Fine assessment of tropical cyclone disasters based on GIS and SVM in Zhejiang Province, China

Tropical cyclones represent major natural disasters in low- and mid-latitude coastal areas. Effective assessment of tropical cyclone disasters provides a scientific reference for the formulation of tropical cyclone prevention and disaster-relief measures. Tropical cyclone disasters in Zhejiang Province are mainly studied based on GIS technology, by considering disaster-causing factors, disaster-affected bodies, the disaster-formative environment, and spatial distribution of disaster prevention and relief capacity. In light of an uncertain nonlinear relationship between assessment factors and disaster factors, we used support vector machines to establish a fine, quantitative assessment model. This model evaluates the following disaster indices: Disaster-affected population, direct economic loss, affected crop area, and number of damaged houses resulting from a tropical cyclone disaster in Zhejiang, with the county as basic assessment unit. Assessment of tropical cyclone No. 0908 shows that the developed assessment model is able to accurately evaluate the geographical distribution of losses caused by a tropical cyclone.     

Meteorological Office, Pune-411 005

Natural disasters like floods, tornadoes, tropicalcyclones, heat and cold wavewreak havoc and cause tremendous loss ofproperty all over the world. Most ofthe natural disasters are either dueto weather or are triggered due toweather related processes.Extreme weather events claimed thousands oflives and caused damage on vastscale. Recent super cyclone which affectedOrissa in 1999, Bangladesh cyclone of1970 and Hurricane Andrew in 1992 areexamples of some of the more damagingtropical cyclones which affected developingas well as the developed world. Heatand cold waves are also extreme events,which cause enormous losses in terms oflives lost and human discomfort and ailmentsarising out of them. The heat waveof 1995 and 1998 are still fresh in the mindof the Indian public. The estimated lossof human lives due to heat wave in 1998 was morethan 15,000. Economic losses asa result of these disasters and in particular inassociation with tropical cyclones haveincreased enormously over the last three decades.During 1961–1991, total loss oflives from drought alone was 1,333,728 overthe whole world. In terms of economiclosses, there is 8–10 fold increase from thebase figure of 1960. The socio-economicimpact of natural disaster is complex dependingupon the vulnerability of the placeand mitigation strategies that are put in place.Meteorology plays a crucial role in forewarningpeople about the severe/extremeweather systems and a constant endeavour by themeteorological services worldover has gone a long way towards minimizing thelosses caused by natural disasters.The paper summarises the natural disasterstatistics over south Asia and the possibleprediction strategies for combating theirsocio-economic impacts.     

Storm surge modelling for the Bay of Bengal and Arabian Sea

Most of the countries around the North Indian Ocean are threatened by storm surges associated with severe tropical cyclones. The destruction due to the storm surge flooding is a serious concern along the coastal regions of India, Bangladesh, Myanmar, Pakistan, Sri Lanka, and Oman. Storm surges cause heavy loss of lives and property damage to the coastal structures and losses of agriculture which lead to annual economic losses in these countries. About 300,000 lives were lost in one of the most severe cyclones that hit Bangladesh (then East Pakistan) in November 1970. The Andhra Cyclone devastated part of the eastern coast of India, killing about 10,000 persons in November 1977. More recently, the Chittagong cyclone of April 1991 killed 140,000 people in Bangladesh, and the Orissa coast of India was struck by a severe cyclonic storm in October 1999, killing more than 15,000 people besides enormous loss to the property in the region. These and most of the world’s greatest natural disasters associated with the tropical cyclones have been directly attributed to storm surges. The main objective of this article is to highlight the recent developments in storm surge prediction in the Bay of Bengal and the Arabian Sea.     

自然灾害灾情评估研究与实践进展

自然灾害灾情评估是科学开展灾害管理工作的基础。灾情评估以单次灾害过程的灾前预评估、灾中应急评估、灾后综合评估以及区域灾情评估为主要内容,基于历史灾情统计资料的评估方法、基于承灾体易损性的评估方法、现场抽样调查统计方法、遥感图像或航片识别法、基层统计上报方法、经济学方法等是灾情评估的主要方法。灾情评估的目标从对灾情的估算和统计扩展到对灾情大小的分级,评估内容上逐步重视灾害的社会经济影响评估,评估方法上注重多种方法的综合应用,以及建立灾情评估系统是灾情评估研究与应用的主要发展方向。国内外研究和实践成果在减灾工作中的实际应用亟待加强,迫切需要形成包括对单次灾害过程的灾前预评估、灾中应急评估、灾后综合评估以及区域灾情综合评估在内的自然灾害灾情评估的内容体系、评估指标体系、评估标准体系和评估方法体系。     

