Assessing response behaviour of debris-flows affected communities in Kaohsiung,Taiwan

During Typhoon Morakot which hit Taiwan from 6 to 9 August, 2009, Kaohsiung City was highly affected by devastating debris-flows and flooding. Recorded casualties were 699 deaths and 1,766 damaged homes, mostly in the mountainous areas of Kaohsiung City. Due to a largely malfunctioning or absent early-warning system, residents in those mountainous villages were required to rely on individual- and/or community-based capacities to evacuate and respond to debris-flow-related disasters. Hence, this study investigates the response behaviour of selected debris-flow-affected communities in Kaohsiung City, based on a preparedness awareness action and affect model. Key results from the survey highlight that only 13.8 % of the households received formal (institutional) early warning, whereas 86.2 % households had to rely on their intrinsic senses and indigenous knowledge to recognise the onset of debris-flows in their villages during Typhoon Morakot. Among those households who did not receive formal early warning, 10 % of the households received previous disaster education, 17 % had previous disaster experience, and 73 % did have neither disaster education nor disaster experience. Furthermore, households with disaster education were among those who were best prepared and knew best how to evacuate and respond to debris-flow-related disasters followed by households with disaster experiences. Finally, findings from the survey and selected key informants’ interviews identified that the response behaviour of communities ought to be enhanced through the following measures: conduction of hydro-meteorological-related disaster education, improved participatory risk communication and enhanced recognition of communities as vital actors during a disaster to provide local knowledge and support to relief operations.     

Risk early warning of maize drought disaster in Northwestern Liaoning Province,China

This study presents a methodology of risk early warning of maize drought disaster in Northwestern Liaoning Province from the viewpoints of climatology, geography, disaster science, environmental science, and so on. The study area was disaggregated into small grid cells, which has higher resolution than counties. Based on the daily meteorological data and maize yield data from 1997 to 2005, the risk early warning model was built up for drought disaster. The early warning crisis signs were considered from exogenous warning signs and endogenous warning signs. The probability of drought was taken as endogenous warnings sign, which was calculated by logistic regression model. Beside precipitation, wind speed and temperature were taken into consideration when assessing the drought. The optimal partition method was used to define the threshold of each warning grade. Take the year of 2009 as an example, this risk early warning model performed well in warning drought disasters of each maize-growing stage. Results obtained from the early warning model can guide the government to take emergency action to reduce the losses.     

Assessment of potential sediment disasters and resilience management of mountain roads using environmental indicators

In recent years, extreme rainfall events occur frequently, causing serious watershed sediment disasters, destroying mountain roads, and endangering the safety of residents' lives and property. This study aims to deal with the spatial change of potential sediment movement on the road slope pre-disaster and to screen disaster hot spots for early warning and control system. The conceptual model is used to simulate the distribution of primary and/or derived disasters on a watershed scale to assess the impact of sediment disasters caused by heavy rain event. Correlation analysis shows that the models in assessment of primary disaster and derived disaster are significantly correlated with the collapse ratio and disaster ratio, respectively. Since the primary disaster has been considered when calculating the derived disaster risk, the terrain subdivision along Provincial Highway 21 (Tai-21) is extracted to understand the derived sediment disaster on the road slope. The model can effectively evaluate the road sections prone to disasters. According to the risk level, the hot spot of road slope disasters and the management of disaster resilience are determined and can be the reference for disaster prevention and control.

     

Integrated model for earthquake risk assessment using neural network and analytic hierarchy process:Aceh province,Indonesia

Catastrophic natural hazards,such as earthquake,pose serious threats to properties and human lives in urban areas.Therefore,earthquake risk assessment(ERA)is indispensable in disaster management.ERA is an integration of the extent of probability and vulnerability of assets.This study develops an integrated model by using the artificial neural network–analytic hierarchy process(ANN–AHP)model for constructing the ERA map.The aim of the study is to quantify urban population risk that may be caused by impending earthquakes.The model is applied to the city of Banda Aceh in Indonesia,a seismically active zone of Aceh province frequently affected by devastating earthquakes.ANN is used for probability mapping,whereas AHP is used to assess urban vulnerability after the hazard map is created with the aid of earthquake intensity variation thematic layering.The risk map is subsequently created by combining the probability,hazard,and vulnerability maps.Then,the risk levels of various zones are obtained.The validation process reveals that the proposed model can map the earthquake probability based on historical events with an accuracy of 84%.Furthermore,results show that the central and southeastern regions of the city have moderate to very high risk classifications,whereas the other parts of the city fall under low to very low earthquake risk classifications.The findings of this research are useful for government agencies and decision makers,particularly in estimating risk dimensions in urban areas and for the future studies to project the preparedness strategies for Banda Aceh.     

