森林沼泽景观区中大比例尺化探找矿工作方法的研究--记大梁金矿的发现

大梁金矿是通过森林沼泽景观区开展1：50000和1：25000地球化学测量及综合地质工作而发现的,证明采用的森林沼泽景观中大比例尺化探方法是行之有效的.     

Structuring subjectivities? Using Q methodology in human geography

This paper presents a critical and reflexive account of using Q methodology in human geography. Q methodology has a long pedigree in psychological, political and sociological research, but is only recently beginning to be used by human geographers. We discuss, in particular, the parts of the process(es) of Q methodology that are often glossed over in the literature, through reflecting on our learning in using Q within a project examining the use and production of environmental science by NGOs. We conclude that Q may be a useful supplement to existing methods in human geography, as long as it is used creatively and reflexively and with full awareness of its interpretative dimensions.     

Measurement of stream cross section using ground penetration radar with Hilbert–Huang transform

This study presents a new method to measure stream cross section without having contact with water. Compared with conventional measurement methods which apply instruments such as sounding weight, ground penetration radar (GPR), used in this study, is a non‐contact measurement method. This non‐contact measurement method can reduce the risk to hydrologists when they are conducting measurements, particularly in high flow period. However, the original signals obtained by using GPR are very complex, different from studies in the past where the measured data were mostly interpreted by experts with special skill or knowledge of GPR so that the results obtained were less objective. This study employs Hilbert–Huang transform (HHT) to process GPR signals which are difficult to interpret by hydrologists. HHT is a newly developed signal processing method that can not only process the nonlinear and non‐stationary complex signals, but also maintain the physical significance of the signal itself. Using GPR with HHT, this study establishes a non‐contact stream cross‐section measurement method with the ability to measure stream cross‐sectional areas precisely and quickly. Also, in comparison with the conventional method, no significant difference in results is found to exist between the two methods, but the new method can considerably reduce risk, measurement time, and manpower. It is proven that the non‐contact method combining GPR with HHT is applicable to quickly and accurately measure stream cross section. Copyright © 2013 John Wiley & Sons, Ltd.     

Effect of rainfall uncertainty on the performance of physically based rainfall–runoff models

This paper analyses the effect of rain data uncertainty on the performance of two hydrological models with different spatial structures: a semidistributed and a fully distributed model. The study is performed on a small catchment of 19.6 km² located in the north‐west of Spain, where the arrival of low pressure fronts from the Atlantic Ocean causes highly variable rainfall events. The rainfall fields in this catchment during a series of storm events are estimated using rainfall point measurements. The uncertainty of the estimated fields is quantified using a conditional simulation technique. Discharge and rain data, including the uncertainty of the estimated rainfall fields, are then used to calibrate and validate both hydrological models following the generalized likelihood uncertainty estimation (GLUE) methodology. In the storm events analysed, the two models show similar performance. In all cases, results show that the calibrated distribution of the input parameters narrows when the rain uncertainty is included in the analysis. Otherwise, when rain uncertainty is not considered, the calibration of the input parameters must account for all uncertainty in the rainfall–runoff transformation process. Also, in both models, the uncertainty of the predicted discharges increase in similar magnitude when the uncertainty of rainfall input increase.     

基于地性线分级的DEM信息量计算方法研究

主要对基于DEM提取的地性线进行信息量的定量分析,探讨地性线的信息量与分辨率、地形单元的关系。这个过程中首先利用河网的自相似理论研究与当前DEM所代表的地貌详细程度相适宜的地性线提取的阈值区间,并讨论此阈值下地性线的分级,以此为基础通过以该分级为权重进行DEM地形信息量的计算。实践证明,这种顾及地性线等级的DEM信息量计算方法能较准确地反映地性线的分布规律和不同等级的要素造成的信息量度量差异。     

车载移动测量系统应用于城市大比例尺地形图更新

将车载移动测量系统与城市大比例尺数字地形图测图相结合,成功地将车载移动测量系统运用于城市大比例尺测图更新应用中。通过在测区内布设靶标控制点,对比靶标的点云量算坐标与全站仪实测实际坐标,统计分析车载移动测量系统的测图精度。实验结果表明,车载移动测量系统能够满足城市大比例尺地形图测图要求。     

