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131.
Analysis of the contribution of multiple factors to the recent decrease in discharge and sediment yield in the Yellow River Basin,China 总被引：5，自引：1，他引：4
The Yellow River basin is well known for its high sediment yield. However, this sediment yield has clearly decreased since the 1980s, especially after the year 2000. The annual average sediment yield was 1.2 billion tons before 2000, but has significantly decreased to 0.3 billion tons over the last 10 years. Changes in discharge and sediment yield for the Yellow River have attracted the attention of both the Central Government and local communities. This study aimed to identify the individual contributions of changes in precipitation and human activities (e.g. water conservancy projects, terracing, silt dams, socio-economic and needs, and soil and water conservation measures) to the decrease in discharge and sediment yield of the Yellow River. The study used both improved the hydrological method and the soil and water conservation method. The study focused on discharge analysis for the upper reaches and the investigation of sediments for the middle reaches of the river. The results showed that discharge and sediment yield have both presented significant decreasing trends over the past 50 years. Precipitation showed an insignificant decreasing trend over the same period. The annual average discharge decreased by 5.68 billion m3 above Lanzhou reach of the Yellow River from 2000 to 2012; human activities (e.g. socio-economic water use) contributed 43.4% of the total reduction, whereas natural factors (e.g. evaporation from lakes, wetlands and reservoirs) accounted for 56.6%. The decrease in annual discharge and sediment yield of the section between Hekouzhen station and Tongguan station were 12.4 billion m3 and 1.24 billion tons, respectively. Human activities contributed 76.5% and 72.2% of the total reduction in discharge and sediment yield, respectively, and were therefore the dominant factors in the changes in discharge and sediment yield of the Yellow River. 相似文献
132.
Wei-yue Li Chun Liu Yang Hong Xin-hua Zhang Zhan-ming Wan Manabendra Saharia Wei-wei Sun Dong-jing Yao Wen Chen Sheng Chen Xiu-qin Yang Yue Yue 《山地科学学报》2016,13(7):1275-1285
Landslide inventory plays an important role in recording landslide events and showing their temporal-spatial distribution. This paper describes the development, visualization, and analysis of a China's Landslide Inventory Database (CsLID) by utilizing Google’s public cloud computing platform. Firstly, CsLID (Landslide Inventory Database) compiles a total of 1221 historical landslide events spanning the years 1949-2011 from relevant data sources. Secondly, the CsLID is further broken down into six zones for characterizing landslide cause-effect, spatiotemporal distribution, fatalities, and socioeconomic impacts based on the geological environment and terrain. The results show that among all the six zones, zone V, located in Qinba and Southwest Mountainous Area is the most active landslide hotspot with the highest landslide hazard in China. Additionally, the Google public cloud computing platform enables the CsLID to be easily accessible, visually interactive, and with the capability of allowing new data input to dynamically augment the database. This work developed a cyber-landslide inventory and used it to analyze the landslide temporal-spatial distribution in China. 相似文献
133.
The real estate market has long provided an active application area for spatial–temporal modeling and analysis and it is well known that house prices tend to be not only spatially but also temporally correlated. In the spatial dimension, nearby properties tend to have similar values because they share similar characteristics, but house prices tend to vary over space due to differences in these characteristics. In the temporal dimension, current house prices tend to be based on property values from previous years and in the spatial–temporal dimension, the properties on which current prices are based tend to be in close spatial proximity. To date, however, most research on house prices has adopted either a spatial perspective or a temporal one; relatively little effort has been devoted to situations where both spatial and temporal effects coexist. Using ten years of house price data in Fife, Scotland (2003–2012), this research applies a mixed model approach, semiparametric geographically weighted regression (GWR), to explore, model, and analyze the spatiotemporal variations in the relationships between house prices and associated determinants. The study demonstrates that the mixed modeling technique provides better results than standard approaches to predicting house prices by accounting for spatiotemporal relationships at both global and local scales. 相似文献
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