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\begin{document}

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



\author[1,1]{R. Quinn Thomas}%
\author[1,1]{Ryan McClure}%
\author[1,1]{Tadhg Moore}%
\author[1,1]{Whitney Woelmer}%
\author[2,2]{Carl Boettiger}%
\author[3,3]{Renato Figueiredo}%
\author[4,4]{Robert Hensley}%
\author[1,1]{Cayelan Carey}%
\affil[1]{Virginia Tech}%
\affil[2]{University of California Berkeley}%
\affil[3]{University of Florida}%
\affil[4]{National Ecological Observatory Network}%


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  \date{May 21, 2024}


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\begin{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.%
\end{abstract}%



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