National identity in relief

This paper explores disaster relief as a medium, through which national identity manifests, circulates and is produced in local spaces.The objective is to move beyond studies of nationalism that emphasize solely the national scale (or the scale of the state) and to make visible the role of women as productive rather than merely reproductive subjects of national communities. The paper is based on over a year of ethnographic field work conducted between the years 2001-2005, in post-earthquake (2001) and in post-Godhra (2002) Gujarat, India.     

云南绿春县城区滑坡成因及防治对策

绿春是云南省地质灾害较为严重的县城,滑坡为突出的地质灾害类型,对县城滑坡发育特征、表现形式、出现时间、空间分布等方面进行综合研究,为强化滑坡防治和管理、减轻灾害损失、维护人民生命财产安全提供科学依据。     

近41年来江南地区暴雨洪涝灾害时空变化特征分析

暴雨和防灾能力建设是影响洪涝灾害发生及其损失变化的重要因素.根据1978—2018年江南地区(沪、浙、闽、湘、赣五省、市)暴雨洪涝灾害数据、气象台站观测逐日降水量资料、社会经济数据,统计分析了近41年江南地区暴雨及其引发的洪涝灾害损失的变化特征及时、空差异,并从降水和社会防治两方面分析其成因.结果表明:近41年来江南地...     

辽宁雨季和多雨季标准的划分研究

遵循针对性、可靠性、实用性和可操作性的原则,对1951—2005年降水量和副热带高压脊线资料进行了统计分析。结果表明:采用降水量作为雨季划分标准,以副热带高压脊线位置作为多雨季划分标准,可得出辽宁各地雨季、多雨季常年起止时间,历年最早、最晚起止时间,从而使雨季、多雨季得以定时、定量预报,进而为防灾、减灾工作提供气象依据。同时得出,辽宁平均雨季降水量占全年平均降水量的60.8%,95%的区域性暴雨(暴雨站数大于等于3)出现在雨季时段内,雨季日数与雨季期间暴雨次数相关系数为0.97,与雨季期间总降水量相关系数为0.98;较强降水过程、重大洪涝灾害集中在多雨季,多雨季时段内辽宁大范围区域性暴雨(暴雨站数大于等于10)次数占全年总次数的70%。多雨季日数的长短一定程度上决定了大范围区域性暴雨的次数,同时决定了多雨季期间的总降水量。     

上游干支流洪水相关性对下游洪水频率分布的影响——以鄱阳湖抚河为例

较大流域下游的洪水往往是由上游干支流洪水叠加形成,而上游洪水变量一般会存在一定的空间相关性。为揭示流域不同来源洪水相关性对下游洪水频率分布特征的影响,本文选取江西省抚河流域作为研究实例,通过广义回归模型构建下游洪水变量对上游干支流洪水变量的条件分布,以此刻画上下游洪水变量之间的关系,然后基于二维Copula函数建立上游干支流洪水变量的联合概率分布,最后由全概率公式推导出下游洪水频率分布,在此基础上,进一步探讨上游洪水变量相关性对下游洪水频率分布特征的影响。结果表明,本文提出的洪水频率分布推导法对抚河李家渡站的洪水频率分布具有较好的拟合效果;上游洪水变量相关性是影响下游洪水频率分布的重要因素,具体来说,相关性的强度与尾部相关性对下游洪水频率分布的均值没有影响,但均对偏态系数C_v与峰度系数C_s等高阶统计特征有较为明显的影响,下游洪水频率分布的C_v值会随上游洪水变量相关性的增强呈现增加趋势,上尾相关性越强,C_v和C_s越大;流域上游洪水变量相关性强度及上尾相关性的增强都会导致下游极端洪水风险升高,此外,重现期较长的极端水文设计值对上游洪水变量相关性的变化更加敏感。     

南海主要岛礁概率海啸风险评估

影响地震海啸的震源参数众多且具有很强的不确定性,充分评估海啸风险需要大量的情景模拟.本文基于建立的概率海啸风险模型,采用一种高效的海啸模拟方法,评估了南海主要岛礁的概率海啸风险.通过对历史地震数据的分析,综合考虑震级、震中位置、震源深度的随机性,形成了百万数量级的潜在地震情景集,并通过叠加近似方法实现了大量地震情景引发...     