Performance evaluation of HWRF model during post monsoon tropical cyclone Nilofar

An accurate tropical cyclone track and intensity forecast is very important for disaster management. Specialized numerical prediction models have been recently used to provide high-resolution temporal and special forecasts. Hurricane Weather Research and Forecast (HWRF) model is one of the emerging numerical models for tropical cyclone forecasting. This study evaluates the performance of HWRF model during the post monsoon tropical cyclone Nilofar on the north Indian Ocean basin. The evaluation uses the best track data provided by the Indian Meteorological Department (IMD) and the Joint Typhoon Warning Centre (JTWC). Cyclone track, central pressure, and wind speed are covered on this evaluation. Generally, HWRF was able to predict the Nilofar track with track error less than 230 km within the first 66 h of forecast time span. HWRF predicted more intense tropical cyclone. It predicted the lowest central pressure to be 922 hPa while it reached 950 hPa according to IMD and 937 hPa according to JTWC. Wind forecast was better as it predicted maximum wind speed of 122 kt while it reached 110 and 115 kt according to IMD and JTWC, respectively.     

浙江宁波市地质灾害的雨量阈值及预报分析

山体滑坡、崩塌、泥石流等地质灾害主要由强降水或连续性降水引发产生,根据最近七年来宁波市国土资源局和宁波市气象台联合制作的地质灾害气象预报预警和地质灾害实况资料分析,结果发现:在最近七年里出现的159处地质灾害中,由热带气旋强降水引发的地质灾害有142处,约占89%;宁波市绝大多数地质灾害是由强降水引发,而强降水中又以热带气旋强降水为主要诱因,引发宁波市地质灾害的有效雨量大多数在100mm以上,因此,100mm有效雨量可作为预报引发宁波市地质灾害的雨量阈值,而200mm有效雨量可引发多处地质灾害产生。统计结果为以后地质灾害气象预报预警及强降水的临近监测提供参考依据。     

The economic impact of future increase in tropical cyclones in Japan

This article estimates the non-first-order economic loss in Japan due to a future increase in tropical cyclones. One possible effect of global warming could be the increase in intensity of tropical cyclones. Using historical storm tracks between the years 1978 and 2007 and altering their intensities due to this potential increase in their intensity, this paper calculates the future potential regional GDP loss in a certain area that is affected by tropical cyclones. Most of the literature is concerned with physical damage and the loss of lives due to tropical cyclones. However, there are additional economic costs when sustained wind speeds are higher than 30 knots (55.56 km/h), a level that generally will lead to a precautionary cessation of many human activities. Using a Monte Carlo simulation, the paper calculates the potential economic costs for the year 2085 under a climate change scenario with a linear one-per cent yearly increase in CO₂. Using a spatial distribution of economic activity in Japan, it is possible to forecast which parts of the country are likely to experience the highest loss risk.     

Progress and Prospect of Statistics and Assessment of Large-scale Natural Disaster Damage and Losses

In June 2014, the "Statistics System for the Damage and Loss of Large-scale Natural Disasters" (SSDLLND) was issued by the Ministry of Civil Affairs and the Office of National Disaster Reduction Committee, which marked that the statistics and assessment of China's catastrophic natural disaster damage and losses formally entered a new stage of institutionalization. On the basis of analyzing the five major international disaster damage and loss assessment systems, including HAZUS-MH, ECLAC, DaLA, EMA-DLA and PDNA, the differences between the “SSDLLND” of China and five major international systems were compared from the statistics and assessment contents and indicators. Combined with the statistics and assessment practices of China’s large-scale disaster damage and losses and the characteristics of international systems in recent years, the future development of the SSDLLND were proposed in three aspects: Enriching and improving the framework of damage and loss statistics content, stepwise improvement of disaster impact assessment methods (such as the ecological capital loss assessment, tourism industry loss assessment due to the disasters, etc.), and improving indicators and parameters of loss statistics. The study has an important practical significance for improving the statistics and assessment system of the damage and loss of major natural disasters and better serving the post-disaster recovery and reconstruction decision-making.     

