中国东部雨季降水事件的静默时间特征及内在动力学机制初探

为了挖掘降水的内在非线性动力学机制,基于1960—2017年4—9月的中国日降水数据,利用百分位阈值法确定不同强度的降水事件,定义相邻同强度降水事件之间的间隔时间为“静默时间”,描述同阈值降水事件再次发生的历时长短。“平均静默时间”可以表征不同强度降水事件的群发性特征,即某一时段内,平均静默时间越短（长）,同类型降水事件再次发生的历时越短（长）,其群发性程度越强（弱）。分析不同强度降水事件的平均静默时间在中国东部地区的空间分布和时间演变特征表明: 1960—2017年,一般强度降水事件在长江中游地区较其余地区群发性强,而东北南部地区则较弱; 极端降水事件在中国东部北方地区群发性较强,而在南方地区则偏弱。就气候态演化过程而言,一般强度降水事件的群发性在长江以南地区有所增强,在东北南部地区则呈减弱趋势;极端降水事件的群发性特征在中国东部北方地区不断减弱,但在南方地区持续增强。各站点降水事件静默时间的概率密度函数均呈幂律分布,且时、空上呈无标度特征,表明日降水系统为现实世界中的自组织临界系统,为时间序列分析角度理解降水事件内在非线性动力学机制提供了新的视角。      

天津市短时暴雨雨型时空分异及其对城市内涝的影响

利用天津市1961—2018年分钟级降水数据,统计了天津地区短时暴雨过程,结合模糊识别法对暴雨过程进行分类,分析了暴雨雨型的时空分布特征,基于天津城市暴雨内涝数值模型,评估了不同雨型对内涝积水总量的影响.结果表明:近58 a天津地区短时暴雨雨型以单峰型(Ⅰ型、Ⅱ型和Ⅲ型)为主,其中各年代60 min暴雨雨型以Ⅰ型、Ⅲ型...     

近十年来南四湖水质时空变化特征研究

基于南四湖湖区2006～2016年的水质监测数据,选取COD_cr、TP、TN、和NH_3-N作为水质评价指标,对南四湖各湖区的污染情况及年际变化趋势进行对比分析,并利用综合水质标识指数法,对南四湖水质变化年际趋势进行综合评价。结果表明:2006～2016年间,各监控站点COD_cr、TP、TN和NH_3-N浓度的年平均值总体呈下降趋势,其中NH_3-N的浓度平均值下降率最大,为72%。2016年南阳湖、独山湖、昭阳湖、微山湖的综合水质指标分别为4.096、3.446、3.350、3.128,呈现出北高南低的空间分布特征。2006～2016年间南四湖的综合水质标识指数由4.620下降至3.350,整体提高了1～2个水质类别,水质得到一定改善。由此可见,加强湖泊区域实施"治"、"用"、"保"并举等流域综合治理措施对湖区的治污效果意义重大。     

论时空大数据的智能处理与服务

时空大数据智能处理与服务是以测绘、遥感和地理信息技术为中心的地球空间信息学的重要应用途径和发展契机。本文首先全面论述了时空大数据的兴起发展、主要特点和挖掘手段,进而介绍了时空大数据的自动匹配、变化检测、智能决策等智能处理技术,并在此基础上论述了由对地观测到对人观测的“3S”社会化应用,最后介绍了天基信息的实时智能服务(PNTRC)的现状、发展目标、关键技术及应用前景。诸多实践证明,在大数据和人工智能时代,面对海量的多源异构的时空大数据,抓好自动化、实时化、智能化、大众化和社会化,地球空间信息学的创新发展前景必将一片光明。     

A Spatiotemporal Data Model on Relational Databases for Coastal Dynamic Research

Spatiotemporal information is currently among the most significant data used in Geographical Information Systems (GIS), especially for modeling dynamic coastal areas. As the design of the data model is the most fundamental issue for representing spatiotemporal data, this article focuses on the basic solution of how to represent spatial, attribute, and temporal information in an integrated form. By presenting a data model on relational databases, together with an object-oriented model for system development, it is expected to provide both theoretic and technical support to the spatial and temporal analyses in GISs for various applications.     

