Predicting and preventing algal blooms

Water managers may soon check two forecasts every morning: one for the weather, and another, a "bloom forecast", for algae that warns you about whether the river near you is about to turn green with algae. For communities in southwest Florida, this isn’t science fiction. It’s the new reality being shaped by researchers who want to give water managers a one-day head start in fighting harmful algal blooms.

A team of scientists from North Carolina State University, the University of Florida, the University of South Florida, and the Sanibel-Captiva Conservation Foundation has developed a day-ahead statistical model that predicts the risk of algal blooms in the Caloosahatchee River and Estuary, one of Florida’s most troubled waterways. Their study, published in the Journal of Environmental Management, offers a simple but powerful forecasting tool designed for real-world use.

And the best part is that it could help water managers make smarter choices about when and how to release water from Lake Okeechobee, decisions that directly influence whether the river downstream turns into a soup of green algae.

Why Florida’s waters are so vulnerable

To understand why this research matters, let’s back up a little. Florida’s waterways have a long and complicated relationship with human engineering. At the center of it all is Lake Okeechobee, the giant freshwater lake in South Florida. A century ago, the lake naturally overflowed into the Everglades every year, nourishing one of the world’s richest wetlands. But devastating floods in the early 1900s led engineers to build a massive dike around the lake. Today, the U.S. Army Corps of Engineers manages the lake like a reservoir, shuttling water east to the St. Lucie Estuary, west to the Caloosahatchee River, and south toward the Everglades.

The problem is that Lake Okeechobee is overloaded with nutrients, especially nitrogen and phosphorus, thanks to decades of agricultural runoff and urban development. These nutrients fuel explosive algal blooms, some of which involve harmful cyanobacteria that can release toxins dangerous to people, pets, and wildlife.

When managers release water from the lake into the Caloosahatchee River to control flooding or maintain navigation, they can unintentionally send a surge of algae and nutrients downstream, setting the stage for massive blooms in the estuary. Local residents, fishermen, and businesses have seen it firsthand: murky, green water, dead fish, and beach closures.

Algal blooms are tricky to predict. They depend on a mix of factors: how much water is flowing, what nutrients are present, how warm the water is, and even how long water sits still in one place. Scientists often use process-based computer models that simulate these dynamics in detail. But those models are slow and complex, requiring heavy computing power and specialized training.

That makes them ill-suited for day-to-day management decisions. Water managers need fast, simple tools, something closer to a weather forecast than a supercomputer simulation.

That’s where María Menchú-Maldonado and her colleagues stepped in. Their goal wasn’t to build the most intricate model, but rather a straightforward statistical tool that could take yesterday’s water conditions and reliably predict tomorrow’s bloom risk.

The researchers gathered more than 14 years of monitoring data from the U.S. Geological Survey and the South Florida Water Management District. They looked at water flow, nutrient levels, suspended solids, and chlorophyll-a (a pigment used as a proxy for algae biomass).

Then they built two separate decision tree models, a kind of predictive flowchart: lake-dominated conditions (when most water entering the estuary comes from Lake Okeechobee) and watershed-dominated conditions (when local runoff from the C-43 canal and surrounding land is the main contributor).

By splitting the problem this way, the models could better reflect the very different dynamics of lake water versus watershed runoff.

They found that lake-driven blooms are easier to predict. The model explained about 78% of the variation in bloom risk when the water source was Lake Okeechobee. The key predictors? Levels of suspended solids and nutrient patterns in the water released from the lake. Instead, watershed-driven blooms are trickier. When runoff was the main driver, the model captured about 49% of the variation. Dissolved phosphorus loads and chlorophyll-a levels from the canal played the biggest roles.

In short, blooms caused by lake releases are not only more predictable, but also more controllable, since water managers can adjust how and when water leaves Lake Okeechobee.

For people living near the Caloosahatchee River, this kind of forecast could be a game-changer. Imagine a tool that alerts water managers that tomorrow’s conditions are ripe for a bloom. They could then hold back water from Lake Okeechobee, tweak release schedules, or coordinate with local agencies to prepare for potential impacts.

It’s not just about cleaner water. Algal blooms hurt tourism, real estate, commercial fishing, and public health. In Florida, entire summer economies have been disrupted by "guacamole-thick" water coating estuaries and beaches. Having a day-ahead risk map could help reduce both the ecological and economic fallout.

The authors emphasize that their framework is adaptable and scalable. While they focused on the Caloosahatchee River, the same approach could be applied to other rivers, lakes, and estuaries facing bloom problems.

They also suggest that the model could be expanded beyond algae to forecast other water quality issues, like low oxygen conditions that suffocate fish, or sediment loads that cloud the water.

In a world where climate change and population growth are putting increasing pressure on freshwater systems, tools like this are invaluable. As reservoirs and engineered waterways become more common, so too will the need for fast, actionable forecasting.

Algal blooms may seem like sudden, unstoppable natural disasters, but research like this shows they’re often predictable, and preventable. By harnessing years of data and building simple, decision-friendly models, scientists are giving water managers a new kind of forecast: one that could protect ecosystems, economies, and communities.

If you want to learn more, read the original article titled "Day-ahead statistical forecasting of algal bloom risk to support reservoir release decisions in a highly engineered watershed" on Journal of Environmental Management at http://dx.doi.org/10.1016/j.jenvman.2025.124327.