Risk assessment of rainstorm and flood disasters in China between 2004 and 2009 based on gray fixed weight cluster analysis

Rainstorm and flood disasters frequently occur in China, causing heavy losses for people’s lives and property and reducing the capability of sustainable development of the national and local economy. In this study, the risks of the rainstorm and flood disasters are assessed for the Chinese mainland, excluding Hong Kong, Macao, and Taiwan and also employ the historical data of seven indicators, including the affected area of crops, the affected population, the direct economic loss, and etc., from 2004 to 2009. Based on the large 1,302 historical sample data, the impact of rainstorm and flood disasters were analyzed using the methodology of gray fixed weight cluster analysis according to disaster losses, which were divided into the three gray classes of high, medium, and low. The regional differences of the risk assessment of the rainstorm and flood disasters are discussed, and the dynamical risk zoning map is conducted. The results show a consistent conclusion with the actual losses of rainstorm and flood disasters over each administrative district, which can provide more scientific evidence for the relevant departments of disaster prevention and mitigation.     

Intensity forecast of tropical cyclones over North Indian Ocean using multilayer perceptron model: skill and performance verification

The coastal regions of India are profoundly affected by tropical cyclones during both pre- and post-monsoon seasons with enormous loss of life and property leading to natural disasters. The endeavour of the present study is to forecast the intensity of the tropical cyclones that prevail over Arabian Sea and Bay of Bengal of North Indian Ocean (NIO). A multilayer perceptron (MLP) model is developed for the purpose and compared the forecast through MLP model with other neural network and statistical models to assess the forecast skill and performances of MLP model. The central pressure, maximum sustained surface wind speed, pressure drop, total ozone column and sea surface temperature are taken to form the input matrix of the models. The target output is the intensity of the tropical cyclones as per the T??number. The result of the study reveals that the forecast error with MLP model is minimum (4.70?%) whereas the forecast error with radial basis function network (RBFN) is observed to be 14.62?%. The prediction with statistical multiple linear regression and ordinary linear regression are observed to be 9.15 and 9.8?%, respectively. The models provide the forecast beyond 72?h taking care of the change in intensity at every 3-h interval. The performance of MLP model is tested for severe and very severe cyclonic storms like Mala (2006), Sidr (2007), Nargis (2008), Aila (2009), Laila (2010) and Phet (2010). The forecast errors with MLP model for the said cyclones are also observed to be considerably less. Thus, MLP model in forecasting the intensity of tropical cyclones over NIOs may thus be considered to be an alternative of the conventional operational forecast models.     

Swarm intelligence and neural nets in forecasting the maximum sustained wind speed along the track of tropical cyclones over Bay of Bengal

Tropical cyclones are well-known extreme weather and the cause of considerable damages, injuries and loss of life. The assessment of the maximum sustained wind speed along the track of the tropical cyclones is very important for estimating the strength of the cyclones. The swarm intelligence in the form of ant colony optimization (ACO) technique is introduced in this study to compute the pheromone deposition along the track of tropical cyclones followed by neural nets to forecast the maximum sustained wind speed of the cyclones occurring over the Bay of Bengal of North Indian Ocean. The ACO is a nonlinear problem-based meta-heuristic optimization method for finding approximate solutions to discrete optimization problems and simulates the decision-making processes of ant colony similar to other adaptive learning techniques. The method has shown its application potential in various fields including the prediction of monsoon rainfall. In this study, the amount of pheromone deposition during the successive stages of the cyclones has been estimated. A range of minimum central pressure (MCP), central pressure drop (PD), maximum sustained wind speed (MSWS) and intensity (T-No) associated with the cyclones of Bay of Bengal are utilized to form the input matrix of the neural nets. The neural nets are trained to forecast the maximum sustained wind speed along the track of the tropical cyclones over Bay of Bengal. The result reveals that the errors in forecasting the MSWS along the track of tropical cyclones with 6, 12, 18 and 24 h lead time are 2.6, 2.9, 3.1 and 4.8, respectively. The result is compared with the existing dynamical, statistical and adaptive models to evaluate the skill of the present model. The result is well validated with observation.     

