How does the calibration method impact the performance of the air2water model for the forecasting of lake surface water temperatures?

Abstract

Abstract The air2water model is a widely used tool for the forecasting of lake water temperatures and projection of climate change. It has been applied in thousands of lakes worldwide. However, it employed the Particle Swarm Optimization (PSO) as the calibration method, which was proposed more than 20 years ago. In this study, 12 advanced optimization algorithms proposed during the recent few years were implemented to calibrate and validate the air2water model. Daily observed water temperature in temperate lakes (22 lowland lakes in Poland) were used to evaluate the model performance. Each optimizer was run 30 times with 3 different settings of the maximum number of function calls, 5,000, 20,000, and 100,000. We have found that the PSO method used in the original air2water model performs relatively poor compared with most of the recent algorithms, and the majority of recently proposed algorithms reach the similar best solutions on many lakes when the highest number of function calls is allowed. However, only the HARD-DE (hierarchical archive-based mutation strategy with depth information of differential evolution) algorithm is never outperformed by any competitor, irrespective which lake and maximum number of function calls is considered. As a result, we highly recommend using HARD-DE as the calibration method for the air2water model in the future studies.