The treatment of rural domestic sewage is essential for the comprehensive improvement of the rural environment. At present, the rate of resource utilization of rural domestic sewage is generally low in China, which fits with the actual situation of rural areas, and low cost is becoming the requirement for the development of rural sewage treatment technologies. Adopting a tailored approach based on local conditions for utilising sewage resources is the best option for rural sewage management. Therefore, it is very important and urgent to explore and evaluate the mode of rural domestic sewage resource utilisation. This paper analyzes the current status of sewage resource utilization in rural China. It researches and explores sewage treatment technology and resource utilization models based on 10 study sites in Yongkang City, Zhejiang Province. At the same time, this article evaluates pollution control effectiveness and environmental emission reduction benefits. The results show that the effluent quality of the treated wastewater by the skid-mounted resource utilization equipment met the reuse requirements and maintained stable water quality. The project can save 251,900 tons of high-quality water resources annually, reducing COD by 78.51 tons, reducing NH₃-N and TP by 5.62 tons and 0.39 tons, respectively, and reducing carbon emissions by more than 134 tons. The project has achieved significant comprehensive benefits in water conservation, pollution reduction, and carbon reduction.

In recent years, flash droughts with a rapid onset and strong intensity have attracted extensive attention due to their impact on agriculture and ecosystems. However, there is still no feasible method for flash drought forecasting and early warning. This paper employs the thresholds of several meteorological variables to identify flash droughts in Zhejiang Province, China, and build a probabilistic flash drought forecasting model through numeric weather forecast (NWF) and the generalized Bayesian model (GBM). The results show that the northern part of Zhejiang Province has the highest risk of flash drought. The NWF is a viable method to provide future information for flash drought forecasting and early warning, but its forecasting accuracy tends to decline with the increase in the lead time and is very limited when the lead time is over 5 days, especially for the precipitation forecast. Due to the low performance of the NWF, the flash drought forecast based on the raw NWF may be unreliable when the lead time is over 5 days. To solve this problem, probabilistic forecasting based on GBM is employed to quantify the uncertainty in the NWF and is tested through an example analysis. In the example analysis, it was found that the probability of a flash drought exceeds 30% from the probabilistic forecasting when the lead time is 12 days, while the deterministic forecasting via the raw NWF cannot identify a flash drought when the lead time is over 5 days. In conclusion, probabilistic forecasting can identify a potential flash drought earlier and can be used to evaluate the risk of a flash drought, which is conducive for the early warning of flash droughts and the development of response measures.

Taking a certain coastal area of Jiangsu province as the research background, this study scientifically predicts the runoff on the medium and long-term time scale according to the changes of various climate factors such as atmospheric circulation, sea surface temperature, and solar activity in the first half of the year. A lag correlation is established between various related climate factors and the monthly runoff process in the research area for the previous 1–6 months. Selecting advantageous factors and constructing a significant factor set. Using the improved BP (Back-Propagation) artificial neural network model and combining it with the sensitivity analysis method, a specific number of 8-factor combinations are selected from the set of significant factors for medium and long-term runoff prediction. After that, the prediction results are compared with the forecasting effects of two multi-factor combination runoff simulation schemes formed by stepwise regression and Spearman rank correlation methods. The study concluded that the multi-factor combination simulation effect formed through sensitivity analysis was the best. The 20% standard forecast qualification rate of the three schemes is not significantly different. The Mean Absolute Relative Error of the multi-factor combination training and validation periods simulated through sensitivity analysis is the smallest among the three schemes, which are 36.61% and 38.01%, respectively. The Nash Efficiency Coefficient in the validation period is 0.45, which is far better than other schemes and has better generalization ability. The Standard Deviation of Relative Error in the training and validation periods is much smaller than other schemes, and the dispersion of relative errors is the smallest.

With the rapid development of meteorological models, numerical weather prediction is increasingly used in flood forecasting and reservoir regulation, but its forecasting ability is limited by the large amount of uncertainty from meteorological systems. In this paper, a new, hybrid framework is developed to improve numerical precipitation forecasting by combining the multimodel ensemble and probabilistic postprocessing methods. The results show that the multimodel ensemble method used in this paper is an efficient way to reduce prediction errors, especially missing alarm errors. In a comparison of the probabilistic postprocessors based the generalized Bayesian model (GBM) and bivariate probabilistic model (BPM), the GBM shows better performance from the aspects of indicators and is more suitable for real-time applications. Meanwhile, the assessment of probabilistic results shows that the skill of probabilistic precipitation forecasts is related to the quality of their inputs. According to these results, a new hybrid framework is proposed by taking the results from multimodel ensemble as the input of probabilistic postprocessor. Compared to using the raw numerical in GBM, the hybrid framework improves the accuracy, sharpness, reliability, and resolution ability from different lead times by 2–13%, 1–22%, and 0–12% respectively, especially when the lead time is less than 4 d, the improvement can reach 9–13%, 10–22%, and 5–12% respectively. In conclusion, the hybrid two-step framework can provide a more skillful precipitation forecast, which can be useful for flood forecasting and reservoir regulation.

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.