准实时地震灾情综合评估系统的研发

震后如何快速获取灾区震情信息、准确评估地震灾情,是震后应急救援的关键问题。本研究从县市地震灾害应急救援需求出发,开发了准实时地震灾情综合评估系统,基于MEMS强震台网的强震观测数据、县域承灾体基础数据和手机终端获取的震后地震现场实时灾情信息,综合采用承灾体地震易损性分析方法和灾情动态修正算法,实现了县市地震灾区灾情准实时评估,为灾区地震应急指挥、救援和辅助决策提供科学依据。     

基于手机位置数据的四川九寨沟7.0级地震人流分析

2017年8月8日四川九寨沟7.0级震后,浙江省地震局利用大数据采集了1031.99万条手机位置记录和4.46万个空间格网位置。本文结合24hr连续定位的手机数据,使用手机位置数据分别对九寨沟灾区人口从时间和地理维度上进行了量化分析,估计了多维人口分布的偏差,同时,探讨了剔除微观误差数据用户来估算灾区通讯基站退服分布的方法。该项工作为震后快速获取灾区人口实时动态分布提供了有效途径,同时也为地震灾害评估提供了较准确的依据。     

基于手机定位服务的地震灾情速报和搜救技术探讨

介绍了无线移动定位技术,TOA、TDOA定位技术原理与数学模型,分析了手机定位服务在地震灾情速报和搜救被困人员的应用价值,提出了手机定位服务于灾情速报和地震灾区埋压被困人员搜救技术系统架构,为地震灾区的快速圈定提供重要支持,使得对大面积的地震灾区埋压被困人员准确、快速搜救成为可能。     

高精度静态人口空间分布研究——以银川市西夏区为例

地震发生后，人口空间分布密度是决定救援力量部署的重要依据。然而，高精度人口空间分布数据存在获取和更新困难的问题，缺少有效的解决途径。以银川市西夏区为例，基于高空间分辨率遥感影像，通过建筑物解译与实地调查相结合的方式获取住宅建筑物信息，建立人口与住宅建筑物之间的关系模型，得到更客观真实的人口空间分布情况。研究结果表明，以高空间分辨率遥感影像解译住宅建筑物作为人口空间分布指示因子建模，得到的总体预测人口误差率为3.56%，人口平均相对误差率为9.19%，研究结果具有较高的可靠性，为震前灾害风险评估和震后灾情快速评估提供可靠的人口空间分布数据。     

无人机技术在新疆塔县地震的应用及实现

目前,地震灾情信息的获取主要依靠地震现场调查,费时费力,为第一时间快速了解灾区灾情,以便尽快做出救灾决策,指挥调度救援力量,引进新的技术手段辅助获取灾情信息十分必要。进行了无人机技术在新疆塔什库尔干县地震的应用研究,利用无人机技术,获取了地震极灾区库孜滚村高分辨率影像数据,结合震前GF-1遥感影像数据及地震现场震害调查数据,获取极灾区灾情信息。结果表明:无人机遥感技术可快速采集极灾区遥感影像,有利于对灾情做出正确判断和评估。根据无人机遥感影像结合震前遥感影像和地震现场调查数据,进行区域建筑物结构类型和损毁程度遥感解译,可快速获取灾情,无人机技术在地震应急方面应用效果显著,为灾情评估、救援和灾后重建工作提供了科学决策依据。     

山西省人口空间公里格网分布模型研究

精准的人口分布信息是震后灾害快速评估、应急救援决策的重要依据。本文应用居民地这一与人口分布密切相关的基础数据,结合居民地建筑物给出不同区域人口权重,以人口统计数据为总量控制,完成人口空间公里格网数据模型建立,并与夜间灯光数据、道路数据、实际调研数据等进行对比分析。结果表明,该人口分布模型可靠性高,可为震后灾害快速评估、应急救援决策提供可参考的人口数据。     

基于遥感影像的建筑数据构建研究

在地震灾情快速评估中,基于空间分布的高精度房屋和人口数据是最重要的两组数据,其直接决定着灾情快速评估结果的准确性以及现场重点救援的目标指向。然而,高精度房屋数据的获取一直是一个难题,由于受到诸多因素的制约,至今缺少好的解决途径,这也是导致当前灾情快速评估准确性不高的主要原因之一。本文基于遥感影像和实地调查相结合,构建较高精度的房屋基础信息,更好地反映房屋和人口的实际分布情况,为提高地震灾害损失快速评估的精度和准确性奠定必要的基础。     

面向防震减灾的人口数据空间化研究——以2007年宁洱地震灾区为例

入口是地震灾害的重要受灾体,准确的入口空问分布信息是防震减灾工作的重要依据.本文借助地理信息系统,将人口统计数据与高分辨率遥感数据相结合,应用基于居民地的人口数据空间化方法,模拟人口空间分布.首先根据城市人口—面积异速生长模型的分形几何意义,推导出城乡人口一面积统一模型;进而以2007年宁洱地震灾区为例,在建立居民地分类体系和遥感解译标志的基础上,目视解译获得准确的居民地信息;最后应用城乡人口—面积统一模型获得网格人口密度矢量数据.经检验,本文的结果达到了较高的精度.同时在人口数据空间化完成的基础上,以地震受灾人口估算为例,探讨了人口数据空间化在防震减灾中的应用.研究结果表明,基于网格人口矢量数据的受灾人口估算结果更能客观反映地震灾情,可以为防震减灾和应急救援工作提供可靠的依据.     

