Preprints
https://doi.org/10.5194/gmd-2021-64
https://doi.org/10.5194/gmd-2021-64

Submitted as: model experiment description paper 02 Jun 2021

Submitted as: model experiment description paper | 02 Jun 2021

Review status: this preprint is currently under review for the journal GMD.

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

Manuel Celestino Vilela Teixeira Almeida1, Yurii Shevchuk2, Georgiy Kirillin3, Pedro Matos Soares4, Rita Margarida Antunes de Paula Cardoso4, José Pedro Matos5, Ricardo Moniz Rebelo1, António Pedro Nobre Carmona Rodrigues1, and Pedro Manuel da Hora Santos Coelho1 Manuel Celestino Vilela Teixeira Almeida et al.
  • 1Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Lisboa, 2825 - 516, Portugal
  • 2MX Automotive GmbH, Berlin, 13355, Germany
  • 3Department of Ecohydrology, Leibniz-Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, 12587, Germany
  • 4Instituto Dom Luís (IDL), Faculdade de Ciências, Universidade de Lisboa, Lisboa, 1749 - 016, Portugal
  • 5Stucky SA, Rue du Lac 33, 1020 Renens, Switzerland

Abstract. 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 in- 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 that surface water temperatures in reservoirs differ significantly from those found in lakes, reinforcing 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 both in accuracy and in computational requirements, if applied to reservoirs with long-term observations available. A relationship between mean water retention times and the importance of inflows and outflows is established: reservoirs with the retention time shorter than ~100 days, if simulated without in- and outflow effects, tend to exhibit a statistically significant deviation in the computed surface temperatures regardless of their morphological characteristics.

Manuel Celestino Vilela Teixeira Almeida et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-64', Anonymous Referee #1, 19 Aug 2021
  • RC2: 'Comment on gmd-2021-64', Anonymous Referee #2, 22 Aug 2021

Manuel Celestino Vilela Teixeira Almeida et al.

Manuel Celestino Vilela Teixeira Almeida et al.

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Short summary
The effect of inland waters on the climate is commonly parameterized as a function of surface water temperature that is estimated by 1D models that run coupled with climate models. These models often neglect advection due to inflows. Analyzing the trade-off between the complexity and requirements of different modeling approaches and the accuracy of their results, this study highlights the need to accurately model reservoir dynamics and selects an efficient way of doing so.