Drought is one of the most frequent types of natural disasters in the world, and it has been classified into several different categories. Generally, meteorological drought is considered to be the beginning of a drought disaster, while socio-economic drought is the possible ultimate result. However, controversy remains around the main impact factors in the propagation from meteorological drought to socio-economic drought over the past decades. In this study, a comprehensive investigation of the 2022 drought event in the city of Lishui, China was conducted to build a model for analyzing the main impact factors in the propagation from meteorological drought to socio-economic drought. The results showed that the 2022 drought event had a great impact on the city’s socio-economic activities. According to governmental reports on socio-economic drought and basic information on water sources, a random forest attribution analysis model was built. The model demonstrated a great performance in distinguishing whether a socio-economic drought had occurred, with an accuracy of 0.9935, a true positive rate of 0.9489 and a false positive rate of 0.0021. Additionally, the variables related to water sources—including drainage area, covered population and daily water supply volume—were found to be more important than the other variables related to meteorological conditions in the model, meaning that the capacity of water sources is the main impact factor in the propagation between meteorological drought and socio-economic drought. In other words, it is feasible to prevent the propagation of meteorological drought to socio-economic drought through water conservancy engineering construction.

Ecological droughts in rivers, as a new type of drought, have been greatly discussed in the past decade. Although various studies have been conducted to identify and evaluate ecological droughts in rivers from different indices, a forecast model for this type of drought is still lacking. In this paper, a numerical weather forecast, a hydrological model, and a generalized Bayesian model are employed to establish a new general framework for the probabilistic forecasting of ecological droughts in rivers, and the Daitou section in China is selected as the study area to examine the performance of the new framework. The results show that the hydrological model can accurately simulate the monthly streamflow with a Nash–Sutcliffe efficiency of 0.91 in the validation period, which means that the model can be used to reconstruct the natural streamflow from the impact of an upstream reservoir. Based on a comparison of ecological drought events from the observed and model-simulated streamflow series, the events from the observed series have a larger deficit volume and a longer duration of ecological droughts after 2014, indicating that human activities may lead to a more severe situation of ecological droughts. Furthermore, due to the uncertainty of precipitation forecasts, a probabilistic precipitation forecast is employed for probabilistic ecological drought forecasting. Compared to the deterministic forecast, the probabilistic ecological drought forecast has better performance, with a Brier score decrease of 0.35 to 0.18 and can provide more information about the risk of ecological droughts. In general, the new probabilistic framework developed in this study can serve as a basis for the development of early-warning systems and countermeasures for ecological droughts.