利用功率谱直接求埋深的影响因素

功率谱方法在磁数据处理方面的应用比较广泛。采用近似公式直接求出地质体的上下底埋深。在此近似方法中,会出现一些影响结果的因素。主要对地质体的水平尺寸和厚度的影响做了一些研究,并得出相应的结论。     

http://www.sciencedirect.com/science/article/pii/S1674987110000113

<正>Against the current background of global climate change,the study of variations in the soil carbon pool and its controlling factors may aid in the evaluation of soil's role in the mitigation or enhancement of greenhouse gas.This paper studies spatial and temporal variation in the soil carbon pool and their controlling factors in the southern Song-nen Plain in Heilongjiang Province,using soil data collected over two distinct periods by the Multi-purpose Regional Geochemical Survey in 2005—2007, and another soil survey conducted in 1982—1990.The study area is a carbon source of 1479 t/km~2 and in the past 20 years,from the 1980s until 2005.the practical carbon emission from the soil was 0.12 Gt.Temperature,which has been found to be linearly correlated to soil organic carbon,is the dominant climatologic factor controlling soil organic carbon contents.Our study shows that in the relevant area and time period the potential loss of soil organic carbon caused by rising temperatures was 0.10 Gt,the potential soil carbon emission resulting from land-use change was 0.09 Gt,and the combined potential loss of soil carbon(0.19 Gt) caused by warming and land-use change is comparable to that of fossil fuel combustion(0.21 Gt).Due to the time delay in soil carbon pool variation,there is still 0.07 Gt in the potential emission caused by warming and land-use change that will be gradually released in the future.     

Temporal stability of apparent soil electrical conductivity measured by electromagnetic induction techniques

Assessing and managing the spatial variability of hydropedological properties are important in environmental,agricultural,and geological sciences.The spatial variability of soil apparent electrical conductivity（ECa） measured by electromagnetic induction（EMI） techniques has been widely used to infer the spatial variability of hydrological and pedological properties.In this study,temporal stability analysis was conducted for measuring repeatedly soil ECa in an agricultural landscape in 2008.Such temporal stability was statistically compared with the soil moisture,terrain indices（slope,topographic wetness index（TWI）,and profile curvature）,and soil properties（particle size distribution,depth to bedrock,Mn mottle content,and soil type）.Locations with great and temporally unstable soil ECa were also associated with great and unstable soil moisture,respectively.Soil ECa were greater and more unstable in the areas with great TWI（TWI 〉 8）,gentle and concave slope（slope 〈 3%; profile curvature 〉 0.2）.Soil ECa exponentially increased with depth to bedrock,and soil profile silt and Mn mottle contents（R2= 0.57）,quadratically（R2 = 0.47）,and linearly（R 2 = 0.47）,respectively.Soil ECa was greater and more unstable in Gleysol and Nitosol soils,which were distributed in areas with low elevation（〈 380 m）,thick soil solum（〉 3 m）,and fluctuated water table（shallow in winter and spring but deep in summer and fall）.In contrast,Acrisol,Luvisol,and Cambisol soils,which are distributed in the upper slope areas,had lower and more stable soil ECa.Through these observations,we concluded that the temporal stability of soil ECa can be used to interpret the spatial and temporal variability of these hydropedological properties.     

Modeling of spatial distributions of farmland density and its temporal change using geographically weighted regression model

This study used spatial autoregression（SAR） model and geographically weighted regression（GWR） model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 1999 and 2009,and discussed the difference between global and local spatial autocorrelations in terms of spatial heterogeneity and non-stationarity. Results showed that strong spatial positive correlations existed in the spatial distributions of farmland density,its temporal change and the driving factors,and the coefficients of spatial autocorrelations decreased as the spatial lag distance increased. SAR models revealed the global spatial relations between dependent and independent variables,while the GWR model showed the spatially varying fitting degree and local weighting coefficients of driving factors and farmland indices（i.e.,farmland density and temporal change）. The GWR model has smooth process when constructing the farmland spatial model. The coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical locations. The performance indices of GWR model showed that GWR model produced more accurate simulation results than other models at different times,and the improvement precision of GWR model was obvious. The global and local farmland models used in this study showed different characteristics in the spatial distributions of farmland indices at different scales,which may provide the theoretical basis for farmland protection from the influence of different driving factors.     

