The Nanliujiang catchment is one of major rice production bases of South China. Irrigation districts play an important role in rice production which requires a large quantity of water. There are potential risks on future climate change in response to rice production, agricultural irrigation water use and pollution control locally. The SWAT model was used to quantify the yield and water footprint (WF) of rice in this catchment. A combined method of automatic and manual sub-basin delineation was used for the model setup in this work to reflect the differences between irrigation districts in yield and water use of rice. We validated our simulations against observed leaf area index, biomass and yield of rice, evapotranspiration and runoff. The outputs of three GCMs (GFDL-ESM2M, IPSL-CM5A-LR and HadGEM2-ES) under three RCPs (RCP2.6, 4.5, 8.5) were fed to the SWAT model. The results showed that: (a) the SWAT model is an ideal tool to simulate rice development as well as hydrology; (b) there would be increases in rice yield ranged from +1.4 to +10.6% under climate projections of GFDL-ESM2M and IPSL-CM5A-LR but slight decreases ranged from −3.5 to −0.8% under that of HadGEM2-ES; (c) the yield and WFs of rice displayed clear differences in the catchment, with a characteristic that high in the south and low in the north, mainly due to the differences in climatic conditions, soil quality and fertilization amount; (d) there would be a decrease by 45.5% in blue WF with an increase by 88.1% in green WF, which could provide favorable conditions to enlarge irrigated areas and take technical measures for improving green water use efficiency of irrigation districts; (e) a clear rise in future grey WF would present enormous challenges for the protection of water resources and environmental pollution control in this catchment. So it should be to improved nutrient management strategies for the agricultural non-point source pollution control in irrigation districts, especially for the Hongchaojiang and Hepu irrigation districts.

To study the impact of land use/cover change (LUCC), the relationship between precipitation and runoff was investigated. Our main objective was to ensure reasonable development, management, and sustainable utilization of water resources at a watershed scale. To investigate the relationship between precipitation and runoff, a SWAT (Soil and Water Assessment Tool) model was developed by analyzing LUCC in Naoli River basin. Then, runoff response was analyzed under different LUCC conditions. The contribution coefficient of different land use types to runoff was calculated. The results of this research study are as follows. From 1986 to 2014, dry land, forest land, paddy fields, and unused land were the major land use types, accounting for more than 93% of the total catchment. On the other hand, grass land, building land, and water bodies accounted for a small proportion. Among the four main land use types, the contribution coefficient of forest land was 3.10 mm·km⁻². This indicates that forest land was suitable for runoff generation. The contribution coefficient of dry land, unused land (fluvial wetland in Naoli River basin), and paddy field are −0.11, −0.37, and −0.83 mm·km⁻², respectively. This implies that these three land use types were adverse factors for runoff generation.

Although the quantity of household water consumption does not account for a huge proportion of the total water consumption amidst socioeconomic development, there has been a steadily increasing trend due to population growth and improved urbanization standards. As such, mastering the mechanisms of household water demand, scientifically predicting trends of household water demand, and implementing reasonable control measures are key focuses of current urban water management. Based on the categorization and characteristic analysis of household water, this paper used Maslow’s Hierarchy of Needs to establish a level and grade theory of household water demand, whereby household water is classified into three levels (rigid water demand, flexible water demand, and luxury water demand) and three grades (basic water demand, reasonable water demand, and representational water demand). An in-depth analysis was then carried out on the factors that influence the computation of household water demand, whereby equations for different household water categories were established, and computations for different levels of household water were proposed. Finally, observational experiments on household water consumption were designed, and observation and simulation computations were performed on three typical households in order to verify the scientific outcome and rationality of the computation of household water demand. The research findings contribute to the enhancement and development of prediction theories on water demand, and they are of high theoretical and realistic significance in terms of scientifically predicting future household water demand and fine-tuning the management of urban water resources.

We wish to make the following correction to the published paper