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 8 publications

(4 citation statements)

References 92 publications

(131 reference statements)

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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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“…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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“…To evaluate the impact of climate change on the lake water environment, numerous climate models have been coupled with one‐dimensional hydrodynamical models at global and local scales (Golub et al., 2022). Previous studies have focused on the projected changes in lake water temperature (Ayala et al., 2020; Shatwell et al., 2019), lake heatwaves (Woolway, Jennings et al., 2021; Woolway et al., 2022), stratification phenology (Woolway, Sharma, et al., 2021), loss of ice cover (Grant et al., 2021; Sharma et al., 2021; Woolway, Denfeld, et al., 2021), alterations in mixing regimes (Råman Vinnå et al., 2021; Woolway & Merchant, 2019), evaporation (La Fuente et al., 2022; Wang et al., 2018; Zhou et al., 2021), lake heat content (Vanderkelen et al., 2020; Weinberger & Vetter, 2014), methane production (Jansen et al., 2022) and water management strategies (Mi et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

Climate Change Impacts on Surface Heat Fluxes in a Deep Monomictic Lake

et al. 2023

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Anthropogenic greenhouse gas emissions have increased since the preindustrial period driven by economics and population growth. Associated impacts have been detected throughout the climate system, and in particular for lake systems, influences of the observed global warming have been detected since the mid-20th century (IPCC AR5, 2014). Lakes are considered as sentinels of climate change (Adrian et al., 2009) and the primary observed physical consequences of climate change on lakes are warming surface water temperature (

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

Cited by 8 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

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

Climate Change Impacts on Surface Heat Fluxes in a Deep Monomictic Lake

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