Monitoring water quality in streams and rivers helps scientists predict harmful algal blooms in Lake Erie each summer. Recently, a team of researchers led by Dr. Nathan Manning of Heidelberg University, funded by Harmful Algal Bloom Research Initiative (HABRI), took steps to improve this process.
Water quality can be extremely dynamic and difficult to measure, particularly in large river systems that are likely influenced by many pollution types and sources, such as the watersheds of the western basin of Lake Erie.
Thankfully, these watersheds have a uniquely long-running, high-frequency, and dense network of stream and river monitoring equipment and sensors compared to anywhere else around the globe. However, because this monitoring is conducted across multiple agencies, organizations, and laboratories across different time intervals, it can be challenging to collate this information in a useful manner to provide an up-to-date status on tributary water chemistry trends.
To better understand the different sampling regimes deployed by these partners and to show a more comprehensive picture of the state of water quality monitoring in the region, researchers aimed to develop new methods to compare data collected by these different groups.
Using data from the National Center for Water Quality Research and the U.S. Geological Survey, researchers created various sampling scenarios that can be applied broadly to other sampling regimes across the region. The scenarios mimicked common sampling frequencies as well as gaps in the sampling record due to weather or funding issues.
The team compared and tested a number of different load estimator applications that are widely used and discovered that some are better under certain sampling conditions than others. Load estimators are tools that help estimate loads and inputs of various chemicals into streams and rivers.
Results can be used to advise others which estimator tool to use based on their sampling model and suggest the best times to sample to yield the most accurate results. This gives scientists a better understanding of the amount of nutrients being added into the lake and allows sampling to be more efficient and cost effective.
The team developed a new data dashboard that will soon be available online to explore and compare data between and within rivers that contribute to the Lake Erie Western Basin. Researchers are also sharing Maumee River trends with the Ohio Environmental Protection Agency, and they partnered with the Cleveland Water Alliance to implement several sensor networks.
“Providing this data and providing a way for people to visualize this data in a way that is easily understandable is really important, not just for us at the National Center for Water Quality Research, but for everyone that’s doing work across the Great Lakes,” Manning said.
To learn more about the project and the water quality data dashboard, watch Manning’s recent Freshwater Science webinar or contact him at nmanning@heidelberg.edu.
Ohio Sea Grant is supported by The Ohio State University College of Food, Agricultural, and Environmental Sciences (CFAES) School of Environment and Natural Resources, Ohio State University Extension, and NOAA Sea Grant, a network of 34 Sea Grant programs nation-wide dedicated to the protection and sustainable use of marine and Great Lakes resources. Stone Laboratory is Ohio State’s island campus on Lake Erie and is the research, education, and outreach facility of Ohio Sea Grant and part of CFAES School of Environment and Natural Resources.