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.

Previous research has primarily focused on soil erosion issues in arid and semi-arid regions, with a limited understanding of soil erosion mechanisms in tropical areas. Additionally, there is a lack of a holistic perspective to determine the spatial attribution of soil erosion. The conversion of tropical rainforests into economically driven plantations, like rubber and pulpwood, has resulted in distinct soil erosion characteristics in specific regions. To enhance our knowledge of soil erosion patterns and mechanisms in tropical regions, it is necessary to examine soil erosion in the three major watersheds of Hainan Island from 1991 to 2021, which encompass significant geographical features such as tropical island water sources and tropical rainforest national parks. The study employed the China Soil Loss Equation (CSLE) model, slope trend analysis, Pearson correlation analysis, land-use transfer matrix, and spatial attribution analysis to examine soil erosion under different scenarios. The research results indicate that scenarios driven by the combination of natural and human factors have the greatest impact on soil erosion changes in the entire study area. Co-driven increases affected 53.56% of the area, while co-driven decreases affected 21.74%. The 31-year soil erosion showed an overall increasing trend. Human factors were identified as the primary drivers of increased soil erosion in the Nandu River basin, while a combination of climate and anthropogenic factors influenced the decrease in soil erosion. In the Changhua River basin, climate and human activities contributed to the soil erosion increase, while human activities primarily caused the decrease in soil erosion. In the Wanquan River basin, climate intensified soil erosion, whereas human activities mitigated it. This study underscores the significant combined impact of human activities and natural factors on soil erosion in tropical regions. It emphasizes the importance of considering human-induced factors when implementing soil erosion control measures in tropical regions.

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.

With a service for the most developed economy and dense population in China, the water use of Guangdong province shows distinct regional difference and is subject to multiple driving forces. The regional differences of total water use (TWU) and water use efficiency (WUE) for Guangdong province and its four sub-regions (i.e. Pearl River Dealt region (PRD), Eastern Wing (YD), Western Wing (YX), and Northern Mountain Region (YB)) were quantified by Theil index, and the influence of various variables on WUE was evaluated through multiple linear regression (MLR) models. Overall, Theil index of TWU showed a decreasing trend whereas Theil index of WUE increased in recent decades, suggesting that Guangdong province has experienced an enlarging regional difference of WUE along with a gradually weakened regional difference of TWU. The PRD has the most significant regional differences of WUE and TWU and accounts for a predominated proportion in the total regional difference. Theil indexes of GDP of industry, per capita GDP and per capita value-added by agriculture had positive regression coefficients and were found to have the most significant impact on the regional difference of WUE. This study has the potential to promote a balanced and coordinated regional development in terms of even regional WUE and TWU.