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Modeling reservoir surface temperatures for regional and global climate models: A multi-model study on the inflow and level variation effects

2022-01-11Artigo científico Acesso aberto
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dc.contributor.authorAlmeida, Manuel C.
dc.contributor.authorShevchuk, Yurii
dc.contributor.authorKirillin, Georgiy
dc.contributor.authorSoares, Pedro M. M.
dc.contributor.authorCardoso, Rita M.
dc.contributor.authorMatos, José P.
dc.contributor.authorRebelo, Ricardo M.
dc.contributor.authorRodrigues, António C.
dc.contributor.authorCoelho, Pedro S.
dc.contributor.institutionDCEA - Departamento de Ciências e Engenharia do Ambiente
dc.contributor.institutionMARE - Centro de Ciências do Mar e do Ambiente
dc.contributor.pblCopernicus Publications
dc.date.accessioned2022-03-10T23:24:37Z
dc.date.available2022-03-10T23:24:37Z
dc.date.issued2022-01-11
dc.descriptionUIDB/04292/2020 KI-853/13 KI-853- 16 UIDB/04292/2020
dc.description.abstractThe complexity of the state-of-the-art climate models requires high computational resources and imposes rather simplified parameterization of inland waters. The effect of lakes and reservoirs on the local and regional climate is commonly parameterized in regional or global climate modeling as a function of surface water temperature estimated by atmosphere-coupled one-dimensional lake models. The latter typically neglect one of the major transport mechanisms specific to artificial reservoirs: heat and mass advection due to inflows and outflows. Incorporation of these essentially two-dimensional processes into lake parameterizations requires a trade-off between computational efficiency and physical soundness, which is addressed in this study. We evaluated the performance of the two most used lake parameterization schemes and a machine-learning approach on high-resolution historical water temperature records from 24 reservoirs. Simulations were also performed at both variable and constant water level to explore the thermal structure differences between lakes and reservoirs. Our results highlight the need to include anthropogenic inflow and outflow controls in regional and global climate models. Our findings also highlight the efficiency of the machine-learning approach, which may overperform process-based physical models in both accuracy and computational requirements if applied to reservoirs with long-term observations available. Overall, results suggest that the combined use of process-based physical models and machine-learning models will considerably improve the modeling of air-lake heat and moisture fluxes. A relationship between mean water retention times and the importance of inflows and outflows is established: reservoirs with a retention time shorter than ĝ1/4g100gd, if simulated without inflow and outflow effects, tend to exhibit a statistically significant deviation in the computed surface temperatures regardless of their morphological characteristics.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent25
dc.format.extent4107225
dc.identifier.doi10.5194/gmd-15-173-2022
dc.identifier.issn1991-959X
dc.identifier.otherPURE: 42287565
dc.identifier.otherPURE UUID: 0c68fde2-0150-4a29-9f05-348a8b854dda
dc.identifier.otherScopus: 85123009672
dc.identifier.otherWOS: 000743994100001
dc.identifier.otherORCID: /0000-0001-6266-1179/work/109668587
dc.identifier.otherORCID: /0000-0002-7525-3112/work/109669493
dc.identifier.urihttp://hdl.handle.net/10362/134269
dc.identifier.urlhttps://www.scopus.com/pages/publications/85123009672
dc.language.isoeng
dc.peerreviewedyes
dc.subjectModelling and Simulation
dc.subjectGeneral Earth and Planetary Sciences
dc.subjectSDG 13 - Climate Action
dc.titleModeling reservoir surface temperatures for regional and global climate models: A multi-model study on the inflow and level variation effectsen
dc.typejournal article
degois.publication.firstPage173
degois.publication.issue1
degois.publication.lastPage197
degois.publication.titleGeoscientific Model Development
degois.publication.volume15
dspace.entity.typePublication
rcaap.rightsopenAccess

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