Unraveling Interactive Effects of Climate, Hydrology, and CO2 on Ecological Drought with Interpretable Machine Learning

As the risk of drought increases due to climate change, understanding ecological drought has become increasingly important for ensuring water resource security and carbon balance. However, most current ecological drought assessments rely on meteorological or hydrological indicators, which may not accurately reflect changes in the eco-physiological status of ecosystems. Therefore, this study establishes an ecological drought assessment framework using solar-induced chlorophyll fluorescence (SIF) as an indicator to examine its interpretable responses to climate–hydrology–environmental variables. The framework was tested across China’s nine major river basins and different ecosystems. Results show that SIF increased in 80.0% of China’s areas, with 60.9% showing significant increases (p < 0.05). Forest ecosystems experienced the lowest frequency of ecological drought but showed increasing duration and intensity, while grassland ecosystems had the highest frequency but decreasing duration and intensity. LightGBM machine learning analysis revealed that surface soil moisture (SMs), temperature (Tm), root-zone soil moisture (SMrz), and CO2 were the main factors influencing ecological drought, with SMs and Tm contributing to over 66.1% of ecological drought. The SMs-Tm interaction alleviated ecological drought under low-temperature and high-humidity conditions but initially intensified then alleviated ecological drought under high-temperature and high-humidity conditions. The SMs-CO2 interaction promoted ecological drought at high or low CO2 concentrations but alleviated it at moderate concentrations.