Machine learning-based modeling of surface water temperature dynamics in arctic lakes.

Lake surface-water temperature (LSWT) regulates physical and biochemical processes in lakes. Therefore, understanding the LSWT dynamics is important, especially in Arctic zone since the region is experiencing a warming rate that is greater than the Earth's average. However, regular measurements of LSWT in the remote Arctic lakes always face difficulties or cannot be done by satellites accurately due to the cloud cover and their limited spatiotemporal resolution. Here, we used a historically rich data (1960-2023) to develop four machine learning-based algorithms for the daily LSWT modeling in Lake Inari, situated in Arctic zone, using the air-temperature data. Our results showed that both air-temperature (0.030 °C/yr) and LSWT (0.023m °C/yr) were warming with a rate faster than those in the globe. The long-short-term memory model, with the coefficients of determination varied from 0.96 to 0.98, outperformed other algorithms in modeling of the daily LSWT dynamics in Lake Inari, followed by both support vector regression and neural network tools, and random forest model. As the air-temperature data are widely accessible through synoptic stations and remote sensing techniques, our suggested models can be simply adopted for other Arctic lakes, where the local water-temperature data are often lacking or contain large windows of missing data due to harsh atmospheric conditions and equipment failure.