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

• Published in: Geoscientific Model Development 2022-01, Vol.15 (1), p.173-197
• Main Authors: Almeida, Manuel C, Shevchuk, Yurii, Kirillin, Georgiy, Soares, Pedro M. M, Cardoso, Rita M, Matos, José P, Rebelo, Ricardo M, Rodrigues, António C, Coelho, Pedro S
• Format: Article
• Language: English
• Subjects: Advection; Analysis; Anthropogenic factors; Aquatic resources; Atmospheric models; Climate; Climate models; Computer applications; General circulation models; Global climate; Global climate models; Heat; Historic temperatures; Inflow; Inland waters; Lake models; Lakes; Learning algorithms; Local climates; Machine learning; Modelling; Morphology; Outflow; Parameterization; Physical characteristics; Precipitation; Regional climates; Reservoirs; Retention; Retention time; Statistical analysis; Surface temperature; Surface water; Temperature; Thermal structure; Turbulence models; Water inflow; Water levels; Water mains; Water temperature
• ISSN: 1991-9603; 1991-959X; 1991-962X; 1991-9603; 1991-962X
• DOI: 10.5194/gmd-15-173-2022

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 ∼ 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.