基于密集台阵研究龙门山断裂带南段地震空段的地震活动性

2016年12月—2018年4月间布设于汶川、芦山地震之间地震空段的密集监测台阵(LmsSGA)提供了密集的观测数据.通过拾取地震走时、初始定位,计算地方震级,得到了完备性震级为0级的地震目录.更加完备的地震目录为地震空段及周围地震活动的时空分布特征和孕震风险性评估提供了丰富的信息.重定位结果显示地震主要集中于龙门山断裂带深度为5～20km的孕震层内.地震活动频繁的汶川、芦山主震区,震源的空间分布模式与其早期余震相似,说明两次大地震的区域仍处于缓慢的应力调整阶段.青藏高原物质东向挤出受宝兴、彭灌杂岩阻挡,在两个杂岩体西北侧地震活动频繁.地震活动性分布显示汶川—茂县、映秀—北川断裂上存在一个清晰的长约30km,宽约20km的地震活动"空白"区域,与其下方因部分熔融而产生的低速体分布一致,我们推测熔融体的加温作用是导致空段内极低的地震活动性的主要原因.监测时段内仍观测到降雨变化率和地震数量呈反相关关系,再次证实了汶川—芦山地震间地震空段及邻区内季节性降雨对地震活动性存在一定调节作用.综合分析S波速度模型、历史强震活动及b值,我们推断地震空段东部的彭灌断裂中段及周围部分隐伏断层存在发生强震的风险.     

Characteristics of the AE Distribution and Triggering Mechanism of a Simulated Meter-Scale Fault after Stick-Slip Events

With the more complete acoustic emission (AE) catalog improved by the multi-channel AE matched-filter technique (MFT), we study the spatiotemporal evolution of the AE activities after laboratory stick-slip events incorporate with the slip data recorded by displacement transducers on an ~1.5 m granite fault. The results show that the number of the AE events identified by MFT is about 9 times larger than that of the traditional method. A logarithmic expansion of early AE events along the fault strike is observed as a function of time, whereas the fault does not slip in the same manner. Thus, we related the expansion of the early AE events along the fault to the stress transfer caused by the adjacent AE events. Moreover, there is a good correlation between the cumulative number of the later AE events and the amount of fault slip. It suggests that the stress change caused by the continuous slip of the simulated fault after the stick-slip events response for the later AE events near or on the simulated fault.     

ZH-1卫星得到的中国及邻区顶部电离层背景特征

利用ZH-1(CSES)卫星LAP载荷原位电子密度数据对中国及邻区(0°-54°N,70°-140°E)的顶部电离层背景分布及随季节变化进行了详细分析,研究结果显示:(1)研究区赤道异常的纬度延伸范围、随经度分布形态及它们的季节变化,具有与其他研究结果一致的规律性.(2)中纬度区,白天电子密度存在一个低值带,夜间电子密...     

Spectral unmixing based spatiotemporal downscaling fusion approach

Time-series remote sensing data are important in monitoring land surface dynamics. Due to technical limitations, satellite sensors have a trade-off between temporal, spatial and spectral resolutions when acquiring remote sensing images. In order to obtain remote sensing images with high spatial resolution and high temporal frequency, spatiotemporal fusion methods have been developed. In this paper, we propose a Linear Spectral Unmixing-based Spatiotemporal Data Fusion Model (LSUSDFM) for spatial and temporal data fusion. In this model, the endmember abundance of the low-resolution image pixel is calculated based on that of the high-resolution image by the spectral mixture analysis. The endmember spectrum signals of low-resolution images are then calculated continuously within an optimized moving window. Subsequently, the fused image is reconstructed according to the endmember spectrum and its corresponding abundance map. A simulated dataset and real satellite images are used to test the fusion model, and the fusion results are compared with a current spectral unmixing based downscaling fusion model (SUDFM). Our experimental work shows that, compared to the SUDFM, the proposed LSUSDFM can achieve better quality and accuracy of fused images, especially in effectively eliminating the “plaque” phenomenon in the results by the SUDFM. The LSUSDFM has great potential in generating images with both high spatial resolution and high temporal frequency, as well as increasing the number of spectral bands of the high spatial resolution data.     

A statistical method for detecting spatiotemporal co-occurrence patterns

Spatiotemporal co-occurrence patterns (STCOPs) are subsets of Boolean features whose instances frequently co-occur in both space and time. The detection of STCOPs is crucial to the investigation of the spatiotemporal interactions among different features. However, prevalent STCOPs reported by available methods do not necessarily indicate the statistically significant dependence among different features, which is likely to result in highly erroneous assessments in practice. To improve the reliability of results, this paper develops a statistical method to detect STCOPs and discern their statistical significance. The proposed method detects STCOPs against the null hypothesis that the spatiotemporal distributions of different features are independent of each other. To construct the null hypothesis, suitable spatiotemporal point-process models considering spatiotemporal autocorrelation are employed to model the distributions of different features. The performance of the proposed statistical method is assessed by synthetic experiments and a case study aimed at identifying crime patterns among multiple crime types in Portland City. The experimental results demonstrate that the proposed method is more effective for detecting meaningful STCOPs than the available alternative methods.