青海牧区干旱、 雪灾灾害损失综合评估技术研究

利用青海省20个草地生态监测站点2003-2011年监测的牧草、 降水和相应年份的灾害监测调查资料分析表明, 青海高原草地牧草产量与降水相关密切, 8月末牧草产量与5-8月降水量相关系数均达到了0.001的相关性检验, 建立了不同草地类型不同月份牧草产量与降水量的最优模拟方程; 利用降水量距平百分率划分干旱等级, 建立了干旱等级与牧草和牲畜损失的对应关系, 实现了干旱和雪灾对草地、 牲畜直接经济损失的定量评估. 对比分析了干旱和雪灾灾害对畜牧业造成损失比例, 将干旱和雪灾两个不同概念的灾害通过经济损失有机地联系起来, 直观地表明了在不同等级的灾害情况下, 损失造成的大小和比例. 结果表明: 随着灾害等级加重, 雪灾造成的损失比例增大, 在特大灾时, 雪灾造成的损失是旱灾损失的2.5倍.评估模型符合草地牧草生长规律和畜牧业生产特征, 实例评估符合畜牧业实际损失程度. 因此, 能够在降水偏少或偏多时对草地畜牧业评估服务中推广应用.     

An analysis on disasters management system in China

This paper introduced the principle, institutional framework, and legal construction of Chinese disaster management system, and operating mechanisms of disaster management departments in pre-disaster, response and post-disaster phases were also demonstrated. Although China has basically built the disaster management system, formed national emergency plan system, and gained achievements in some aspects, the disasters management system is still a segmental model and is not an integrated management system. This article analyzes problems of Chinese disaster management system, and puts forward some suggestions for improving and optimizing this system. This can make Chinese disaster management system better respond and handle to disasters risk, and reduce the social and economic losses of disasters caused.     

青海省地质灾害防治资金投入与成效分析

因特殊的地质环境条件,我省地质灾害发育,加之近年来经济增长、城镇扩张、降雨增多等因素共同影响,地质灾害数量和经济损失都呈现上升的趋势,因此地质灾害防治显得尤为必要。为全面认识地质灾害防治投入和社会经济发展的关系,本文通过对近十年的地质灾害现状、地质灾害防治投入和经济发展数据进行分析研究,建立固定经济投入下的总体收益函数,认为虽然青海省近年来地质灾害防治效果较好,但是从整体看,我省地质灾害防治投入较低,未达到最佳收益点。因此,相关部门在兼顾经济发展的同时也要关注地质灾害防治工作,随着经济增长适当调整防治投入,不仅能有效控制地质灾害损失,而且也保障社会其他效益。     

The Tropical Cyclone Risk in Cairns

Because of its location close to a region of tropical cyclone formation, the city ofCairns is regularly affected by tropical cyclones. Most cyclones occur in the monthsJanuary–March, although some have affected Cairns in December and April. There is a strong year-to-year variation in tropical cyclone numbers in the Cairns region, with twice as many impacts occurring during La Niña conditions than during El Niño. Several case studies are presented of cyclone impacts on Cairns. Rapidly developing cyclones are a particularly difficult forecast problem, as they can strike the city with less than 24 hours warning. Serious inundation from storm surge often occurs some time after landfall at Cairns.     

湖南省新宁县地质灾害经济损失评估

文章介绍了新宁县地质灾害特征,依据区内地质灾害经济损失评估的原则和方法,进行地质灾害现状评估和预测评估,为防治地质灾害提供科学借鉴。评估结果表明：（1）新宁县地质灾害灾情以轻度为主,其中,滑坡88处,崩塌10处,泥石流9处,地面塌陷6处,不稳定斜坡5处,共计118处;中度灾害及重度灾害各1处,皆为泥石流灾害。各类地质灾害已伤亡15人,毁坏城乡房屋333间,毁田44．5hm^2,毁路135m,直接经济损失878．1万元。（2）以新宁县地质灾害危险性分级指标为依据,按照点评估的要求,把新宁县地质灾害划分为高、中等、低三个危险性级别,其中高危险性级别9处,中等危险性级别49处,低危险性级别60处,地质灾害隐患点共威胁着4885人的生命和财产安全,威胁田地282．1hm^2,潜在最大经济损失为5042．1万元。     

The occurrence laws of campus stampede accidents in China and its prevention and control measures