Basinwide disaster loss assessments under extreme climate scenarios: a case study of the Kaoping River basin

This study examined the Kaoping River basin, Taiwan, an area severely destroyed by Typhoon Morakot in 2009. Dynamically downscaled data were applied to simulate extreme typhoon precipitation events for facilitating future preparation efforts (2075–2099) under climate change conditions. Models were used to simulate possible impacts in upstream and downstream areas for basinwide disaster loss assessment purposes. The Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability and FLO-2D models were applied to simulate slope-land disaster impacts and sediment volume in the upstream area. The sediment delivery ratio was used to calculate the valid sediment amount delivered downstream and the riverbed uplift altitude. SOBEK was used to build a flood impact model for the Kaoping River basin, and the model was used to simulate potential flooding caused by future extreme typhoon events. The Taiwan Typhoon Loss Assessment System established by the National Science and Technology Center for Disaster Reduction was used to evaluate the potential loss associated with extreme events. The property loss calculation included 32 land-use categories, including agriculture, forestry, fishery, and animal husbandry losses; industrial and commercial service losses; public building losses; and traffic and hydraulic facility losses. One of the Kaoping River basin townships, Daliao District, had the highest flood depth increase ratio (12.6%), and the losses were 1.5 times the original situation. This was much worse than were the losses suffered during Typhoon Morakot. These results also show that sediment delivered from the upstream areas had a significant influence on the downstream areas. This is a critical issue for future flood mitigation under climate change conditions.     

岩质滑坡−碎屑流的运动距离计算公式研究

滑坡?碎屑流的远程运动距离是碎屑流体所能够达到的最大堆积距离,是灾害预警和评估的重要指标。通过总结已有碎屑流运动距离研究成果,从岩体势能入手开展研究,并结合量纲分析,首先建立了运动距离与势能之间的基本方程。其后,采用4种颗粒材料开展岩质碎屑流滑槽试验,研究碎屑体体积V、滑移区坡度?、碎屑粒径d以及最大垂直运动距离H等对碎屑流运动距离L的影响,通过逐步拟合回归,建立了基于势能的岩质滑坡?碎屑流最大水平运动距离的计算公式。最后,采用汶川地震触发的38个岩质碎屑流以及17个其他典型岩质滑坡碎屑流数据对计算公式进行了验证,结果表明,考虑该运动距离计算公式具有较好的可靠性,能够为山区滑坡?碎屑流灾害预警工作提供理论指导。     

Earthquake risk assessment in NE India using deep learning and geospatial analysis

Earthquake prediction is currently the most crucial task required for the probability, hazard, risk mapping, and mitigation purposes. Earthquake prediction attracts the researchers' attention from both academia and industries. Traditionally, the risk assessment approaches have used various traditional and machine learning models. However, deep learning techniques have been rarely tested for earthquake probability mapping. Therefore, this study develops a convolutional neural network (CNN) model for earthquake probability assessment in NE India. Then conducts vulnerability using analytical hierarchy process (AHP), Venn's intersection theory for hazard, and integrated model for risk mapping. A prediction of classification task was performed in which the model predicts magnitudes more than 4 Mw that considers nine indicators. Prediction classification results and intensity variation were then used for probability and hazard mapping, respectively. Finally, earthquake risk map was produced by multiplying hazard, vulnerability, and coping capacity. The vulnerability was prepared by using six vulnerable factors, and the coping capacity was estimated by using the number of hospitals and associated variables, including budget available for disaster management. The CNN model for a probability distribution is a robust technique that provides good accuracy. Results show that CNN is superior to the other algorithms, which completed the classification prediction task with an accuracy of 0.94, precision of 0.98, recall of 0.85, and F1 score of 0.91. These indicators were used for probability mapping, and the total area of hazard (21,412.94 km²), vulnerability (480.98 km²), and risk (34,586.10 km²) was estimated.     