震害快速评估中基于GM(1;1)模型的人口预测

地震灾区人口数据是破坏性地震发生时震害快速评估中一项重要的关键基础数据.本文利用GM(1,1)模型建立了甘肃省县一级人口数量的增长模型,实现了静态人口基础数据在地震发生时的动态计算,使其更接近于人口发展的实际情况,为震害评估提供更准确的人口基础数据.     

人口、房屋数据空间化及在2021年拜城MS5.4地震中的应用

地震灾情快速评估依赖于震区准确的人口、房屋等设施的数量和空间分布,是震后地震应急救援、地震烈度评估的核心支撑。以阿克苏地区的居民点、POI数据为基础,采用核密度估算结合道路分布和房屋占地面积的方法,构建了人口密度权重模型,获得了研究区1 km×1 km的人口、房屋格网数据。结果显示,各乡镇实际人口数量与模拟人口数量线性拟合相关系数为0.909,线性拟合相对较好,表明人口空间化结果达到了较高精度。为了更好地验证上述方法和数据,以2021年拜城M_S5.4地震为例,叠加对比分析震区人口、房屋格网数据与地震评估区之间的定量关系。按照地震评估区进行了人口、房屋面积的模拟,该数据与实际现场数据之间误差较小,能够满足震后地震灾情快速评估的需求,可为灾害预评估及地震现场灾害快速评估等工作提供可靠依据和支撑,同时空间化的方法也为后续该方向的研究提出了一个新的思路。     

SCIENCE CHINA Earth Sciences SUBJECT INDEX TO VOLUME 57(2014)

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Distribution of throughfall and stemflow in multi‐strata agroforestry,perennial monoculture,fallow and primary forest in central Amazonia,Brazil

The partitioning of rain water into throughfall, stemflow and interception loss when passing through plant canopies depends on properties of the respective plant species, such as leaf area and branch angles. In heterogeneous vegetation, such as tropical forest or polycultural systems, the presence of different plant species may consequently result in a mosaic of situations with respect to quantity and quality of water inputs into the soil. As these processes influence not only the water availability for the plants, but also water infiltration and nutrient leaching, the understanding of plant effects on the repartitioning of rain water may help in the optimization of land use systems and management practices. We measured throughfall and stemflow in a perennial polyculture (multi‐strata agroforestry), monocultures of peach palm (Bactris gasipaes) for fruit and for palmito, a monoculture of cupuaçu (Theobroma grandiflorum), spontaneous fallow and primary forest during one year in central Amazonia, Brazil. The effect on rain water partitioning was measured separately for four useful tree species in the polyculture and for two tree species in the primary forest. Throughfall at two stem distances, and stemflow, differed significantly between tree species, resulting in pronounced spatial patterns of water input into the soil in the polyculture system. For two tree species, peach palm for fruit (Bactris gasipaes) and Brazil nut trees (Bertholletia excelsa), the water input into the soil near the stem was significantly higher than the open‐area rainfall. This could lead to increased nutrient leaching when fertilizer is applied close to the stem of these trees. In the primary forest, such spatial patterns could also be detected, with significantly higher water input near a palm (Oenocarpus bacaba) than near a dicotyledonous tree species (Eschweilera sp.). Interception losses were 6·4% in the polyculture, 13·9 and 12·3% in the peach palm monocultures for fruit and for palmito, respectively, 0·5% in the cupuaçu monoculture and 3·1% in the fallow. With more than 20% of the open‐area rainfall, the highest stemflow contributions to the water input into the soil were measured in the palm monocultures and in the fallow. Copyright © 1999 John Wiley & Sons, Ltd.     

A Monte Carlo study of rainfall forecasting with a stochastic model

A procedure for short-term rainfall forecasting in real-time is developed and a study of the role of sampling on forecast ability is conducted. Ground level rainfall fields are forecasted using a stochastic space-time rainfall model in state-space form. Updating of the rainfall field in real-time is accomplished using a distributed parameter Kalman filter to optimally combine measurement information and forecast model estimates. The influence of sampling density on forecast accuracy is evaluated using a series of a simulated rainfall events generated with the same stochastic rainfall model. Sampling was conducted at five different network spatial densities. The results quantify the influence of sampling network density on real-time rainfall field forecasting. Statistical analyses of the rainfall field residuals illustrate improvement in one hour lead time forecasts at higher measurement densities.     

Anthropogenic Eutrophication and Red Tide Outbreak in Lacustrine Systems of the Kashmir Himalaya

Red tide, a recurrent phenomenon has become conspicuous in several Kashmir lake ecosystems since 1991. The responsible organism (Euglena pedunculata), a rare flagellate rediscovered in the Kashmir Himalaya (Khan 1993) caused first and unprecedented red tide outbreak, constituting a maximum of 96% of resident numerical phytoplankton density in Dal Lake. At present, conflicting hypotheses exist on the generation of causal assemblage(s) imparting redness to waters: Jeeji Bai (1991) linked its origin to acid precipitation – a fallout of burning oil‐fields during the Gulf War – whilst Khan (1993) holds local factor(s) responsible. Field/experimental studies support the latter contention that the influx of untreated sewage, in unison with warm temperatures, high levels of PhAR, iron and interruption to hydrological flow‐pattern together with absence/or reduction in grazing activity created conducive environmental milieu for red tide outbreak. Dal Lake “red tide” drifted the bloom‐inoculum to other waters, including Lake Wular, where additional ecological niches were carved out, threatening the aesthetic value and biological diversity of Kashmir lakes. Ecological monitoring indicates frequent seasonal red tide occurrence in Dal Lake (including summer‐autumn event of 1998) which testifies its unabated eutrophication status. Further studies are needed on ecological adaptability and biogeographic distribution of this rare and unique red tide‐causing flagellate.