In this study, an inland reservoir water quality parameters’ inversion model was developed using a back propagation (BP) neural network to conduct reservoir eutrophication evaluation, according to multi-temporal remote sensing images and field observations. The inversion model based on the BP neural network (the BP inversion model) was applied to a large inland reservoir in Jiangmen city, South China, according to the field observations of five water quality parameters, namely, Chlorophyl-a (Chl-a), Secchi Depth (SD), total phosphorus (TP), total nitrogen (TN), and Permanganate of Chemical Oxygen Demand (CODMn), and twelve periods of Landsat8 satellite remote sensing images. The reservoir eutrophication was evaluated. The accuracy of the BP inversion model for each water parameter was compared with that of the linear inversion model, and the BP inversion models of two parameters (i.e., Chl-a and CODMn) with larger fluctuation range were superior to the two multiple linear inversion models due to the ability of improving the generalization of the BP neural network. The Dashahe Reservoir was basically in the state of mesotrophication and light eutrophication. The area of light eutrophication accounted for larger proportions in spring and autumn, and the reservoir inflow was the main source of nutrient salts.

Enhancing water use efficiency (WUE) is essential for the sustainable and green development of water utilization. The conventional Super Slack-Based Measure (CSSBM) model is commonly employed to measure WUE, however, it is prone to underestimating WUE due its exaggeration of the slack variable. Recognizing the need to deal with problems involving the slack variable without limitation, we propose an improved Super-SBM (ISSBM) model that assigns an upper bound to the slack variables. In addition, the general deprivation index (GDI) of water resource exploitation is then introduced as the output indicator representing the social equality, resulting in a comprehensive set of output indicators related to the economy, society, and ecological environment. The ISSBM and CSSBM models were applied to determine the WUE in Guangdong province, China from 2009 to 2018, and the results indicate that the WUE calculated via CSSBM exhibited relatively extreme performance (i.e., the high and low values were greater than 2 and less than 0.1, respectively), while the ISSBM-estimated WUE showed relatively stable performance (i.e., the majority of the city’s WUE was located in the range between 0.5 and 1). The WUE determined from the output indicators involving GDI thus demonstrated stronger discriminating power compared to that without GDI. Furthermore, the spatial pattern of WUE in Guangdong province presents an essentially radial distribution, with high WUE located in Pearl River Delta and low WUE located North, East, and West of Guangdong. These results verify that the proposed ISSBM model can obtain a relatively appropriate WUE and could potentially be applied to other regions.