The effect of urbanization on sediment disasters

An aim of the study is to understand the effect of recent urbanization on the increase of sediment disasters. A case study was done in Nagasaki City and its vicinity, where a severe disaster had occurred in July 1982. The results of this study are summarized as follows: (1) The houses built in the first period locate on the relatively safe morphological units such as lower terrace and gentle foot slope. (2) The houses built in the second period intruded onto the rather risky morphological units; steep slope, valley floor and river bed. These units were not used as a residential area before. (3) The houses built in the last period are settled on the more risky area compared to older settled areas. However the demand for new housing land increased. Thus it was decided to develop the area risky to sediment disaster.     

Typhoon disaster in China: prediction,prevention, and mitigation

Typhoon-induced disaster is one of the most important factors influencing the economic development and more than 250 million in China. In view of the existing state of typhoon disaster prediction, prevention, and mitigation, this paper proposes a new probability model, Multivariate Compound Extreme Value Distribution (MCEVD), to predict typhoon-induced extreme disaster events. This model establishes prevention criteria for coastal areas, offshore structures, and estuarine cities, and provides an appropriate mitigation scheme for disaster risk management and decision-making.     

Dynamic risk prediction based on discriminant analysis for maize drought disaster

This study presents a discriminant analysis-based method for prediction of agriculture drought disaster risk. We selected the Chaoyang city in the Northeast China as the study area. We employed multi-scale standard precipitation index (SPI) to reflect drought hazard. We used the yield losses to indicate the drought disaster risk, which was divided into no, low, or high drought risk. We used the multi-scale SPI and drought disaster risk as the input factors for the discriminant analysis-based risk prediction model. The results showed that the model’s prediction accuracy varied between 40 and 82.4 %. The accuracy of high drought disaster risk category was higher than low and no drought disaster risk category. The prediction accuracy of the milky maturity stage was highest. We use leave-one-out cross-validation method to validate the model’s accuracy. And the results showed that the model validation accuracy of high drought group could reach 70.6 % in milky maturity stage. This study showed discriminant analysis is an effective and operable method for disaster risk prediction. This model can provide timely information for decision makers to make effective measures for drought disaster management and to reduce the drought effects to yields at the minimum level.     

Dynamic risk assessment of drought disaster for maize based on integrating multi-sources data in the region of the northwest of Liaoning Province, China

The traditional studies on drought disaster risk were based on the ground point data, which were unable to realize the continuity of space and the timeliness. It is shown that the monitoring and evaluation precision on drought were reduced significantly. However, remote sensing data in adequate spatial and temporal resolution can overcome these limitations. It can better monitor the crop in large area dynamically. This study presents a methodology for dynamic risk analysis and assessment of drought disaster to maize production in the northwest of Liaoning Province based on remote sensing data and GIS from the viewpoints of climatology, geography and disaster science. The model of dynamic risk assessment of drought disaster was established based on risk formation theory of natural disaster, and the expression of risk by integrating data came from sky, ground and space. The risk indexes were divided into four classes by data mining method, and the grade maps of drought disaster risk were drawn by GIS. It is shown that the spatial and temporal risk distributions of maize at each growth stage changed over time. The model has been verified against reduction in maize yield caused by drought. It demonstrated the reasonability, feasibility and reliability of the model and the methodology. The dynamic risk assessment of regional drought disaster for maize can be used as a tool, which can timely monitor the status (the possibility and extent of drought) and trends of regional drought disaster. The results obtained in this study can provide the latest information of regional drought disaster and the decision-making basis of disaster prevention and mitigation for government management and farmers.     

基于GIS与信息量模型的汶川次生地质灾害危险性评价

5·12汶川大地震诱发了大量滑坡、崩塌、泥石流等次生地质灾害,对人民群众的生命财产和社会经济的发展形成了严重威胁.针对次生地质灾害危险性评价,选取重灾区汶川县作为研究区域,利用遥感与地理信息技术的空间数据管理和空间数据分析平台,获取了研究区的次生地质灾害信息,分析了研究区内次生地质灾害与各影响因子,包括地形地貌、地层岩性、水系、地震断裂之间的相关性特征,并结合信息量法模型进行次生地质灾害危险性评价.高度、中度和轻度危险区的面积分别为1 130.196 km2、1 739.584 km2、1 213.219 km2.本次地震触发次生地质灾害的分布具有集群式分布的特点,即断裂带及其附近地区地质灾害集中发育,而远离断裂带区地质灾害很快衰减,呈零星分布;从灾害发育的区域特征分析,汶川县震后次生地质灾害呈现出北部和东部重、西部和南部轻的特点.所得研究结果与实际情况较吻合,表明地理信息系统结合信息量模型能够快速、有效地对次生地质灾害的空间分布以及危险性作出评价.      

