China is facing severe pressure on its water resources and water environments. Calculating the industrial water efficiency of each province is an important index for the central government to evaluate local governments. In the traditional water resources evaluation index, the industrial water use efficiency and pollutant discharge are evaluated separately. In this paper, we collected industrial input data, output data and pollutant discharge data with a four-stage data envelopment analysis to calculate China's industrial water use efficiency with and without considering pollutant discharge, and then analyzed the factors influencing the industrial water use efficiency. The results show that the eastern coastal provinces of China have the highest water use efficiency and are less affected by pollutant discharge than other provinces. The industrial water use efficiency of the central and western provinces is lower than that of the other provinces, and the industrial water use efficiency in the central provinces is greatly affected by pollutant discharge. Factor endowment, economic development level, scientific and technological progress, industrial structure, proportion of foreign investment, water consumption per 10000 yuan of value-added by industry, industrial sewage treatment capacity and educational investment have a significant influence on the industrial water use efficiency of China. We suggest that the government strengthen the construction of sewage plants and other related infrastructure in central provinces when conducting the industrial transfer of heavy polluting enterprises.

The efficient control of water usage is one of the core goals of the strictest water resources management system in China. Therefore, the objective and reasonable evaluation of the effects of implementing this system is crucial. Based on the natural and social water cycle theories and the mechanism of the influence of agricultural, industrial, domestic and ecological water utilization, this paper proposes an evaluation index system through the qualitative and quantitative analysis of external and internal factors affecting the efficiency of water usage. Then, a matter-element model is developed on the basis of game theory weight to evaluate the effects of the implementation of efficiency control measures for regional water usage. By calculating the comprehensive correlation, this model can directly indicate the level of regional water use efficiency control. The model is applied to water usage in Jiangxi Province for the period 2011–2014. The results indicate a gradual improvement in the efficiency of water usage in this province. The matter-element extension evaluation model is simple and practical, and the evaluation results are in agreement with the facts. In summary, this method can provide a new theoretical basis for controlling the efficiency of regional water usage.

Under the combined impacts of climate change and human activities, a series of water issues, such as water shortages, have arisen all over the world. According to current studies in Science and Nature, water security has become a frontier critical topic. Water supply security (WSS), which is the state of water resources and their capacity and their capacity to meet the demand of water users by water supply systems, is an important part of water security. Currently, WSS is affected by the amount of water resources, water supply projects, water quality and water management. Water shortages have also led to water supply insecurity. WSS is now evaluated based on the balance of the supply and demand under a single water resources condition without considering the dynamics of the varying conditions of water resources each year. This paper developed an optimal allocation model for water resources that can realize the optimal allocation of regional water resources and comprehensively evaluate WSS. The objective of this model is to minimize the duration of water shortages in the long term, as characterized by the Water Supply Security Index (WSSI), which is the assessment value of WSS, a larger WSSI value indicates better results. In addition, the simulation results of the model can determine the change process and dynamic evolution of the WSS. Quanzhou, a city in China with serious water shortage problems, was selected as a case study. The allocation results of the current year and target year of planning demonstrated that the level of regional comprehensive WSS was significantly influenced by the capacity of water supply projects and the conditions of the natural water resources. The varying conditions of the water resources allocation results in the same year demonstrated that the allocation results and WSSI were significantly affected by reductions in precipitation, decreases in the water yield coefficient, and changes in the underlying surface.

HIGHLIGHTS. Four precipitation products were tested on an Alpine basin.A simulation model combining GO and the CMADS provided the best result.The IMERG can be u

The number of precipitation products at the global scale has increased rapidly, and the accuracy of these products directly affects the accuracy of hydro-meteorological simulation and forecast. Therefore, the applicability of these precipitation products should be comprehensively evaluated to improve their application in hydrometeorology. This paper evaluated the performances of six widely used precipitation products in southwest China by quantitative assessment and contingency assessment. The precipitation products were Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis 3B42 version 7 (TRMM 3B42 V7), Global Satellite Mapping of Precipitation (GSMaP MVK), Integrated Multi-satellitE Retrievals for GPM final run (GPM IMERG Final), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network—Climate Data Record (PERSIANN-CDR), Climate Hazards Infrared Precipitation with Stations version 2.0 (CHIRPS V2.0), and the Global Land Data Assimilation System version 2.0 (GLDAS V2.0). From the above six products, the daily-scale precipitation data from 2001 to 2019 were chosen to compare with the measured data of the rain gauge, and the data from the gauges were classified by river basin and elevation. All precipitation products and measured data were evaluated by statistical indicators. Results showed that (1) GPM IMERG Final and CHIRPS V2.0 performed well in the Yarlung Zangbo River (YZ) basin, while GPM IMERG Final and GLDAS V2.0 performed well in the Lantsang River (LS), Nujiang River (NJ), Yangtze River (YT), and Yellow River (YL) basins; (2) in the upper and middle reaches of the YZ basin, GPM IMERG Final and CHIRPS V2.0 were outstanding in all evaluated products; downstream of the YZ basin, all six products performed well; and upstream of the LS and NJ, GPM IMERG Final, TRMM 3B42 V7, CHIRPS V2.0, and GLDAS V2.0 can be recommended as a substitute for measured data; and (3) GPM IMERG Final and GLDAS V2.0 can be seen as substitutes for measured data when elevation is below 4000 m. GPM IMERG Final and CHIRPS V2.0 were recommended when elevation is above 4000 m. This study provides a reference for data selection of hydro-meteorological simulation and forecast in southwest China and also provides a basis for multi-source data assimilation and fusion.

Reliable simulation of seawater intrusion (SI) is necessary for sustainable groundwater utilization. As a powerful tool, feedforward neural network (FNN) was applied to study seawater intrusion area (SIA) fluctuations in Longkou, China. In the present study, changes of groundwater level (GWL) were modeled by FNN Model 1. Then, FNN Model 2 was developed for fitting the relationship between GWL and SIA. Finally, two models were integrated to simulate SIA changes in response to climatic and artificial factors. The sensitivity analysis of each impact factor was conducted by the “stepwise” method to quantify the relative importance for SIA and GWL. The results from the integrated model indicated that this method could accurately reproduce SIA fluctuations when the Nash–Sutcliffe efficiency coefficient was 0.964, the root mean square error was 1.052 km², the correlation coefficient was 0.983, and the mean absolute error was 0.782 km². The results of sensitivity analysis prove that precipitation and groundwater pumping for agriculture mainly affect fluctuations of SIA in the study area. It can be concluded that FNN is effectively used for modeling SI fluctuations together with GWL, which can provide enough support for the sustainable management of groundwater resources with consideration of crucial impact factors of seawater intrusion (SI).