Performance of one-dimensional hydrodynamic lake models during short-term extreme weather events

• Published in: Environmental modelling & software : with environment data news Vol. 133; p. 104852
• Main Authors: Mesman, J.P., Ayala, A.I., Adrian, R., De Eyto, E., Frassl, M.A., Goyette, S., Kasparian, J., Perroud, M., Stelzer, J.A.A., Pierson, D.C., Ibelings, B.W.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01-11-2020; Elsevier Science Ltd
• Subjects: Computational fluid dynamics; Extreme weather; Fluid flow; General lake model; GOTM; Heat waves; Heatwave; Hydrodynamics; Lakes; Model accuracy; Model validation; Simstrat; Storm; Storms; Surface stability; Surface temperature; Weather; GOTM; Storm; Heatwave; General lake model; Model validation; Simstrat
• ISSN: 1364-8152; 1873-6726; 1873-6726
• DOI: 10.1016/j.envsoft.2020.104852

Numerical lake models are useful tools to study hydrodynamics in lakes, and are increasingly applied to extreme weather events. However, little is known about the accuracy of such models during these short-term events. We used high-frequency data from three lakes to test the performance of three one-dimensional (1D) hydrodynamic models (Simstrat, GOTM, GLM) during storms and heatwaves. Models reproduced the overall direction and magnitude of changes during the extreme events, with accurate timing and little bias. Changes in volume-averaged and surface temperatures and Schmidt stability were simulated more accurately than changes in bottom temperature, maximum buoyancy frequency, or mixed layer depth. However, in most cases the model error was higher (30–100%) during extreme events compared to reference periods. As a consequence, while 1D lake models can be used to study effects of extreme weather events, the increased uncertainty in the simulations should be taken into account when interpreting results.
•Three 1D lake models reproduced the overall impacts of storms and heatwaves well.•Timing of effects was simulated accurately and there was little consistent bias.•Uncertainty in simulations increased during extremes compared to reference periods.•Increased uncertainty should be kept in mind when applying models to extreme events.