{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T06:43:33Z","timestamp":1772261013679,"version":"3.50.1"},"reference-count":89,"publisher":"Copernicus GmbH","issue":"1","license":[{"start":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T00:00:00Z","timestamp":1641859200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Geosci. Model Dev."],"abstract":"<jats:p>Abstract. The complexity of the state-of-the-art climate models\nrequires high computational resources and imposes rather simplified\nparameterization of inland waters. The effect of lakes and reservoirs on the\nlocal and regional climate is commonly parameterized in regional or global\nclimate modeling as a function of surface water temperature estimated by\natmosphere-coupled one-dimensional lake models. The latter typically neglect\none of the major transport mechanisms specific to artificial reservoirs:\nheat and mass advection due to inflows and outflows. Incorporation of these\nessentially two-dimensional processes into lake parameterizations requires a\ntrade-off between computational efficiency and physical soundness, which is\naddressed in this study. We evaluated the performance of the two most used\nlake parameterization schemes and a machine-learning approach on\nhigh-resolution historical water temperature records from 24\u00a0reservoirs.\nSimulations were also performed at both variable and constant water level to\nexplore the thermal structure differences between lakes and reservoirs. Our\nresults highlight the need to include anthropogenic inflow and outflow\ncontrols in regional and global climate models. Our findings also highlight\nthe efficiency of the machine-learning approach, which may overperform\nprocess-based physical models in both accuracy and computational\nrequirements if applied to reservoirs with long-term observations\navailable. Overall, results suggest that the combined use of process-based\nphysical models and machine-learning models will considerably improve the\nmodeling of air\u2013lake heat and moisture fluxes. A relationship between mean\nwater retention times and the importance of inflows and outflows is\nestablished: reservoirs with a retention time shorter than \u223c\u2009100\u2009d, if simulated without inflow and outflow effects, tend to exhibit a\nstatistically significant deviation in the computed surface temperatures\nregardless of their morphological characteristics.<\/jats:p>","DOI":"10.5194\/gmd-15-173-2022","type":"journal-article","created":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T04:35:42Z","timestamp":1641875742000},"page":"173-197","source":"Crossref","is-referenced-by-count":8,"title":["Modeling reservoir surface temperatures for regional and global climate models: a multi-model study on the inflow and level variation effects"],"prefix":"10.5194","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1834-3821","authenticated-orcid":false,"given":"Manuel C.","family":"Almeida","sequence":"first","affiliation":[]},{"given":"Yurii","family":"Shevchuk","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7337-3586","authenticated-orcid":false,"given":"Georgiy","family":"Kirillin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9155-5874","authenticated-orcid":false,"given":"Pedro M. M.","family":"Soares","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0259-6827","authenticated-orcid":false,"given":"Rita M.","family":"Cardoso","sequence":"additional","affiliation":[]},{"given":"Jos\u00e9 P.","family":"Matos","sequence":"additional","affiliation":[]},{"given":"Ricardo M.","family":"Rebelo","sequence":"additional","affiliation":[]},{"given":"Ant\u00f3nio C.","family":"Rodrigues","sequence":"additional","affiliation":[]},{"given":"Pedro S.","family":"Coelho","sequence":"additional","affiliation":[]}],"member":"3145","published-online":{"date-parts":[[2022,1,11]]},"reference":[{"key":"ref1","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C.,\nCorrado, G. 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