GIS-based flood risk assessment in suburban areas: a case study of the Fangshan District,Beijing

Suburban areas have become rapid development zones during China’s current urbanization. Generally, these areas are also regional precipitation centers that are prone to flood disasters. Therefore, it is important to assess the flood risk in suburban areas. In this study, flood risk was defined as the product of hazard and vulnerability based on disaster risk theory. A risk assessment index system was established, and the analytic hierarchy process method was used to determine the index weight. The Fangshan District in Beijing, China, which is an example of a typical suburban area undergoing rapid urbanization, was selected for this study. Six factors were considered in relation to hazard, and three factors were considered for vulnerability. Each indicator was discretized, standardized, weighted, and then combined to obtain the final flood risk map in a geographical information system environment. The results showed that the high and very high risk zones in the Fangshan District were primarily concentrated on Yingfeng Street, Xingcheng Street, Xincheng Street, and Chengguanzhen Street. The comparison to an actual flood disaster suggested that the method was effective and practical. The method can quantitatively reflect the relative magnitude and spatial distribution patterns of flood risk in a region. The method can be applied easily to most suburban areas in China for land use planning and flood risk management.     

Medium-scale natural disaster risk scenario analysis: a case study of Pingyang County, Wenzhou, China

A series of empirical studies involving typhoon rainstorm and flood risk scenario analysis were carried out on a medium spatial scale, covering Pingyang County. Considering a rainstorm/water-logging conversion process, active flooding submergence and per unit area values (million yuan/km²), two typical risk scenarios (50- and 100-year frequency) were simulated and analyzed. The study revealed that high-risk areas distributed across the towns of Aojiang, Qiancang and Xiaojiang, with a maximum submerged depth of 4.61 m for a 100-year flood hazard. In the case of a disaster loss rate >65 %, the potential maximum loss could be more than 10 million yuan/km². For medium-scale disaster risk, more attention must be paid to catastrophic events, which have a low probability of occurrence but would induce great losses. An amended risk formula could determine the degree of priority for responses to hazards of equal risk value better. In Pingyang County, the 50-year flood risk for Kunyang, Aojiang, Qiancang and Xiaojiang is greater than that of 100-year events for the next 50 years. However, these areas should give priority to their responses to 100-year disaster events during the next 100 years. In addition, the attention of disaster risk should vary in different spatial regions.     

单体滑坡灾害风险简易评价研究

按照灾害风险评价的一般框架,定性定量相结合,运用破坏概率、滑坡强度、承灾因子、遭遇特征与易损程度等5项指标,尝试提出单体滑坡灾害风险简易评价模型,并结合防治规划,提出了滑坡灾害风险管理对策建议。采用云南新平县滑坡灾害调查数据,开展了实例校验,结果表明,该模型基本反映了滑坡灾害的相对风险水平,可为现阶段管理提供更加明确的分级依据。同时,以期抛砖引玉,讨论险情调查与风险调查之间的关系。     

基于灾情数据的新疆雪灾的评估与区划

选取1955-2017年新疆因雪灾造成的牲畜受灾数量、作物受灾面积、经济损失三大灾情数据,揭示新疆雪灾时间和空间分布特征,通过构建雪灾灾损指数,对新疆雪灾进行区划。分析表明：新疆雪灾的发生北疆多于南疆,山区多于盆地,西部多于东部,主要集中发生在阿尔泰山区、天山山区及昆仑山区,灾情最重的是阿勒泰地区、伊犁地区、塔城地区;进入2000年以后,雪灾呈现明显高位震荡特点;利用多指标综合评估法,运用连续型随机变量分布密度近似求法,求得的新疆雪灾灾损指数,能客观反映新疆雪灾灾情;雪灾灾损指数遵从Γ分布,根据Γ分布函数属性及各类雪灾事件发生概率,进行了新疆雪灾等级区划。研究成果可为决策服务及气候可行性论证提供理论依据,对科学准确地制定防灾备灾措施、及时组织开展雪灾应急救助工作以及灾后的恢复重建决策给予一定参考。     

