Diel temperature patterns unveiled: High-frequency monitoring and deep learning in Lake Kasumigaura

Abstract

The diel variation of water temperatures is crucial information for lakes. This study investigated such variation in Lake Kasumigaura, the second largest lake in Japan, situated 60 km from the Tokyo metropolitan area, utilizing high-frequency monitoring and deep learning techniques. Data from high-frequency monitoring of vertical water temperatures at seven depths were employed. A convolutional neural network (CNN) based deep learning model was developed and assessed across three input scenarios. The impact of forecast horizons ranging from one to 48 h ahead on model performance was examined. Results indicate a degradation in model performance with increasing forecast horizons, irrespective of input scenario or water depth. Notably, the CNN model demonstrates superior performance in near-term and medium-term forecasts compared to long-term predictions, underscoring the need for enhanced efforts in long-term forecasting. Overall, the CNN model effectively reproduces vertical water temperature profiles and captures diel variations in lake water temperatures. Incorporating additional input variables does not necessarily improve model performance; however, using surface water temperatures and air temperatures as inputs produces acceptable results for modeling vertical temperature profiles. These findings have implications for lake management in Lake Kasumigaura (e.g., controlling phytoplankton and zooplankton communities) and offer insights into water temperature modeling for other lakes.

Keywords

• Thermal dynamics
• Diel variation
• High-frequency monitoring
• Deep modeling
• Climate change