我国西部1:50000地图新产品设计与表达技术

西部测图工程以"丰富产品种类,拓展服务领域"为设计宗旨,力求地图新产品开发,在传统4D产品的基础上,根据西部地物地貌特征补充了地形图要素内容,开发研制了影像地图、晕渲地形图新产品。本文针对原有地形图在表达西部地物地貌特征方面的不足,介绍了西部测图工程中地图新产品的内容设计与制图表达,包括地形图要素扩充内容与制图表达、影像地图产品内容设计与制图表达、晕渲地形图产品内容设计与制图表达等技术内容。地图新产品设计与表达技术已应用在西部测图中,指导生产了我国西部首批1:50000地形图、影像地图和晕渲地形图产品,丰富了我国基本比例尺地形图产品种类。     

两次副高边缘特大暴雨对比分析

利用常规资料、地面加密自动观测资料、NCEP/NCAR的1°×1°每6h再分析资料及多普勒雷达资料,对2011年6月16日（简称6.16过程）及2008年7月31日（简称7.31过程）发生在粤东南两次副高边缘特大暴雨进行对比分析。结果表明：6.16过程主要是受高空短波槽和偏南风急流共同影响而产生的,较厚的暖云层、深厚的湿层等使该过程降水范围更广;7.31过程主要是受对流中层扰动诱发产生的,为局地性强降水。雷达回波均表现为强的反射率因子,回波发展迅速且移动缓慢;6.16过程回波图上出现有界弱回波区（BWER）等超级单体风暴特征。     

Fusion of high-resolution DEMs derived from COSMO-SkyMed and TerraSAR-X InSAR datasets

Voids caused by shadow, layover, and decorrelation usually occur in digital elevation models (DEMs) of mountainous areas that are derived from interferometric synthetic aperture radar (InSAR) datasets. The presence of voids degrades the quality and usability of the DEMs. Thus, void removal is considered as an integral part of the DEM production using InSAR data. The fusion of multiple DEMs has been widely recognized as a promising way for the void removal. Because the vertical accuracy of multiple DEMs can be different, the selection of optimum weights becomes a key problem in the fusion and is studied in this article. As a showcase, two high-resolution InSAR DEMs near Mt. Qilian in northwest China are created and then merged. The two pairs of InSAR data were acquired by TerraSAR-X from an ascending orbit and COSMO-SkyMed from a descending orbit. A maximum likelihood fusion scheme with the weights optimally determined by the height of ambiguity and the variance of phase noise is adopted to syncretize the two DEMs in our study. The fused DEM has a fine spatial resolution of 10 m and depicts the landform of the study area well. The percentage of void cells in the fused DEM is only 0.13 %, while 6.9 and 5.7 % of the cells in the COSMO-SkyMed DEM and the TerraSAR-X DEM are originally voids. Using the ICESat/GLAS elevation data and the Chinese national DEM of scale 1:50,000 as references, we evaluate vertical accuracy levels of the fused DEM as well as the original InSAR DEMs. The results show that substantial improvements could be achieved by DEM fusion after atmospheric phase screen removal. The quality of fused DEM can even meet the high-resolution terrain information (HRTI) standard.     

中坝隧道地下分水岭演化的数值模拟分析

由于铁路隧道的施工,地下水进入隧道,形成了新的排泄基准面,对区域地下水环境产生较大影响,故对该类问题进行深入研究具有重要的工程实践意义。以中坝岩溶隧道为例,采用Visual modflow对中坝隧道进行三维数值模拟,模拟中坝隧道开挖建设后,地下分水岭的演化过程,以此来探讨分析其对地下水环境的影响。     

基于 BP 人工神经网络算法的临沂市土地利用集约状况探析

通过对2009—2012年度临沂市9县3区统计年鉴和变更调查中11个相关指标数据的搜集,借助 MATLAB7．0软件平台,利用 BP 人工神经网络算法,算出2009—2012年度各县区土地利用集约度,并利用 BP 人工神经网络的预测功能,预测了2013年和2014年度土地利用集约度值,结果表明临沂市平均土地利用集约度水平正逐步从粗放利用发展到中度集约,兰山区土地利用集约度水平一直为高度集约,集约度居全市之首,罗庄区一直为中度集约,稳中有进。预计到2014年底,大部分县区在中度集约或以上,仅有费县、蒙阴2县为粗放利用。。     

From flood control to flood adaptation: a case study on the Lower Green River Valley and the City of Kent in King County,Washington

Despite massive investment in flood control infrastructure (FCI), neither cities nor rivers have been well served—flooding continues to challenge cities around the world, while riverine ecosystems are degraded by FCI. Although new flood hazard management concepts have shifted the focus away from FCI, many cities continue to count on FCI to prevent flood damage. It is assumed that existing built-up areas can only count on FCI, as large-scale retreat is often impossible. However, flood adaptation—retrofitting the built environment to prevent damage during flooding—as an option is often ignored. This paper argues against the continual use of FCI to prevent flood damage by reviewing FCI’s established problems. The paper examines human–river interactions associated with FCI, focusing on the feedback mechanisms in the interactions, with a case study on the Lower Green River (LGR) valley in King County, Washington, USA. An urban ecology research model is employed to organize the case study, where interactions between floodplain urbanization, FCI, flow and sediment changes, flood risk, and riverine ecosystem are explored and two feedback mechanisms—river adjustment and flood risk perception—are explicitly addressed. The resulting complex dynamics, in terms of cross–scale interactions, emergence, nonlinearity, and surprises, are synthesized and limitations of FCI outlined. Flood adaptation is explored as a plausible alternative to flood control to nurture flood resilience. A management scenario of flood adaptation for the City of Kent—the largest municipality in the LGR valley—is developed to discuss the implications of flood adaptation on flood risk and river restoration.