Virginia Reservoirs LTREB
Many ecosystems are exhibiting increased variability as a result of human activities. This environmental variability poses substantial challenges for managers and decision-makers, who can no longer use historical baselines to guide predictions of future ecosystem conditions. Consequently, advancing our capacity to predict the future for a range of physical, chemical, and biological ecosystem variables that influence water quality is paramount for improving natural resource management. In response to this need, the Virginia Reservoirs Long-Term Research in Environmental Biology (LTREB) program (supported by the National Science Foundation grant DEB-2327030) leads novel ecosystem monitoring, data publishing, and forecasting at two drinking water supply reservoirs. The ecosystem data that we collect (which includes water temperature, clarity, chemistry, and phytoplankton, among other variables) are used to generate and evaluate real-time forecasts (predictions of future ecosystem conditions, with associated uncertainty) at daily to annual scales as part of the Virginia Ecoforecast Reservoir Analysis (VERA) forecasting challenge.
Forecasting is a powerful approach for quantifying ecological predictability, as it requires using models that represent our best hypotheses about how ecosystems function to predict ecological conditions into the future. Thus, iteratively evaluating the forecasts submitted to the VERA Challenge as new data are collected can reveal which models perform best in different environmental conditions and identify how far into the future different variables can be accurately predicted, from one day to one decade in advance. Altogether, the Virginia Reservoirs LTREB program enables the testing of fundamental hypotheses about the predictability of ecosystems; develops novel workflows for integrating environmental observations into real-time forecasting and data publishing; and broadens the participation of students from underrepresented groups in environmental data science. Moreover, all forecasts are disseminated to water managers in real time, enabling their immediate use as decision-making tools.
The Virginia Reservoirs LTREB program represents one of the first systematic analyses of the predictability of ecosystem dynamics, thereby providing valuable information on the gradients and controls of predictability between contrasting ecosystems and among ecosystem variables. Importantly, our team is able to compare the performance of different forecast models with competing representations of ecosystem dynamics (e.g., varying driver variables, model structures) to test ecological hypotheses about predictability and examine the controls on ecosystem function. In addition, through a collaboration with the Environmental Data Initiative (EDI), the Virginia Reservoirs LTREB program is developing novel FAIR (Findable, Accessible, Interoperable, Reusable) data publishing workflows that advance reproducibility in ecology, support environmental data science education, and enable the scaling of ecological forecasting to other sites.
We invite you to explore our website, which provides access to: 1) reservoir physical, chemical, and biological ecosystem variables available in real-time for automated forecasting; 2) a suite of different forecasting models and evaluated forecasts; 3) forecasting and data-publishing workflows and software; and most critically, 4) substantial ecosystem knowledge gained about the predictability of reservoir dynamics.