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A multi-model ensemble of empirical and process-based models improves the predictive skill of near-term lake forecasts
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  • Freya Olsson,
  • Tadhg Moore,
  • Cayelan Carey,
  • Adrienne Breef-Pilz,
  • R. Quinn Thomas
Freya Olsson
Virginia Tech

Corresponding Author:[email protected]

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Tadhg Moore
Virginia Tech
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Cayelan Carey
Virginia Tech
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Adrienne Breef-Pilz
Virginia Tech
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R. Quinn Thomas
Virginia Tech
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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 (<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.
27 Jul 2023Submitted to ESS Open Archive
27 Jul 2023Published in ESS Open Archive