基于栅格径流汇流模拟的西藏林芝市泥石流灾害预警模型初探

西藏林芝市泥石流灾害频发,亟需建立泥石流灾害预警模型,预测林芝市泥石流灾害可能发生的区域,减少泥石流灾害导致的损失。文章提出了一种基于栅格径流汇流的林芝市泥石流灾害预警模型,从栅格像元尺度上模拟流域各位置上的水深,以提高泥石流预警的空间针对性。该模型将泥石流致灾因子分为背景因子和激发因子。通过林芝市裸岩率、河床纵比降等因子的逻辑回归,获取林芝市泥石流灾害概率,作为泥石流预警模型的背景因子;引入栅格径流汇流模型,以站点降水和雪水当量为模型的水量输入,模拟预警时段内的流域各位置上的模型水深,作为泥石流预警模型的激发因子。利用二元逻辑回归的方法计算背景因子和激发因子的权重,建立泥石流预警模型。利用2011—2020年18次历史灾害对模型进行验证,落入预警区内的灾害点占比64.4%,预警精度较高,对于林芝市泥石流灾害预警具有一定的指导意义。     

澜沧江流域重大水电工程扰动灾害风险评价

地质灾害风险性评价对当地防灾减灾具有指导意义。本文以澜沧江重大水电工程扰动灾害为例,在遥感解译与野外实际调查的基础上,选取高程、坡度、坡向、植被归一化指数、距库区距离、工程地质岩组、断裂带密度、年均降雨量、地震峰值加速度9个因素,并基于加权信息量模型进行危险性评价,然后以人口密度、水电站、道路、土地覆盖类型和GDP为承灾体进行易损性评价,最后将危险性和易损性进行信息融合,构建地质灾害风险性矩阵,完成地质灾害风险性评价。评价结果表明：极高和高风险区主要分布在乌弄龙及其上游水电站附近,以及下游库区两岸人类活动相对密集区域,中风险区主要分布在乌弄龙上游库区两岸以及乌弄龙—托巴水电站全域,在下游零散分布;低风险区主要分布在中游高山峡谷段。本次研究较为准确地评估了地质灾害风险性,可为澜沧江流域扰动地质灾害风险规划提供科学依据和技术指导。     

Integrated risk assessment of multi-hazards in China

Maps of population exposure, vulnerability and risk to natural hazards are useful tools for designing and implementing disaster risk mitigation programs in China. The ranking of provinces by relative risk to natural hazards would provide a metric for prioritizing risk management strategies. Using provinces as our study unit, from the perspectives of hazard exposure, susceptibility, coping capacity and adaptive capacity, this study first constructed China’s disaster risk index for five types of major natural hazards: earthquakes, floods, droughts, low temperatures/snow and gale/hail. Then, the relative risk level at the provincial scale in China was assessed. Finally, the hotspots with the highest hazard exposure, vulnerability and risk were identified. The results showed that high exposure was a significant risk driver in China, whereas high vulnerability, especially social vulnerability, amplified the risk levels. Similar to the population exposure to disasters, the relative risk levels in the southwestern, central and northeastern regions of China were significantly higher than those in the eastern, northern and western regions. The high-risk regions or hotspots of multi-hazards were concentrated in southern China (less-developed regions), while the low-risk regions were mainly distributed in the eastern coastal areas (well-developed regions). Furthermore, a nonlinear relationship existed between the disaster risk level and poverty incidence as well as per capita GDP, demonstrating that disaster losses in middle-income areas are likely to increase if economic policies are not modified to account for the rising disaster risk. These findings further indicated that research on disaster risk should focus not only on hazards and exposure but also on the vulnerability to natural disasters. Thus, reducing vulnerability and population exposure to natural hazards would be an effective measure in mitigating the disaster risk at hotspots in China.     

陕西陇县李家下滑坡风险评价

在野外勘查及室内实验的基础上,应用滑坡风险评价方法,对李家下滑坡进行了较为完整的风险评价,包括滑坡失稳概率计算、承灾体承灾概率计算、承灾体易损性计算和承灾体价值核算,最后得出滑坡总风险值,并将风险值用价值的形式量化表示。