Ecological Forecasting

We carried out a qualitative assessment of (1) how useful forecasts would have been for a problematic past season and (2) the relevance of any windows of opportunity (seasons and variables where forecasts are thought to perform well) for management. Overall, water managers were optimistic about the potential for improved decision-making and identified actions that could be taken based on forecasts. However, there was often a mismatch between those variables that could best be predicted and those which would be most useful for management.

In this module, students will generate an ecological forecast for a NEON site and quantify the different sources of uncertainty within their forecast. This module will introduce students to the conceptof uncertainty within an ecological forecast; where uncertainty in a forecast comes from; how uncertainty can be quantified within a forecast; and how uncertainty can be managed.

In this module, students will apply the iterative forecasting cycle to develop an ecological forecast for a NEON site. This module will introduce students to the basic components of an ecological forecast; how a simple forecasting model is constructed; how changes to model inputs affect forecast uncertainty; and how productivity forecasts vary across ecoclimatic regions.

In this module, students will explore real ecological forecast visualizations, identify ways to represent uncertainty, make management decisions using forecast visualizations and learn decision support techniques. Lastly, students will then customize a forecast visualization for a specific forecast user’s decision needs.

Create open-source software for flexible, scalable, robust, and near-real time iterative ecological forecasts in lakes and reservoirs.

Develop stand-alone, modular classroom activities for undergraduate students that use publicly-available, long-term, and high-frequency datasets to explore the core concepts of macrosystems ecology and ecological forecasting while developing quantitative literacy.

Integrate state-of-the-art seasonal climate predictions and water quality simulations in an advanced solution that ensures efficient decision making and adaptation of water resources management to an increasing frequency of climate extreme events.