Geography student awarded NASA funding to study harmful algal blooms

UNIVERSITY PARK, Pa. — Shuyu Chang, a doctoral candidate in Penn State’s Department of Geography, received a Future Investigators in NASA Earth and Space Science and Technology (FINESST) award to study harmful algal blooms in the Chesapeake Bay watershed.

The watershed extends across six states — New York, Pennsylvania, West Virginia, Delaware, Maryland and Virginia — as well as the District of Columbia. The rivers and streams that feed into the bay flow across farmland, and nutrient-rich agricultural runoff remains a concern. The nitrogen and phosphorous found in fertilizers and manures can enter waterways and cause algal blooms, some of which may contain bacteria that are harmful to animals, pets and people. Understanding what happens in these upstream systems is important for addressing the water quality of the bay itself.

“The Chesapeake Bay watershed is home to more than 1,400 reservoirs,” said Chang. “Scientists have done a lot of work to study harmful algal blooms and nutrient loading, but mainly in the Chesapeake Bay itself. We have less understanding of the freshwater ecosystems, especially when it comes to the biogeochemical processes occurring in these reservoirs.”

The 3-year, nearly $150,000 award will enable Chang and her collaborators to use satellite imagery and sediment core data to study 1,475 reservoirs in the watershed. They aim to develop novel machine-learning models to identify historically harmful algal bloom events and understand nutrient loading in the watershed, as well as to predict future events under a changing climate and water management policies.

Chang’s project is the first to examine all 1,475 reservoirs at once, a large task made even more complicated by reservoir size and location. The reservoirs range in size from approximately 2.5 to 13,000 acres. The size and location of each reservoir affect residence time, or how long nutrients like phosphorous and nitrogen remain in the reservoir, which make harmful algal blooms more likely to occur. Residence time varies in the reservoirs from one day to more than one year, according to Chang, and water management policies vary by state.

To identify potential historical algal hotspots, Chang will use the novel Mixed Density Networks (MDN) machine-learning model. The model currently uses data from the Landsat-8 and Sentinel 2 satellites. The satellites provide information about concentrations of chlorophyll-a, a pigment found in algae, in the reservoirs from 2015 to the present. Chang will further develop the MDN model to incorporate Landsat-7 satellite data, which can provide information back to the year 2000.

Chang and her collaborators will then link the satellite data with sediment core data to identify the past presence of harmful algae like cyanobacteria. Linking the data will help the research team to estimate harmful algal bloom events. The scientists plan to train the model at a global scale so researchers and water quality agencies around the world can use it to identify potential high-risk areas that may require future close monitoring.

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