Modeling reservoir surface temperatures for regional and global climate models: a multi-model study on the inflow and level variation effects
<p>The 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...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-01-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://gmd.copernicus.org/articles/15/173/2022/gmd-15-173-2022.pdf |
| _version_ | 1798022454809985024 |
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| author | M. C. Almeida Y. Shevchuk G. Kirillin P. M. M. Soares R. M. Cardoso J. P. Matos R. M. Rebelo A. C. Rodrigues P. S. Coelho |
| author_facet | M. C. Almeida Y. Shevchuk G. Kirillin P. M. M. Soares R. M. Cardoso J. P. Matos R. M. Rebelo A. C. Rodrigues P. S. Coelho |
| author_sort | M. C. Almeida |
| collection | DOAJ |
| description | <p>The 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 <span class="inline-formula">∼</span> 100 d, if simulated without inflow and outflow effects, tend to exhibit a
statistically significant deviation in the computed surface temperatures
regardless of their morphological characteristics.</p> |
| first_indexed | 2024-04-11T17:30:24Z |
| format | Article |
| id | doaj.art-aa83bfbbced24655b6c8146eb97927cb |
| institution | Directory Open Access Journal |
| issn | 1991-959X 1991-9603 |
| language | English |
| last_indexed | 2024-04-11T17:30:24Z |
| publishDate | 2022-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Geoscientific Model Development |
| spelling | doaj.art-aa83bfbbced24655b6c8146eb97927cb2022-12-22T04:12:09ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032022-01-011517319710.5194/gmd-15-173-2022Modeling reservoir surface temperatures for regional and global climate models: a multi-model study on the inflow and level variation effectsM. C. Almeida0Y. Shevchuk1G. Kirillin2P. M. M. Soares3R. M. Cardoso4J. P. Matos5R. M. Rebelo6A. C. Rodrigues7P. S. Coelho8Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Lisbon, 2825–516, PortugalMX Automotive GmbH, 13355 Berlin, GermanyDepartment of Ecohydrology, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), 12587 Berlin, GermanyInstituto Dom Luís (IDL), Faculdade de Ciências, Universidade de Lisboa, Lisbon, 1749-016, PortugalInstituto Dom Luís (IDL), Faculdade de Ciências, Universidade de Lisboa, Lisbon, 1749-016, PortugalStucky SA, Rue du Lac 33, 1020 Renens, SwitzerlandFaculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Lisbon, 2825–516, PortugalFaculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Lisbon, 2825–516, PortugalFaculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Lisbon, 2825–516, Portugal<p>The 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 <span class="inline-formula">∼</span> 100 d, if simulated without inflow and outflow effects, tend to exhibit a statistically significant deviation in the computed surface temperatures regardless of their morphological characteristics.</p>https://gmd.copernicus.org/articles/15/173/2022/gmd-15-173-2022.pdf |
| spellingShingle | M. C. Almeida Y. Shevchuk G. Kirillin P. M. M. Soares R. M. Cardoso J. P. Matos R. M. Rebelo A. C. Rodrigues P. S. Coelho Modeling reservoir surface temperatures for regional and global climate models: a multi-model study on the inflow and level variation effects Geoscientific Model Development |
| title | Modeling reservoir surface temperatures for regional and global climate models: a multi-model study on the inflow and level variation effects |
| title_full | Modeling reservoir surface temperatures for regional and global climate models: a multi-model study on the inflow and level variation effects |
| title_fullStr | Modeling reservoir surface temperatures for regional and global climate models: a multi-model study on the inflow and level variation effects |
| title_full_unstemmed | Modeling reservoir surface temperatures for regional and global climate models: a multi-model study on the inflow and level variation effects |
| title_short | Modeling reservoir surface temperatures for regional and global climate models: a multi-model study on the inflow and level variation effects |
| title_sort | modeling reservoir surface temperatures for regional and global climate models a multi model study on the inflow and level variation effects |
| url | https://gmd.copernicus.org/articles/15/173/2022/gmd-15-173-2022.pdf |
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