Auto regressive integrated moving average (ARIMA) models have been widely used to calculate monthly time series data formed by interannual variations of monthly data or inter-monthly variation. However, the influence brought about by inter-monthly variations within each year is often ignored. An improved ARIMA model is developed in this study accounting for both the interannual and inter-monthly variation. In the present approach, clustering analysis is performed first to hydrologic variable time series. The characteristics of each class are then extracted and the correlation between the hydrologic variable quantity to be predicted and characteristic quantities constructed by linear regression analysis. ARIMA models are built for predicting these characteristics of each class and the hydrologic variable monthly values of year of interest are finally predicted using the modeled values of corresponding characteristics from ARIMA model and the linear regression model. A case study is conducted to predict the monthly precipitation at the Lanzhou precipitation station in Lanzhou, China, using the model, and the results show that the accuracy of the improved model is significantly higher than the seasonal model, with the mean residual achieving 9.41 mm and the forecast accuracy increasing by 21%.

The security of food-energy-water (FEW) systems is an issue of global concern, especially in mega-urban regions (MURs) with high-density populations, industries and carbon emissions. To better understand the hidden links between urbanization and FEW systems, the pressure on FEW systems was quantified in a typical rapidly urbanizing region—the Bohai MUR. The correlations between urbanization indicators and the pressure on FEW systems were analyzed and the mechanism of the impact of urbanization on FEW systems was further investigated. The results showed that approximately 23% of cropland was lost, 61% of which was lost via conversion to construction land and urban areas expanded by 132.2% in the Bohai MUR during 1980–2015. The pressure on FEW systems showed an upward trend, with the stress index of the pressure on FEW systems (FEW_SI) ranging from 80.49% to 134.82%. The dominant pressure consisting of that has converted from water system pressure to energy system pressure since 2004. The FEW_SI in the Bohai MUR was enhanced with cropland loss and increases in urbanization indicators. Additionally, land use, populations, incomes, policies and innovation are the main ways that urbanization affects FEW systems in MURs. This study enhances our understanding of the variation in pressure on FEW systems in MURs and the effects of urbanization on FEW systems, which will help stakeholders to enhance the resilience of FEW systems and promote sustainable regional development.