| In recent years, the water crisis faced to China is more and more serious. Water resources in temporal and spatial distribution are extremely uneven, which requires more attention. Formerly, the study of water resources was concentrated in the following aspects of water resources issues:its assessment, carrying capacity, sustainable utilization and water demand, as well as its use efficiency and so on. However, literature about the concept and model of water resource utilization strength were less. Therefore, this paper tries to put forward the concept of water resource utilization strength and the strength calculation model. Through the model, the utilize intensity of water resources was calculated, and the temporal and spatial differentiation characteristics was analyzed by using the combination of quantitative and qualitative methods.In this paper, ArcGIS10.2, Excel, SPSS, OpenGeoda software were used to deal with data, and the spatial auto-correlation analysis, regionalized variables, and the variation coefficient methods were put to analysis. Firstly, the intensity of water resource utilization was quantified through statistical data and model analysis. Secondly, the level basin in China was visualized and the temporal and spatial distribution characteristics as well as reasons were analyzed based on Arcgis10.2 platform. Finally, based on the 50km×50km unit grid, the spatial distribution differentiation of water resources utilization intensity was analyzed qualitatively by using the Arcgis10.2 statistical analysis module and OpenGeoda spatial auto-correlation function.The main results of this study were as follows:(1) From the water resources utilization intensity of river basin, the national water resource utilization strength increased slightly, on the whole belonged to level Ⅱ. The difference of water resources utilization in river basin regional was significant. The overall distribution characteristics were:the north was more than the south; east was greater than west. Haihe River Basin has the highest strength in the utilization of water resources, belonged to level Ⅴ; Yellow River and Huaihe River Basin belonged to level Ⅳ; The intensity of Southwest River Basin was the minimum. The average of water resources utilization intensity of Songliao River Basin in Ⅲ level, the Zhujiang, Yangtze river, Southeast and Northwest Rivers Basin were in level Ⅱ.(2) From the point of regionalized variable analysis, the space variation of water resources utilization intensity in China mostly belonged to the medium degree of spatial correlation. Spatial variability was made by structural factors (spatial auto-correlation) and random factors (human factors, etc.). But the variation difference of nugget effect (C0/C0+C) in each year was bigger. This was mainly affected by the differences of regional water resources utilization intensity, which resulted in the different interaction between the two kinds of factors.(3) The global spatial auto-correlation analysis showed that the Moran’s I values overall were above 0.3, which showed positive correlation and spatial agglomeration. That is, the area with high intensity of water resource utilization level adjacent to high values zone; oppositely, low minimum areas were adjacent.The Moran scatter plot and LISA figure showed High-High and Low-Low form in the spatial distribution, that is, a significant spatial dualistic structure in 2003-2012. The distribution of High-High grids were mainly concentrated in the north of our country, such as Yellow Rivers, Huaihe River and Haihe River basin were the most significant. Due to the accordance trend of water resources utilization intensity of the three Basins, the zone showed High-High spatial agglomeration. The distribution of Low-Low grids was mainly concentrated in the south, such as the Southwest rivers Basin, the Zhujiang Basin, the Yangtze River Basin and the Southeast Basin. These areas showed the areas of low value were tending to be similar. The LISA distribution of the Northwest Rivers grids was not significant, that is, the attribute value was not similar or reverse compared with adjacent grids. |
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