Estimation of the mean depth of boreal lakes for use in numerical weather prediction and climate modelling

Abstract : Lakes influence the structure of the atmospheric boundary layer and, consequently, the local weather and local climate. Their influence should be taken into account in the numerical weather prediction (NWP) and climate models through parameterisation. For parameterisation, data on lake characteristics external to the model are also needed. The most important parameter is the lake depth. Global database of lake depth GLDB (Global Lake Database) is developed to parameterise lakes in NWP and climate modelling. The main purpose of the study is to upgrade GLDB by use of indirect estimates of the mean depth for lakes in boreal zone, depending on their geological origin. For this, Tectonic Plates Map, geological, geomorphologic maps and the map of Quaternary deposits were used. Data from maps were processed by an innovative algorithm, resulting in 141 geological regions where lakes were considered to be of kindred origin. To obtain a typical mean lake depth for each of the selected regions, statistics from GLDB were gained and analysed. The main result of the study is a new version of GLDB with estimations of the typical mean lake depth included. Potential users of the product are NWP and climate models.
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M. Choulga, E. Kourzeneva, E. Zakharova, A. Doganovsky. Estimation of the mean depth of boreal lakes for use in numerical weather prediction and climate modelling. Tellus A, Co-Action Publishing, 2014, 66, pp.17. ⟨10.3402/tellusa.v66.21295⟩. ⟨hal-01016589⟩

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