Legacy phosphorus and ecosystem memory control future water quality in a eutrophic lake

Hanson, Paul C.; Ladwig, Robert; Buelo, Cal D.; Albright, Ellen; Delany, Austin; Carey, Cayelan C.

doi:10.22541/essoar.168677211.11983579/v1

Cited by 2 publications

(2 citation statements)

References 51 publications

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“…First, the three PMs were all 1‐D hydrodynamic models. Examining whether adding more complex PMs (e.g., 3‐D models) or simpler PMs (e.g., Hanson et al., 2023) could help reduce inter‐model covariance is another opportunity for further research. Second, the three PMs all used the same forecasted weather from the NOAA Global Ensemble Forecasting System as driver data.…”

Section: Discussionmentioning

confidence: 99%

A Multi‐Model Ensemble of Baseline and Process‐Based Models Improves the Predictive Skill of Near‐Term Lake Forecasts

Olsson,

Moore,

Carey

et al. 2024

Water Resources Research

Self Cite

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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 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 MME and a five‐model MME that 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.

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Section: Discussionmentioning

confidence: 99%

A Multi‐Model Ensemble of Baseline and Process‐Based Models Improves the Predictive Skill of Near‐Term Lake Forecasts

Olsson,

Moore,

Carey

et al. 2024

Water Resources Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…First, the three process models were all 1-D hydrodynamic models. Examining whether adding more complex process models (e.g., 3-D models) or simpler process models (e.g., Hanson et al, 2023) could help reduce inter-model covariance is another opportunity for further research. Second, the three PMs all used the same forecasted weather from the NOAA Global Ensemble Forecasting System as driver data.…”

Section: The Pm Mme Did Not Significantly Improve On the Best Individ...mentioning

confidence: 99%

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

Olsson

Moore

Carey

et al. 2023

Preprint

View full text Add to dashboard Cite

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.

show abstract

Legacy phosphorus and ecosystem memory control future water quality in a eutrophic lake

Cited by 2 publications

References 51 publications

A Multi‐Model Ensemble of Baseline and Process‐Based Models Improves the Predictive Skill of Near‐Term Lake Forecasts

A Multi‐Model Ensemble of Baseline and Process‐Based Models Improves the Predictive Skill of Near‐Term Lake Forecasts

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

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