基于双损失指标的地震灾度模型

将地震灾害等级划分为巨灾、大灾、中灾、小灾、微灾等5个等级,给出每个灾害等级的灾度下限D_i。选取地震灾害的死亡人数和直接经济损失相对值作为计算灾度的2个指标,采用“直接经济损失/年人均GDP”处理方法对直接经济损失进行无量纲化,为不同年代、不同地区的地震灾害灾度大小对比奠定基础。采用椭圆方程作为相邻灾害等级之间的分界线,给出相应参数以确定分界线方程表达式,在此基础上给出合理的地震灾度模型计算公式。利用假设值和中国部分实际地震灾例验证了本文给出的地震灾度模型的科学性、适用性和可比性,给出的地震灾害灾度计算模型可以推广到其他灾害。最后,比较唐山地震和汶川地震的计算灾度值,认为唐山地震的灾度大于汶川地震,并对这一结果的合理性进行了讨论。     

基于RegCM4模式的华北区域未来洪涝灾害风险预估

基于区域气候模式RegCM4东亚地区25 km分辨率气候变化试验模拟结果,在分析华北区域基准期(1986—2005年)洪涝灾害致灾危险度以及人口和GDP承灾体易损度基础上,建立区域灾害风险评估模型;应用建立的模型预估华北区域RCP4.5和RCP8.5两种情景下近期(2020—2035年)、中期(2046—2065年)和远期(2080—2098年)洪涝灾害风险的变化。结果表明：(1)RegCM4对华北区域基准期洪涝灾害危险度评估指标R_{20 mm}和R_{x5 day}模拟能力较好,基准期洪涝灾害风险Ⅲ级及以上的区域位于北京、天津、河北南部和东部以及山西南部等地。(2)RCP4.5和RCP8.5情景下,未来3个不同时期华北区域大部分地区R_{20 mm}和R_{x5 day}、洪涝致灾危险度以及风险增加,RCP8.5情景下增加更明显。风险等级Ⅲ级及以上范围在两种情景下均在中期最大。     

The Deccan tholeiite lavas and dykes of Ghatkopar–Powai area,Mumbai, Panvel flexure zone: Geochemistry,stratigraphic status,and tectonic significance

Mumbai City, situated on the western Indian coast, is well known for exposures of late-stage Deccan pillow basalts and spilites, pyroclastic rocks, rhyolite lavas, and trachyte intrusions. These rock units, and a little-studied sequence of tholeiitic flows and dykes in the eastern part of Mumbai City, constitute the west-dipping limb of a regional tectonic structure called the Panvel flexure. Here we present field, petrographic, major and trace element and Sr–Nd isotopic data on these tholeiitic flows and dykes, best exposed in the Ghatkopar–Powai area. The flows closely resemble the Mahabaleshwar Formation of the thick Western Ghats sequence to the east, in Sr–Nd isotopic ratios and multielement patterns, but have other geochemical characteristics (e.g., incompatible trace element ratios) unlike the Mahabaleshwar or any other Formation. The flows may have originated from a nearby eruptive center, possibly offshore of Mumbai. Two dykes resemble the Ambenali Formation of the Western Ghats in all geochemical characteristics, though they may not represent feeders of the Ambenali Formation lavas. Most dykes are distinct from any of the Western Ghats stratigraphic units. Some show partial (e.g., Sr–Nd isotopic) similarities to the Mahabaleshwar Formation, and these include several dykes with unusual, concave-downward REE patterns suggesting residual amphibole and thus a lithospheric source. The flows and dykes are inferred to have undergone little or no contamination, by lower continental crust. Most dykes are almost vertical, suggesting emplacement after the formation of the Panvel flexure, and indicate considerable east–west lithospheric extension during this late but magmatically vigorous stage of Deccan volcanism.     

Multi-hazard susceptibility mapping based on Convolutional Neural Networks

Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions. However, with the rapid development of artificial intelligence technology, multi-hazard susceptibility prediction techniques based on machine learning has encountered a huge bottleneck. In order to effectively solve this problem, this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks (CNN). First, we use historical flash flood, debris flow and landslide locations based on Google Earth images, extensive field surveys, topography, hydrology, and environmental data sets to train and validate the proposed CNN method. Next, the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria, i.e., coefficient of determination, overall accuracy, mean absolute error and the root mean square error. Experimental results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods, debris flows and landslides. Finally, the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility map. It can be observed from the map that 62.43% of the study area are prone to hazards, while 37.57% of the study area are harmless. In hazard-prone areas, 16.14%, 4.94% and 30.66% of the study area are susceptible to flash floods, debris flows and landslides, respectively. In terms of concurrent hazards, 0.28%, 7.11% and 3.13% of the study area are susceptible to the joint occurrence of flash floods and debris flow, debris flow and landslides, and flash floods and landslides, respectively, whereas, 0.18% of the study area is subject to all the three hazards. The results of this study can benefit engineers, disaster managers and local government officials involved in sustainable land management and disaster risk mitigation.