Spatial and temporal distribution of precipitation based on corrected TRMM data around the Hexi Corridor, China

Accurate rainfall distribution is difficult to acquire based on limited meteorological stations, especially in remote areas like high mountains and deserts. The Hexi Corridor and its adjacent regions （including the Qilian Mountains and the Alxa Plateau） are typical districts where there are only 30 available rain gauges. Tropical Rainfall Measuring Mission （TRMM） data provide a possible solution. After precision analysis of monthly 0.25 degree resolution TRMM 3B43 data from 1998 to 2012, we find that the correlations between TRMM 3B43 estimates and rain gauge precipitation are significant overall and in each station around the Hexi Corridor; however, the biases of annual precipitation differ in different stations and are seriously overestimated in most of the sites. Thus, Inverse Distance Weighting （IDW） interpolation method was used to rectify TRMM data based on the difference between TRMM 3B43 estimates and rain gauge observations. The results show that rectified TRMM data present more details than rain gauges in remote areas where there are few stations, alt- hough they show high coherence of distribution. Precipitation decreases from southeast to northwest on an annual and seasonal scale. There are three rainfall centers （〉500 mm） including Menyuan, Qilian and Toson Lake, and two low rain- fall centers （〈50 mm） including Dunhuang and Ejin Banner. Meanwhile, precipitation in most of the study area presents an increasing trend; especially in northern Qilian Mountains （〉5 mm/a）, Badain Jaran Desert （〉2 mm/a）, Toson Lake （〉20 mm/a） and Qingtu Lake （〉20 ram/a） which shows a significant increasing trend, while precipitation in Hala Lake （〈-2 mm/a） and Tengger Desert （〈-3 mm/a） demonstrates a decreasing trend.     

SVLBI用于大地测量的卫星轨道及其跟踪网设计

SVLBI （space very long baseline interferometry） has some important potential applications in geodesy and geodynamics, for which one of the most difficult tasks is to precisely determine the orbit of an SVLBI satellite. This work studies several technologies that will possibly be able to determine the orbit of a space VLBI satellite. Then, according to the types and charac- teristics of the satellite and the requirements for geodetic study and the geometry of the GNSS （GPS, GALILEO） satellite to track the space VLBI satellite, the six Keplerian elements of the SVLBI satellite （TEST-SVLBI） are determined. A program is designed to analyze the coverage area of space of different altitudes by the stations of the network, with which the tracking network of TEST-SVLBI is designed. The efficiency of tracking TEST-SVLBI by the network is studied, and the results are presented.     

Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model

The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature; 2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage; and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation training method has been used for the selection of the five different random training sites in order to calculate the factor’s weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide test locations that were not used during the training phase of the neural network. Our findings of verification results show an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis. The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide areas.     

用基于栅格数据结构的USEMAP软件包制作多因素叠加专题图

以茶叶种植适应性分布图为例，介绍常规滤网制图法和用USEMAP软件包制作多因素叠加专题图的方法和步骤。