Explainable machine learning reveals climate warming increases risk of algal blooms in lakes and reservoirs

In recent decades, China has exerted significant efforts to diminish the influx of exogenous nitrogen (N) and phosphorus (P) into lakes and reservoirs (L&Rs) in an attempt to control algal blooms (ABs). However, climate change has undermined the effectiveness of these measures. Therefore, distinguishing the contributions of climate change and nutrients is crucial for developing effective ABs management strategies, but remains challenging. Here, we combine the onset and end times of ABs with neural network models to study the trends and controlling factors of algal bloom risk periods (ABRPs) of L&Rs in China from 2003 to 2020. Our results showed that over the past 20 years, the ABRPs of lakes and reservoirs increased by 42 days (11.5%) and 54 days (14.8%), respectively. The contribution of air temperature (T_air) (11%) to prolonging ABRPs was much greater than that of P (5.3%) and N (3.5%). T_air's contribution surpasses that of N and P accounted for up to 63% of one year and will continue to extend with global warming. Moreover, the ABRPs for reservoirs showed a faster growth rate compared to lakes. This may be due to the lower T_air threshold in the reservoir (12.6 °C vs. 16.7 °C in the lake) and the higher contribution of temperature (17% vs. 5% in the lake). Overall, our study highlights the potential importance of climate warming on the onset and end times of ABs, and also distinguishes the differences in driving mechanisms between L&Rs ABs, thus providing a scientific basis for the development of ABs prevention and control strategies for L&Rs in China.