Changes of precipitation extremes over South Korea projected by the 5 RCMs under RCP scenarios

The change of extreme precipitation is assessed with the HadGEM2-AO - 5 Regional Climate Models (RCMs) chain, which is a national downscaling project undertaken cooperatively by several South Korean institutes aimed at producing regional climate change projection with fine resolution (12.5 km) around the Korean Peninsula. The downscaling domain, resolution and lateral boundary conditions are held the same among the 5 RCMs to minimize the uncertainties from model configuration. Climatological changes reveal a statistically significant increase in the mid-21st century (2046- 2070; Fut1) and the late-21st century (2076-2100; Fut2) precipitation properties related to extreme precipitation, such as precipitation intensity and average of upper 5 percentile daily precipitation, with respect to the reference period (1981-2005). Changes depending on the intensity categories also present a clear trend of decreasing light rain and increasing heavy rain. In accordance with these results, the change of 1-in-50 year maximum precipitation intensity over South Korea is estimated by the GEV method. The result suggests that the 50-year return value (RV50) will change from -32.69% to 72.7% and from -31.6% to 96.32% in Fut1 and from -31.97% to 86.25% and from -19.45% to 134.88% in Fut2 under representative concentration pathway (RCP) 4.5 and 8.5 scenarios, respectively, at the 90% confidence level. This study suggests that multi-RCMs can be used to reduce uncertainties and assess the future change of extreme precipitation more reliably. Moreover, future projection of the regional climate change contains uncertainties evoked from not only driving GCM but also RCM. Therefore, multi-GCM and multi-RCM studies are expected to provide more robust projection.     

中国城市网络的空间组织及其复杂性结构特征

全球化、信息化与快速城市化深刻影响了中国的城市体系,多区位企业组织所形成的城市网络正处于日益复杂的空间嬗变过程.基于2010年企业名录的总部—分支机构型关联数据,研究构建了330×330的地级以上城市网络连接关系,并运用复杂网络分析工具来探索中国城市网络的空间组织特征.研究发现:①中国的城市网络联系呈现以“北京—上海—广深—成都”为核心的菱形空间结构,不同等级的网络流强度具有显著的空间异质性,城市网络的空间组织是一个择优性和地理邻近性复杂作用的过程;②中国城市网络正处于一个简单随机向复杂有序结构的转化期,整体大尺度的网络结构还有待形成;③中国城市网络整体表现出明显的小世界网络效应;④中国城市的二值点度网络为明显的异配性连接特征,而加权强度网络连接则一定程度上表现出“富人圈”的现象;⑤中国城市网络的层级性并不明显,城市网络的点度和强度的关系呈非线性增加特征.     

Dynamic Prediction Model of Overburden Settlement in Deep Mining Areas based on Space–Time Relationship

Natural Resources Research - Depletion of shallow mineral resources caused by deep mining has become an inevitable trend, and deep mining can increase safety accidents and geological hazards....     

包兰铁路沙坡头段不同编制结构的枝条阻沙栅栏防护效应风洞模拟

对包兰铁路沙坡头段枝条阻沙栅栏流场结构进行风洞模拟试验。结果表明:气流在经过横向、竖向阻沙栅栏时均出现了明显的流速分区。气流在经过阻沙栅栏时速度会减弱,但是竖向枝条阻沙栅栏的综合阻风防沙效果明显优于横向阻沙栅栏,同时竖向枝条阻沙栅栏制作上具有工序简单、原材料广泛及不受枝条长度限制的特性,因此,在防沙治沙应用中,竖向枝条阻沙栅栏更适合推广。虽然孔隙度是阻沙栅栏设计的重要技术参数,但栅栏结构对风沙流场产生直接影响,是决定着其对风沙活动防护效应高低的关键因子。     

