lakes worldwide. We saw this in August, 2014 when half a million people living in and around Toledo, Ohio were issued a water advisory alerting them to avoid all contact with Toledo drinking water after a harmful algal bloom of cyanobateria in Lake Erie had produced unsafe levels of the toxin microcystin.

Blooms like these are becoming a more frequent occurrence and are having greater impacts than ever before. The estimated annual cost of U.S. freshwater degraded by harmful algal blooms is $64 million in additional drinking water treatment, loss of recreational water usage, and decline in waterfront real estate values.

The new multi-agency effort will build on previous NASA ocean satellite sensor technologies created to study the global ocean’s microscopic algal communities. EPA researchers will provide the science that links the current and historical satellite data on cyanobacteria algal blooms provided by NASA, NOAA, and USGS to monitor changes in the environment, assess economic impacts, and protect human health.

The first step in the five-year project will be creating a reliable, standard method for identifying cyanobacteria blooms in U.S. freshwater lakes and reservoirs using ocean color satellite data. NOAA and NASA have lead the way in using oceanic satellite data for monitoring and forecasting harmful algal blooms and EPA is integrating this data into the decision-making process.

Researchers will also conduct a large-scale investigation of potential causes of harmful algal blooms in U.S. freshwater systems. Blooms in lakes and estuaries result from aquatic plants receiving a combination of excess nutrients, perhaps from river runoff, and other environmental conditions such as temperature and light. Various land uses, such as urbanization or modernized agricultural practices, influence the amount of sediment and nutrients delivered in watersheds, which can influence cyanobacterial growth.

This innovative use of satellite data to monitor and report blooms throughout a region or state will help with management of events and significantly reduce risk to the public. Ultimately, this project will reduce the amount of resources needed to protect human health and the environment.

About the Author: Science writer and student contractor Kacey Fitzpatrick is a frequent contributor to It All Starts with Science.



Editor's Note: The opinions expressed here are those of the author. They do not reflect EPA policy, endorsement, or action.

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SPARROWs, Lakes, and Nutrients?

By Jeff Hollister

Dock extending into a lake with forested background.Based on the title above, you probably think I don’t know what I am talking about. I mean really, what do sparrows, lakes, and nutrients have in common? In this case, a lot. So much so, an inter-agency team of EPA researchers in Narragansett RI, and a colleague from the U.S. Geological Survey (USGS) in New Hampshire have been working together to better understand how these three seemingly disparate concepts can be linked together. Some of the results of this work are outlined in a recent publication in the Open Access journal, PLos One

The sparrow I am referring to isn’t small and feathered, it is a model developed and refined by the USGS. Since the late 1990’s, USGS has been developing SPARROW models which have been widely used to understand and predict the total amount of nutrients (among other materials) that streams are exposed to over the long-term. This is known as “nutrient load.” The models are important because they provide a picture over a very large extent of where nutrients might be relatively high.

However, when it comes to lakes, SPARROW doesn’t directly provide the information we need. For our research on lakes, we need reasonable estimates of the quantity of nutrients in a given volume of water (i.e., nitrogen and phosphorus concentration), not long term nutrient load for the year. This is important, because the higher the nutrient concentrations at any given time, the greater the chance of triggering algal blooms—and more blooms mean a greater probability of toxins released by algae reaching unhealthy levels.

In order to better estimate the nutrient concentrations, we needed to use the SPARROW model for total load, but also account for the differences between load and concentration. Our solution: combining field data, data on lake volume and the SPARROW Model.

In our paper “Estimating Summer Nutrient Concentrations in Northeastern Lakes from SPARROW Load Predictions and Modeled Lake Depth and Volume,” recently published in PLoS One, we describe how we combined modeling information from SPARROW, summertime nutrient concentrations collected during EPA’s 2007 National Lakes Assessment, and estimated lake volume (see this and this for more).

The end result of this effort is better predictions, by an average of 18.7% and 19.0% for nitrogen and phosphorus, respectively.

What is the meaning of this in terms of our environment, and importantly, the potential human health impacts? If we are able to better predict concentrations of nutrients it will hopefully also improve our ability to know where and when we might expect to see harmful algal blooms, specifically harmful cyanobacterial algal blooms. Cyanobacteria have been associated with many human health issues, from gastro-intestinal problems, to skin rash, and even a hypothesized association with Lou Gehrig’s Disease (for example, see this). So, in short, better predictions of nutrients, will, in the long run, improve our understanding of cyanobacteria and hopefully reduce the public’s exposure to a potential threat to health.

About the author: Jeff Hollister, a co-author on the study outlined in this blog post, is a research ecologist with an interest in landscape ecology, Geographic Information Systems (GIS), the statistical language R, and open science. The focus of Jeff’s work is to develop computational and statistics tools to help with the cyanobacteria groups research efforts. Jeff is also an outspoken advocate for open science and open access among his colleagues.

Editor's Note: The opinions expressed here are those of the author. They do not reflect EPA policy, endorsement, or action.

Please share this post. However, please don't change the title or the content. If you do make changes, don't attribute the edited title or content to EPA or the author.