研习新时期地理信息系统中测量数据的管理方式

地理信息系统是把各种信息用地理以及相关的视图相结合,利用计算机及数据库技术对所需的信息进行收集和分析,经过分析和研究处理过的信息具有很高的使用价值,能够为土地利用、城市规划等各种大型工程提供数据参考。本文在简要阐述地理信息系统中测量数据管理问题的基础上,对新时期地理信息系统中测量数据管理的方式和优缺点及无缝空间数据管理的技术原理和优缺点进行了详细研究和分析。     

一种简化的暴雨灾害风险及影响评估方法和应用研究 ——以京津冀“7·21”暴雨事件为例

文章综合考虑中国区域范围内降雨时空分布特征以及地理地貌等特征,将全国降雨区划分为4大类,在此基础上,得出不同降雨类区暴雨致灾因子的强度等级评定方法;同时,研究确定了与暴雨灾害密切相关的地形高程、高程标准差、河网密度、土壤类型等环境脆弱性影响要素,并对各类要素分别进行了分级评定;将各类环境脆弱性影响要素结合暴雨致灾因子要素,运用加权求和方法建立了暴雨灾害综合风险评估模型;并结合GIS技术,将城市、农村人口分布情况、用地等数据叠加到风险分布格局中,最终分析得出不同风险等级下影响的城市和农村人口数量、土地面积等内容。该评估模型相较于以往其他暴雨风险评估模型,其适用范围更广,可以适用于全国范围内的任意区域暴雨灾害风险评估;实时评估业务能力更强,将该模型结合降雨实况资料或预报资料可以对全国任意区域降雨灾害综合风险进行事后、跟踪评估或预评估;评估对象更有针对性,结合GIS技术,可以针对得出的风险分布结果分别给出不同风险等级范围内的承灾体受影响的定量评估结果。     

国家级格点化定量降水预报系统

利用主客观融合降水反演、降水统计降尺度、降水时间拆分等技术构建了国家级格点化定量降水预报系统。该系统结构合理,模块功能明确,于2014年6月在国家气象中心投入业务使用,生成0~168 h时效,10 km分辨率,逐3 h的格点化定量降水预报产品。通过对2015年第13号热带气旋苏迪罗的格点化降水预报个例检验,结果显示,相比欧洲中期数值预报中心的确定性模式预报和预报员主观预报,该产品能更好地体现台风降水的时空精细化分布特点,对福建东北部和浙江东南部的特大暴雨中心位置表现更准确细致。通过对2015年4—9月的格点化产品整体效果检验,结果显示,相比欧洲中期数值预报中心的确定性模式预报和由反距离客观分析后的预报员主观预报,该产品既能保持和预报员主观预报相同的准确率,同时也能较明显地提高降水预报的时空精细化程度。     

地基探测与NCEP GFS模式预报云量在ARM SGP站点对比

使用大气辐射测量实验(Atmospheric Radiation Measurements:ARM)在美国南部大平原站点(Southern Great Plains:SGP)长时间序列(2001 2010年)的地基主动遥感云(Active Remote Sensing of Clouds:ARSCL)和美国国家环境预报中心(National Centers for Environmental Prediction:NCEP)全球预报系统(Global Forecast System:GFS)模式预报资料,对比分析了两者云量在不同时间尺度内(年际、月份和季节)的差异。结果表明,GFS模式预报总云量为83.8%,略高于地基观测结果(78.1%);两者总云量差异在秋季最大(8.8%),春季最小(2.2%)。在低垂直高度分辨率(≥3 km)时,地基探测低云、中云和高云的云量分别为46.1%、43.5%和61.2%;模式预报三类云的云量均要高于地基探测的云量,差异分别为9.6%、17.2%和9.1%。但是,在高垂直分辨率(250 m)时,地基探测云量在大多数高度层上要高于模式预报结果。这应该是两种资料廓线中有云出现的高度层数目存在差异引起的。地基观测和GFS模式预报同时表明,SGP站点上空云量垂直廓线呈现双峰结构,在边界层附近(1 km)和上对流层区域(8-12 km)云量较大,2-3 km高度范围内云量较小。在春夏秋冬四个季节内,两种资料在低层边界层附近的最大云量偏差分别为9.5%、8.8%、7.8%和11.2%。