Cyanobacterial blooms prediction in China's large hypereutrophic lakes based on MODIS observations and Bayesian theory

Abstract

Cyanobacterial harmful algal blooms (HABs) pose a significant threat to aquatic ecosystems, water quality, and public health, particularly in large hypereutrophic lakes. Developing accurate short-term prediction models is essential for early warning and effective management of HABs. This study introduces a Bayesian-based model aimed at predicting HABs in three of China's large hypereutrophic lakes: Lake Taihu, Lake Chaohu, and Lake Hulunhu. By integrating MODIS data from the Terra and Aqua satellites with meteorological data spanning from 2010 to 2018, the model forecasts HABs distributions 1, 4, and 7 days in advance. Validation with meteorological data from 2019 to 2020 showed high accuracy, with 0.83 at the pixel level, 0.74 for zonal predictions, and 0.64 for lake-wide HABs area forecasts. Further evaluation using 2023 weather forecast data yielded similar accuracies of 0.78, 0.57, and 0.62, respectively. In addition to predicting the spatial extent of HABs, the model provides binary HABs maps, outbreak areas, and HABs status within lake zones. This method for building prediction models significantly enhances early warning and management capabilities for HABs, providing a scalable framework that can be adapted to other regions facing similar threats from HABs.