To overcome the difficulty that existing hydrological models cannot accurately simulate hydrological processes with limited information in irrigated paddy areas in southern China, this paper presents a prediction model combining the Ensemble Empirical Mode Decomposition (EEMD) method and the Long Short-Term Memory (LSTM) network. Meteorological factors were set as the multivariate input to the model. Rainfall, regarded as the main variable affecting runoff, was decomposed and reconstructed into a combination of new series with stronger regularity by using the EEMD and K-means algorithm. The LSTM was used to explore the data laws and then to simulate and predict the runoff of the irrigated paddy areas. The Yangshudang (YSD) watershed of the Zhanghe Irrigation System (ZIS) in Hubei Province, China was taken as the study area. Compared with the other models, the results show that the EEMD-LSTM multivariate model had better simulation performance, with an NSE above 0.85. Among them, the R2, NSE, RMSE and RAE of the EEMD-LSTM(3) model were the best, and they were 0.85, 0.86, 1.106 and 0.35, respectively. The prediction accuracy of peak flows was better than other models, as well as the performance of runoff prediction in rainfall and nonrainfall events, while improving the NSE by 0.05, 0.24 and 0.24, respectively, compared with the EEMD-LSTM(1) model. Overall, the EEMD-LSTM multivariations model is suited for simulating and predicting the daily-scale rainfall–runoff process of irrigated paddy areas in southern China. It can provide technical support and help decision making for efficient utilization and management of water resources.
The black-odor phenomenon has been widely reported worldwide and recognized as a global ecological risk for aquatic environments. However, driving factors for black-odor-related microorganisms and potential self-remediation strategies are still poorly understood. This study collected eight water samples (sites A–H) disturbed by different factors from the Jishan River located in Jinmen, Hubei Province, China. Black-odor-related environmental factors and functional bacterial structure were further measured based on the basic physicochemical parameters. The results indicated that different types of disturbed conditions shape the distribution of water quality and microbial community structures. Site B, which was disturbed by dams, had the worst water quality, the lowest abundance of functional microbes for Mn, Fe, and S biotransformation, and the highest abundance of functional microbes for fermentation. The natural wetlands surrounding the terminus of the river (site H) were keys to eliminating the black-odor phenomenon. Potential black-odor-forming microorganisms include Lactococcus, Veillonella, Clostridium sensu stricto, Trichococcus, Rhodoferax, Sulfurospirillum, Desulfobulbus, and Anaeromusa-Anaeroarcus. Potential black-odor-repairing microbes include Acinetobacter, Mycobacterium, and Acidovorax. pH and COD were paramount physiochemical factors contributing to blackening-odor-related microorganisms. This study deepens our understanding of driving factors for black-odor-related microorganisms and provides a theoretical basis for eradicating the black-odor phenomenon.
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