新疆非物质文化遗产的空间分布及其影响因素

基于国家级和自治区级非物质文化遗产（简称非遗）资源空间数据,集成相关自然地理环境和社会经济文化数据,采用最邻近分析、核密度估计和全局空间自相关分析,研究新疆非遗的空间分布规律和集聚特征,采用地理探测器模型深入探究影响新疆非遗空间分布的自然因素、经济因素和社会文化因素。结果表明：新疆非遗空间分布呈现北多南少的特征,主要集中在喀什、伊犁、塔城、巴州和乌鲁木齐等地（市、州）;新疆非遗在空间上具有明显的集聚特征,不同类型非遗的集聚程度也有差异,在空间上形成了乌鲁木齐-吐鲁番-库尔勒为核心的一级组团、伊宁和喀什为核心的二级组团以及以塔城、哈密和阿克苏为中心的若干三级组团。新疆非遗空间分布受自然因素、经济因素、社会文化因素的综合影响：社会文化因素（0.5494）>经济因素（0.4597）>自然因素（0.2285）。自然因素和经济因素、社会文化因素的协同效应对非遗空间分布的影响更明显。     

岩体中泥化夹层的流变试验及其长期强度的确定

文中首先介绍两种室内试验设备--单剪流变仪和剪应力松弛仪（松弛流变仪）及其试验方法,然后根根岩体中泥化夹层的试验成果,证实陈宗基教授提出的最高屈服值f3--强度的临界值即长期强度值,工程设计要重视它。     

湖北省测绘地理信息档案信息化服务刍议

分析了湖北省测绘地理信息档案的现状,指出进行信息化的必要性,总结前期经验并提出湖北省测绘地理信息档案信息化服务的关键问题,构建了湖北省测绘地理信息档案信息化系统的总体框架.     

A GNSS interference identification using an adaptive cascading IIR notch filter

A low-complexity time-domain approach for global navigation satellite systems is proposed to detect and identify single-tone, multi-tone, swept continuous wave interference (CWI) and band-limited white Gaussian noise (BLWN). An adaptive notch filter and adaptive cascading filter structure are employed to identify the type of interference signals. The number of the cascading stages is selected by comparing the total power in receiver bandpass, and after, passing the adaptive notch filter. For this reason, the proposed filter structure is more efficient in identifying and mitigating interference signals than the conventional filter structure. In addition, the automatic gain control gain is used to detect the existence of continuous wave interference. The performance of the interference detection and identification method is evaluated for the cases of GPS signal in the presence of single-tone, multi-tone, swept CWI and BLWN.     

基于MapReduce的空间数据并行划分算法

针对海量空间数据分布式存储中存在的不顾及空间邻近性、分布不均和数据倾斜的问题,基于MapReduce并行编程模型,对Hilbert空间曲线层次分解的思想和节点容量感知的方法进行了研究,提出了一种层次分解的空间数据并行划分策略,并通过临界值判定实现空间数据的均衡存储。最后通过实例分析说明该方法可以在保证空间数据邻近特性的同时,解决海量空间数据分布式存储不均和数据倾斜的问题。     

Exploring the spatially varying innovation capacity of the US counties in the framework of Griliches’ knowledge production function: a mixed GWR approach

Griliches’ knowledge production function has been increasingly adopted at the regional level where location-specific conditions drive the spatial differences in knowledge creation dynamics. However, the large majority of such studies rely on a traditional regression approach that assumes spatially homogenous marginal effects of knowledge input factors. This paper extends the authors’ previous work (Kang and Dall’erba in Int Reg Sci Rev, 2015. doi: 10.1177/0160017615572888) to investigate the spatial heterogeneity in the marginal effects by using nonparametric local modeling approaches such as geographically weighted regression (GWR) and mixed GWR with two distinct samples of the US Metropolitan Statistical Area (MSA) and non-MSA counties. The results indicate a high degree of spatial heterogeneity in the marginal effects of the knowledge input variables, more specifically for the local and distant spillovers of private knowledge measured across MSA counties. On the other hand, local academic knowledge spillovers are found to display spatially homogenous elasticities in both MSA and non-MSA counties. Our results highlight the strengths and weaknesses of each county’s innovation capacity and suggest policy implications for regional innovation strategies.