A multi-model ensemble of baseline and process-based models improves the predictive skill of near-term lake forecasts

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dc.contributor.authorOlsson, Freyaen
dc.contributor.authorMoore, Tadhg N.en
dc.contributor.authorCarey, Cayelan C.en
dc.contributor.authorBreef-Pilz, Adrienneen
dc.contributor.authorThomas, R. Quinnen
dc.date.accessioned2025-01-14T20:29:42Zen
dc.date.available2025-01-14T20:29:42Zen
dc.date.issued2024-03en
dc.description.abstractWater 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 modeling 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 MMEand a five‐modelMMEthat includes process‐based and empirical models to forecast water temperature profiles at a temperate drinking water reservoir. We found 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: (a) no individual model performed best at every depth and horizon (days in the future), and (b) 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 utilizing MMEs, rather than individual models, in operational forecasts.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1029/2023WR035901en
dc.identifier.orcidThomas, Robert [0000-0003-1282-7825]en
dc.identifier.urihttps://hdl.handle.net/10919/124187en
dc.identifier.volume60en
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleA multi-model ensemble of baseline and process-based models improves the predictive skill of near-term lake forecastsen
dc.title.serialWater Resources Researchen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
pubs.organisational-groupVirginia Techen
pubs.organisational-groupVirginia Tech/Scienceen
pubs.organisational-groupVirginia Tech/Science/Biological Sciencesen
pubs.organisational-groupVirginia Tech/All T&R Facultyen
pubs.organisational-groupVirginia Tech/Science/COS T&R Facultyen

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