Cayelan Carey
Virginia Polytechnic Institute and State University, VA·Ecology & Evolution
How far into the future can we predict the state of a lake? Climate change and other human activities are making freshwater ecosystems more variable.
- Fields
Ecology & Evolution
Earth & Environmental Science
Computer Science
- Topics
ecological forecasting
freshwater ecology
limnology
- Grants
- 3 known grants, through Dec 2028
What they work on
- Test how far ahead physical, chemical and biological variables in two drinking-water reservoirs can be predicted, by iteratively evaluating real-time forecasts made by competing models against new monitoring data.
- Determine how anoxia, or oxygen loss in the water, changes ecosystem predictability by comparing forecast accuracy between two reservoirs that are alike except for oxygen availability.
- Develop FAIR data-publishing and forecasting workflows with the Environmental Data Initiative, so that reservoir sensor data automatically flow into forecasts and public archives.
- Build an automated system that forecasts phytoplankton blooms 1–35 days ahead, by combining individual-based models of algal growth with real-time statistical methods and cloud and edge computing.
- Study how water managers interpret phytoplankton forecasts and design tools that communicate forecast uncertainty effectively for drinking-water decisions.
- Adapt existing forecasting software, lake ecosystem models and sensor networks to produce daily water-quality and methane forecasts for four climate-stressed lakes in the U.S. and Australia.
More about the research
As a result, past conditions no longer serve as reliable guides to the future. This is a serious problem for drinking-water reservoirs, which face harmful algae (phytoplankton) blooms, oxygen loss and methane emissions. This research builds real-time forecasting systems that predict water quality days to years ahead and estimate how uncertain each prediction is. The forecasts help test basic ideas about how predictable ecosystems are, and they also give water managers practical tools. The three projects share sensors, data pipelines and models, and they extend from local reservoirs to a planned international center.
Why it matters
Water utilities and the communities they serve could use forecasts of algae blooms, water quality and methane emissions to act early. Possible actions include adjusting water treatment, diverting water from other reservoirs or changing water flows, which could lower costs and improve drinking-water safety. The work also aims to help lake managers in the U.S. and Australia adapt to the effects of climate change.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
LTREB: Integrating real-time open data pipelines and forecasting to quantify ecosystem predictability at day to decadal scales
NSF · Jan 2024 – Dec 2028
Collaborative Research: URoL:ASC: Applying rules of life to forecast emergent behavior of phytoplankton and advance water quality management
NSF · Aug 2023 – Jul 2027
Global Centers Track 2: Building the Global Center for Forecasting Freshwater Futures
NSF · Dec 2023 – Nov 2026
Closest labs
Renato Figueiredo
Oregon State University, OR·Computer Science
Build an automated, real-time system that forecasts lake phytoplankton blooms 1 to 35 days ahead with quantified uncertainty, combining ecosystem models, individual-based models and statistical methods.
Nasrin Alamdari
Florida Agricultural and Mechanical University, FL·Earth & Environmental Science
Identify the hydrological, meteorological, biological, physical and geochemical processes that control nutrient export from urban landscapes into freshwater lakes.
Anuj Karpatne
Virginia Polytechnic Institute and State University, VA·Computer Science·Early-career
Develop science-guided curriculum learning and resampling strategies that train deep learning models using both data and scientific equations, so forward predictions, for example in fluid dynamics, stay scientifically consistent.
The school
Virginia Polytechnic Institute and State University: 35 labs on PI Pages, $657M a year on research, 496 research doctorates a year.
More about Virginia Polytechnic Institute and State University
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