Near-term forecasts of NEON lakes reveal gradients of environmental predictability across the U.S.

Abstract

The National Ecological Observatory Network (NEON)’s standardized monitoring program provides an unprecedented opportunity for comparing the predictability of ecosystems. To harness the power of NEON data for examining environmental predictability, we scaled a near-term, iterative water temperature forecasting system to all six conterminous NEON lakes. We generated 1 to 35-day ahead forecasts using a process-based hydrodynamic model that was updated with observations as they became available. Forecasts were more accurate than a null model up to 35-days ahead among lakes, with an aggregated 1-day ahead RMSE (root-mean square error) of 0.60℃ and 35-days ahead RMSE of 2.17℃. Water temperature forecast accuracy was positively associated with lake depth and water clarity, and negatively associated with catchment size and fetch. Our results suggest that lake characteristics interact with weather to control the predictability of thermal structure. Our work provides some of the first probabilistic forecasts of NEON sites and a framework for examining continental-scale predictability.