Résumé
In urban areas, small lakes provide many ecosystem services including biodiversity, landscape composition, cooling islands, recreation, etc. Many have been created during the recent decades as sand-pit lakes. Their origin comes from urbanisation which requires large quantities of sand and gravel for the construction of buildings and infrastructure. Gravel is often extracted from riverbeds, beach deposits or alluvial fans. When gravel extraction stops, the quarries fill up with groundwater and become artificial lakes.The thermal regime and hydrodynamics of these lakes have a strong influence on their ecological functioning and on the fate of contaminants in the water column. In order to better understand their physical behaviour and to which extent it may be affected by climate change, numerical modelling can be very effective.Calibration of the model parameters is a crucial step to obtain reliable modelling results. However, the available field data are generally too scarce to obtain a single set of parameter values. Performing a sensitivity analysis allows to identify the most sensitive parameters that need to be calibrated.The results of the parameter sensitivity analysis and the calibration of a one-dimensional model (GLM, General Lake Model) (Hipsey et al., 2019) are presented. The sensitivity analysis was performed according to a global sensitivity analysis technique, the Morris method (Herman et al., 2013). For the parameter calibration, the CMA-ES method (Covariance Matrix Adaptation - Evolution Strategy), which has been previously used for GLM lake modelling (Ladwig et al., 2021), was applied. The study site is a sand-pit lake located in the Great Paris region. High-frequency water temperature records are available at 4 depths in the water column for the last two years (2023 and 2024).The sensitivity analysis showed that the thermal regime of the lake is particularly sensitive to the values of 4 parameters that are related to the meteorological forcing (sw, a scaling factor to adjust the shortwave radiation data), the light attenuation in the water column (Kw), the latent heat flux transfer coefficient (Ce) and the mean sediment temperature (sed_temp_mean). Calibration of these 4 parameters was then conducted. The simulation obtained with the calibrated parameters was then compared with a reference simulation performed using default values for all parameters.The results highlight the importance of including the sediment temperature to correctly simulate the temperature of the lake bottom layers. The high-frequency monitoring (time step = 15mn) allows to accurately check the efficiency of the calibration method. After the calibration of the 4 parameters identified in the sensitivity analysis, the simulation of the lake temperature is significantly improved, according to different metrics (e.g. RMSE decreasing from 1.8°C to 0.8°C).