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At present, water resource information management in China is mainly a centralized model, and there exist some problems such as high cost, low efficiency, and data storage insecurity. Blockchain technology provides a good solution which can create an efficient trust mechanism among the links in the process of water resource utilization. It guarantees the security of the data, avoiding the sudden collapse of the central institutions caused by some normal operations of the entire system. Based on a decentralization blockchain, we propose a decentralized water resource information management system for the whole process of “supply-use-consumption-discharge,” which improves the traditional water data storage. Specifically, the monitoring and business data are encrypted by the blockchain and are transmitted using a peer-to-peer network. Moreover, the centralized management mode is changed and part of the management work is dispersed to each node. Thus, decisions and measures can be made and implemented quickly after discovering problems to improve the efficiency of information transmission and management. In addition, two typical blockchain-based application scenarios for water resource management are designed. A blockchain-based approach makes issuing and monitoring water abstraction permits more convenient and obtaining license information more secure and verifiable. A reliable mechanism for tracing water quality ensures the accuracy and reliability of water quality information, enables the detection of locations with inadequate water quality, and clarifies people’s responsibility, thus guaranteeing the water safety of the residents.

The relationship between sea level change and a single climate indicator has been widely discussed. Despite this, few studies focused on the relationship between monthly mean sea level (MMSL) and several key impact factors, including CO2 concentration, sea ice area, and sunspots, on various time scales. In addition, research on the independent relationship between climate factors and sea level on various time scales is lacking, especially when the dependence of climate factors on Niño 3.4 is excluded. Based on this, we use wavelet coherence (WC) and partial wavelet coherence (PWC) to establish a relationship between MMSL and its influencing factors. The WC results show that the influence of climate indices on MMSL has strong regional characteristics. Sunspots affect MMSL on a scale of more than 64 months. The influence of the sea ice area on MMSL in the northern hemisphere is opposite to that in the southern hemisphere. The PWC results show that after removing the influence of Niño 3.4, the significant coherent regions of the Pacific Decadal Oscillation (PDO), Dipole Mode Index (DMI), Atlantic Multidecadal Oscillation (AMO), and Southern Oscillation Index (SOI) decrease to varying degrees on different time scales in different regions, demonstrating the influence of Niño 3.4. Our work emphasizes the independent relationship between MMSL and its influencing factors on various time scales and the use of PWC and WC to describe this relationship. The study has important reference significance for selecting the best predictors of sea level change or climate systems.

The source region of the Yangtze River (SRYR) is located in the hinterland of the Tibetan Plateau (TP). The natural environment is hash, and the hydrological and meteorological stations are less distributed, making the observed data are relatively scarce. In order to overcome the impact of lack of data, the China Meteorological Forcing Dataset (CMFD) was used to correct the meteorological data, to make the data more closer to the real distribution on the SRYR surface. This paper used the Soil and Water Assessment Tool (SWAT) to verify interpolation effect. Since the SRYR is an important water resource protection area, have a great significance to study the hydrological response under future climate change. The Back Propagation (BP) neural network algorithm was used to integrate data extracted from the six Global Climate Models (GCMs), and then the SWAT model was used to predict runoff changes in the future status. The results show that the CMFD data set has a high precision in the SRYR, and can be used for meteorological data correction. After the meteorological data correction, the Nash-Sutcliffe efficiency increased from 0.64 to 0.70. Under the future climate change, the runoff in the SRYR shows a decreasing trend, and the distribution of runoff during the year changes greatly. This reflects the amount of water resources in the SRYR will be decreased, which will brings challenges to water resources management in the SRYR.