Multi-model projections of future evaporation in a sub-tropical lake

Fuente, Sofia La; Jennings, Eleanor; Kirillin, Georgiy; Shatwell, Tom; Ladwig, Robert; Moore, Tadhg; Couture, Raoul-Marie; Côté, Marianne; Vinnå, C. Love Råman; Woolway, R. Iestyn

doi:10.1016/j.jhydrol.2022.128729

Cited by 14 publications

(11 citation statements)

References 92 publications

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“…It was found that the weighted multimodel ensemble was superior to the individual models in reproducing the reference evaporation from the CS during the historical period. This agrees with the outcomes of similar climate change studies conducted by La Fuente et al (2022) and Moore et al (2021).…”

Section: Discussionsupporting

confidence: 93%

Probabilistic estimates of future changes in evaporation from the Caspian Sea based on multimodel ensembles of CMIP6 projections

Hoseini,

Zolfaghari,

Soltanpour

2023

Intl Journal of Climatology

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The Caspian Sea level (CSL) is strongly sensitive to climate‐induced changes in its water balance components, especially evaporation from its surface as the main expenditure component of the Caspian Sea's (CS) water budget. Projecting evaporation and determining associated uncertainties obtained from such studies are critical for reliably predicting future CSL fluctuations and developing mitigation and adaptation strategies. This paper studies the projected changes in evaporation from the CS using 18 global climate models (GCMs) from the latest Coupled Model Intercomparison Project phase 6 (CMIP6) and Meyer's semi‐empirical formula. Future evaporation projections are constructed employing a weighted combination of the top‐ranked GCMs, including all ensemble members of selected individual models in a probabilistic framework. This study estimates late 21st century median evaporation of 945, 1016, 1105 and 1173 mm under the low‐emission, medium‐emission, medium‐to‐high emission and high‐emission scenarios, respectively. The weighted multimodel ensemble suggests CS's annual mean evaporation is projected to substantially increase by 3.9%, 13.2%, 20% and 27.9% under SSP1‐2.6, SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5, respectively, for the late 21st century against the reference period. According to the spatial distribution of evaporation, arid eastern regions of CS experience higher evaporation than semi‐arid western and southwestern temperate regions. Additionally, the northwestern continental regions experience the least evaporation over the CS.

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Section: Discussionsupporting

confidence: 93%

Probabilistic estimates of future changes in evaporation from the Caspian Sea based on multimodel ensembles of CMIP6 projections

Hoseini,

Zolfaghari,

Soltanpour

2023

Intl Journal of Climatology

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“…FPVs probably exert a dual influence on evaporation rates. First, they create a shading effect, decreasing water surface temperature and consequently suppressing the vapour pressure gradient at the air-water interface, a key driver of latent heat fluxes and, in turn, evaporation [23][24][25] . Second, FPVs may act as wind barriers, further dampening evaporative losses, as wind speed is positively correlated with evaporation rates 26,27 .…”

Section: Potential For Fpv To Reduce Water Scarcitymentioning

confidence: 99%

Decarbonization potential of floating solar photovoltaics on lakes worldwide

Woolway,

Zhao,

Rocha

et al. 2024

Nat Water

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As climate change progresses, there is increasing emphasis on net zero and energy system decarbonization. Several technologies are contributing to this agenda, but among these, the growth of solar photovoltaics has consistently exceeded all projections. With increasing land-use pressures, and the expense of building-mounted photovoltaics, water surfaces are increasingly being exploited to host these technologies. However, to date, we lack an understanding of the global potential of floating solar photovoltaics and, as such, we do not yet have sufficient insight to inform decisions on (in)appropriate areas for future deployment. Here we quantify the energy generation potential of floating solar photovoltaics on over 1 million water bodies worldwide (14,906 TWh). Our analysis suggests that with a conservative 10% surface area coverage, floating solar photovoltaics could produce sufficient energy to contribute a considerable fraction (16%, on average) of the electricity demand of some countries, thus playing an important role in decarbonizing national economies.

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“…To the best of our knowledge, no one has applied an MME approach to forecasting lake and reservoir temperatures with specified uncertainty. While MMEs for water temperatures have been applied to long-term projections (Almeida et al, 2022;Feldbauer et al, 2022;La Fuente et al, 2022;Wynne et al, 2023), or as model intercomparisons (Golub et al, 2022), the utility of MMEs for real-time water temperature forecasting remains unknown. This gap may exist because ensemble near-term forecasts have, to date, focused on using ensembles of multiple driver data sets (e.g., weather forecasts; Mercado-Bettín et al, 2021) and parameter sets (e.g., Thomas et al, 2020) to partition and quantify uncertainty (Clayer et al, 2023;Thomas et al, 2020), rather than using multiple models to generate more skillful operational forecasts.…”

Section: Introductionmentioning

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

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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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Multi-model projections of future evaporation in a sub-tropical lake

Cited by 14 publications

References 92 publications

Probabilistic estimates of future changes in evaporation from the Caspian Sea based on multimodel ensembles of CMIP6 projections

Probabilistic estimates of future changes in evaporation from the Caspian Sea based on multimodel ensembles of CMIP6 projections

Decarbonization potential of floating solar photovoltaics on lakes worldwide

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

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