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\begin{document}

\title{A multi-model ensemble of empirical and process-based models improves
the predictive skill of near-term lake forecasts}



\author[1]{Freya Olsson}%
\author[1]{Tadhg Moore}%
\author[1]{Cayelan Carey}%
\author[1]{Adrienne Breef-Pilz}%
\author[1]{R. Quinn Thomas}%
\affil[1]{Virginia Tech}%


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  \date{June 13, 2024}


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\selectlanguage{english}
\begin{abstract}
Water temperature forecasting in lakes and reservoirs is a valuable tool
to manage crucial freshwater resources in a changing and more variable
climate, but previous efforts have yet to identify an optimal modelling
approach. Here, we demonstrate the first multi-model ensemble (MME)
reservoir water temperature forecast, a forecasting method that combines
individual model strengths in a single forecasting framework. We
developed two MMEs: a three-model process-based MME and a five-model MME
that includes process-based and empirical models to forecast water
temperature profiles at a temperate drinking water reservoir. Our
results showed that the five-model MME improved forecast performance by
8-30\% relative to individual models and the process-based MME, as
quantified using an aggregated probabilistic skill score. This increase
in performance was due to large improvements in forecast bias in the
five-model MME, despite increases in forecast uncertainty. High
correlation among the process-based models resulted in little
improvement in forecast performance in the process-based MME relative to
the individual process-based models. The utility of MMEs is highlighted
by two results: 1) no individual model performed best at every depth and
horizon (days in the future), and 2) MMEs avoided poor performances by
rarely producing the worst forecast for any single forecasted period
(\textless{}6\% of the worst ranked forecasts over time). This work
presents an example of how existing models can be combined to improve
water temperature forecasting in lakes and reservoirs and discusses the
value of utilising MMEs, rather than individual models, in operational
forecasts.%
\end{abstract}%



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\textbf{Hosted file}

\verb`969370_0_art_file_11213740_ry5q8b.docx`  available at \url{https://authorea.com/users/643754/articles/656917-a-multi-model-ensemble-of-empirical-and-process-based-models-improves-the-predictive-skill-of-near-term-lake-forecasts}

\textbf{Hosted file}

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