With the water resources stress index taken as an indicator, Mann-Kendall test was used to analyze the trend and abrupt change points of water resources stress in Hubei Province from 2011 to 2020, and the Tapio decoupling model was applied to analyze the spatiotemporal evolution of the decoupling relationship between water resources stress and economic development. The results show that except in 2016 and 2020, the overall water resources stress in Hubei Province was relatively large, showing a spatial trend of maximum in western Hubei, middle in eastern Hubei, and minimum in central Hubei; the overall water resources stress in Hubei Province showed a downward trend, and abrupt change points occurred around 2012 and 2019; the decoupling relationship between water resources stress and economic development in Hubei Province experienced from strong decoupling during 2012-2016 to expansionary negative decoupling during 2017-2019, and then to recessionary decoupling in 2020 in time sequence due to fluctuations in hydrological characteristics; because of the differences in natural climate and development strategies, spatially, the average decoupling rate in Hubei Province experienced a gradual transition from the lowest in western Hubei, the middle in eastern Hubei, and the highest in central Hubei during 2012-2016, to the lowest in eastern Hubei, the middle in central Hubei, and the highest in western Hubei during 2017-2019, and finally tended to be balanced in 2020. Therefore, Hubei Province should pay more attention to formulating water stress response plans based on hydrological fluctuations, and implement industrial structure adjustment in eastern Hubei and central Hubei in the future.

This paper collected relevant research literatures on the smart water conservancy in the databases of China National Knowledge Infrastructure (CNKI) and Web of Science (WOS) from 2000 to 2021. By using the VOSviewer, CiteSpace and other software, this study built various knowledge maps for the time series distribution of literatures in the field of smart water conservancy, publishing institutions, and evolution of research hotspots, to analyze the current progress of smart water conservancy research. The results show that the literature amount of smart water conservancy is increasing year by year, but there is a significant gap between the CNKI database and the WOS database, and core research institutions have been formed in the field of smart water conservancy making important contributions to the frontier development. The CNKI database focuses on the construction of digital watershed and smart water conservancy framework by basin as a unit, while the WOS database focuses on researches from the perspective of geography and earth. Both of them build the platforms for smart water conservancy based on the Internet of Things and deep learning.

To mitigate the impacts of renewable energy accommodation on the river flow regime and ecological integrity in scheduling of hydropower stations on the upper Yellow River, a multi-energy complementary system ecological scheduling model was established with the objectives of minimizing variations in river flow regime, maximizing power generation of hydropower stations, and ensuring the highest stability of generation output. A multi-objective evolutionary algorithm was employed to explore the balance among the generation output of the multi-energy complementary system, renewable energy accommodation, and ecosystem protection of rivers. The results indicate a clear competitive relationship between power generation of hydropower stations and river ecosystem protection objectives. The larger the fluctuation in generation output of the multi-energy complementary system, the greater the power generation of hydropower stations and the larger the variation in river flow regime. Moreover, the larger the inflow volume, the smaller the impacts of ecological scheduling of hydropower stations on river flow regime, and the impacts are mainly concentrated on the indicators, including the numbers of annual occurrences of high/low flow pulses and the average increase/decrease rate of flow. In the scheduling results with maximum power generation in dry years, the indicators of the number of annual occurrence of low flow pulses and average decrease rate of flow have the greatest changes.