In this paper, firstly, in accordance with the principles of the hydrologic cycle simulation, methods commonly used in the runoff yield simulation were analyzed. On this basis, the rainfall-runoff coefficient of correlation, the storage-full runoff and the runoff yield under excess infiltration applied in the runoff simulations, as well as the methods of isochronic hydrograph, unit hydrograph, the Saint-Venant equations, the Muskingum method applied in the flow concentration simulations, and also parametrization methods of topography, land cover, land use and soil type applied in major simulation methods were analyzed and discussed. In addition, the degree of description of the simulation process mechanism by these parametrization methods of watershed topography, land cover, land use and soil types was discussed and the parametrization methods were divided into different categories, namely: the not clearly expressed category, the rating parameters category, the deterministic parameters category and the expressed by physical processes category. Furthermore, the influence of the applied in different parametrization methods topography, land cover, land use and soil types on the hydrologic cycle simulation results was clarified. Finally, returning to the hydrologic models nature, major drawbacks of the simplified description of complex rational and physical mechanisms existing in the underlying surface parametrization methods in hydrologic models were outlined, and also two directions in the future development of those methods in the hydrologic cycle simulations were discussed.

In this paper, the Kriging interpolation method was introduced to analyze the spatial distribution characteristics of PM_2.5 in Jiangsu province in 2014, and then the evaluation index system for the PM_2.5 was constructed, which consists of three index layers and 27 indexes. The grey correlation analysis method was used to explore the correlation between PM_2.5 and its influencing factors. Finally, the relationship between the spatial distribution of PM_2.5 and the main influencing factors was analyzed. The conclusions can be drawn as follows: (1) The PM_2.5 in the coastal areas and the north is lower, while it is higher in the inland areas and the south. (2) The weight of PM_2.5 pollution sources index layer is the largest (w_i = 0.4691), the weight of the air quality index and meteorological elements layer is larger (w_i = 0.2866), and the weight value of urbanization and industrial structure index layer is the minimum (w_i = 0.2453). (3) In the 27 indexes, the volume of highway freight, housing construction area, garden green space area and population density have moderate correlation degrees. The other indexes have strong correlation degrees, among which, the correlation degree of the PM₁₀, O₃, total road freight volume and gross industrial output value are relatively high. (4) The synthetic correlation degree values between the PM_2.5 pollution sources index layer and PM_2.5 are much higher in cities of Nanjing, Wuxi, Changzhou, Nantong and Taizhou. The synthetic correlation degree values between urbanization and industrial structure index layer and PM_2.5 are much higher in cities of Xuzhou, Suzhou, Yancheng and Changzhou. The synthetic correlation degree values between the air quality index and meteorological elements layer and PM_2.5 are much higher in cities of Yancheng, Yangzhou, Changzhou and Nantong. Our results demonstrate that the grey correlation degrees of the evaluation indexes system are closely related with spatial distribution of PM_2.5 in Jiangsu province. Therefore, the grey correlation analysis model can be employed to analyze and evaluate the spatial distribution of PM_2.5.

Global warming and climate change are issues that have aroused widespread attention, and the need for a transition to a low-carbon economy has become the consensus of the international community. China has become one of the world's largest energy consumers, as well as one of the biggest emitters of greenhouse gases. This further highlights the importance and urgency of research on carbon emissions from energy consumption. Based on regional perspectives of the impacts of carbon emissions, the analysis of mechanisms responsible for carbon emissions has become an important research topic. Xinjiang, an important Chinese energy production base, is currently going through a period of strategic opportunities for rapid development. It is critical to ensure stable socioeconomic development as well as to achieve energy savings and meeting emission reductions targets, thus the harmonious development of "society-economy-energy-environment," is the key issue currently facing the region. This study, based on the input-output theory, presents a structural decomposition analysis of the factors affecting energy consumption and carbon emissions in Xinjiang from 1997-2007. This analysis employs a hybrid input-output analysis framework of "energy-economy-carbon emissions," and uses an extended IO-SDA model. The data for this study come from the Xinjiang input-output table for 1997-2002-2007. Population, economic, and energy source data are derived from the Statistical Yearbook of the Xinjiang Uygur Autonomous Region. (1) Xinjiang's carbon emissions from energy consumption increased from 20.70 million tons in 1997 to 40.34 million tons in 2007; carbon emissions growth was mainly concentrated in the production and processing of energy resources, the mining of mineral resources, and the processing industry. (2) The analysis of the direct effects of the influencing factors on carbon emissions shows that the change in per capita GDP, final demand structure, population scale, and production structure were the important factors causing an increase in carbon emissions, while the decrease in carbon emission intensity during this period was an important factor in stopping the growth of carbon emissions. This shows that while Xinjiang's economy and population were growing, the economic structure had not been effectively optimized and production technology had not been improved, which results in a rapid growth of carbon emissions from energy consumption. (3) An analysis of the indirect effects of the factors influencing carbon emissions shows that inter-provincial transfers, gross fixed capital formation, and consumption by urban residents had significant influence on the changes in carbon emissions from energy consumption in Xinjiang. (4) The growth of investments in fixed assets of carbon-intensive industry sectors, as well as the growth of inter-provincial transfers of energy resource products, makes the transfer effect of inter-area "implicit carbon" very significant.