Comparison analysis of sampling methods to estimate regional precipitation based on the Kriging interpolation methods: A case of northwestern China

The accuracy of spatial interpolation of precipitation data is determined by the actual spatial variability of the precipitation, the interpolation method, and the distribution of observatories whose selections are particularly important. In this paper, three spatial sampling programs, including spatial random sampling, spatial stratified sampling, and spatial sandwich sampling, are used to analyze the data from meteorological stations of northwestern China. We compared the accuracy of ordinary Kriging interpolation methods on the basis of the sampling results. The error values of the regional annual precipitation interpolation based on spatial sandwich sampling, including ME(0.1513), RMSE(95.91), ASE(101.84), MSE(-0.0036), and RMSSE(1.0397), were optimal under the premise of abundant prior knowledge. The result of spatial stratified sampling was poor, and spatial random sampling was even worse. Spatial sandwich sampling was the best sampling method, which minimized the error of regional precipitation estimation. It had a higher degree of accuracy compared with the other two methods and a wider scope of application.     

Improvement of ecological geographic regionalization based on remote sensing and canonical correspondence analysis: A case study in China

Ecological geographic regions, also called eco-regions, can be used to divide a remotely sensed image, which is helpful for reducing the complexity of land cover types within eco-regions and for improving the classification accuracy of land cover. In this case study in China, we improved a method of ecological geographic regionalization that is more suitable for remote sensing mapping of regional land cover, and we obtained new eco-regions. The canonical correspondence analysis (CCA) and k-means clustering were adopted in the ecological geographic regionalization using both seasonal remotely-sensed vegetation information and environmental data including climate, elevation and soil features. Our results show that the combination of seasonal vegetation information and the CCA performed well in the selection of the dominant environmental factor of the biogeographic pattern, and it can be used as regionalization indicators of eco-regions. We found that thermal factors are the most important driving forces of the biogeographic pattern in China, which followed by moisture factors. Two global land cover products (MODIS MCD12C1 and GlobCover) were used to assess our eco-regions. The results show that our eco-regions performed better than that of a previous study regarding the complexity of land cover types, such as in the number of types and the proportional area of the major/secondary type. These results indicate that the method of ecological geographic regionalization, which is based on environmental factors associated with seasonal vegetation features, is effective for reducing the regional complexity of land cover.     

热液体系溶解度实验和热力学计算方法在成矿过程模拟中的应用

热液流体对金属元素的迁移、富集及沉淀成矿起着关键作用,定量刻画金属元素在热液成矿过程中的运移是矿床学研究的重点和难点。以热力学理论为基础,通过开展溶解度实验,揭示金属元素在热液流体中的地球化学行为,是研究金属元素迁移、富集沉淀机制行之有效的重要手段。其中,基于钛合金反应釜开展不同物理化学条件下金属元素在热液流体中的溶解度实验,结合相关的热力学模拟,能够精确测定金属元素在热液流体中的溶解度、络合物配位数、气液两相中的分配系数及相关热力学参数,为定量模拟热液成矿过程提供重要的实验依据。文章归纳了近年来针对热液体系的实验矿床学方法,分析了各类实验方法与分析手段的优点与不足。重点介绍钛合金反应釜溶解度实验和热力学模拟计算方法,并结合该方法模拟钪矿床成矿的水岩反应过程,说明该方法在成矿过程模拟中的应用,最后阐述了其存在的问题,并对该领域未来的研究趋势提出了展望,以期推动该方法在战略性关键金属超常富集机制研究中的应用。