This research appraises how residential built environment growth influences coastal exposure and how this component of societal vulnerability contributes to tropical cyclone impact and disaster potential. Historical housing unit data and future demographic projections from a high-resolution, spatial allocation model illustrate that the area within 50 km of the US Atlantic and Gulf Coastlines has the greatest housing unit density of any physiographic region in the USA, with residential development in this region outpacing non-coastal areas. Tropical cyclone exposure for six at-risk metropolitan statistical areas (MSAs) along the US Atlantic and Gulf Coasts are assessed. All six MSAs evaluated are distinct in their development character, yet all experienced significant growth from 1940 through the contemporary period; projections from the model under various socioeconomic pathways reveal that this growth is anticipated to continue during the twenty-first century. Using a worst-case scenario framework, the historical and future residential data for the six MSAs are intersected with synthetic hurricane wind swaths generated from contemporary landfalling events. The New York City MSA contains the greatest residential built environment exposure, but Miami is the most rapidly changing MSA and has the greatest potential for hurricane disaster occurrence based on the juxtaposition of climatological risk and exposure. A disaster potential metric illustrates that all six MSAs will experience significant increases in disaster probability during the twenty-first century. This analysis facilitates a detailed spatiotemporal assessment of US coastal region vulnerability, providing decision makers with information that may be used to evaluate the potential for tropical cyclone disasters, mitigate tropical cyclone hazard impacts, and build community resilience for these and other hazards in the face of environmental and societal change.     

China’s regional meteorological disaster loss analysis and evaluation based on grey cluster model

To evaluate the regional meteorological disaster loss of China, this paper analyzed the different types of meteorological disasters, including droughts, floods, tropical storms, snowstorms and hail disasters. Based on the analysis about Chinese geographical features, the historical characteristics of different meteorological disasters are analyzed. In particular, these meteorological disasters influence to agriculture production are discussed. According to the analysis of data from 2004 to 2010, we know that the distribution characteristics are very different about different disasters. Provinces like Heilongjiang, Liaoning, Jilin, Inner Mongolia, Gansu, Shanxi and Yunnan are serious affected areas of drought influence. And Anhui, Shandong, Jiangsu, Henan, Jiangxi, Hubei, Hunan, Guangxi, Sichuan and Heilongjiang are serious affected areas by floods and heavy rain. While Guangdong, Fujian, Zhejiang, Shanghai, Jiangsu and Shandong are mainly affected by tropical storms, Henan, Hebei, Hunan and Hubei are serious affected by snowstorms and hail disasters. Then, a novel method based on grey cluster model is constructed and combined with the regional meteorological disaster loss evaluation index system. A total of 31 provinces are considered to evaluate the integrated meteorological disaster losses. The results indicated that Beijing, Tianjin, Shanghai, Xizang, Qinghai and Ningxia belong to the lighter loss grey class. Jiangxi, Hubei, Hunan, Hainan, Sichuan and Gansu belong to the serious loss grey class. Other regions belong to the general loss grey class that the influence caused by meteorological disasters not better than the lighter loss grey class and not worst than the serious loss grey class.     

Prediction of severe tropical cyclones over the Bay of Bengal during 2007–2010 using high-resolution mesoscale model

In this paper, the performance of a high-resolution mesoscale model for the prediction of severe tropical cyclones over the Bay of Bengal during 2007?C2010 (Sidr, Nargis, Aila, and Laila) is discussed. The advanced Weather Research Forecast (WRF) modeling system (ARW core) is used with a combination of Yonsei University PBL schemes, Kain-Fritsch cumulus parameterization, and Ferrier cloud microphysics schemes for the simulations. The initial and boundary conditions for the simulations are derived from global operational analysis and forecast products of the National Center for Environmental Prediction-Global Forecast System (NCEP-GFS) available at 1°lon/lat resolution. The simulation results of the extreme weather parameters such as heavy rainfall, strong wind and track of those four severe cyclones, are critically evaluated and discussed by comparing with the Joint Typhoon Warning Center (JTWC) estimated values. The simulations of the cyclones reveal that the cyclone track, intensity, and time of landfall are reasonably well simulated by the model. The mean track error at the time of landfall of the cyclone is 98?km, in which the minimum error was found to be for the cyclone Nargis (22?km) and maximum error for the cyclone Laila (304?km). The landfall time of all the cyclones is also fairly simulated by the model. The distribution and intensity of rainfall are well simulated by the model as well and were comparable with the TRMM estimates.