National vulnerability to extreme climatic events: the cases of electricity disruption in China and Japan

Extreme climatic events are likely to adversely affect many countries throughout the world, but the degrees among countries may be different. China and Japan are the countries with high incidences of extreme weather/disaster, both facing with the urgent task of addressing climate change. This study seeks to quantitatively compare the impacts of extreme climatic events on socioeconomic systems (defined as vulnerability) of the two countries by simulating the consequences of hypothetical same degree of electricity disruption along with extreme events. To do that, two computable general equilibrium models are constructed, by using which three-stage scenarios are simulated for China and Japan, respectively. The results reveal that China and Japan have unequal socioeconomic vulnerabilities to extreme events. (1) Negative impact of the same degree of power outages is bigger on China’s socioeconomic system than on that of Japan, and this difference is more obvious in the very short-run scenario. (2) The decline of China’s GDP, total output, and employment levels is 2–3 times higher than that of Japan, while the difference of the resident welfare levels is sharper, which of China drops 3–5 times of Japan. (3) Structural factors are the main reason for vulnerability differences between China and Japan, including the differences of expenditure structure, factor input structure for production of life requirement sectors, material and energy dependence for the production of industrial sectors, and usage structure of services outputs. Based on these findings, some policy implications and recommendations for fairness issues on climate change adaptation are proposed.     

A comparison of logistic regression-based models of susceptibility to landslides in western Colorado,USA

The Paonia-McClure Pass area of Colorado has been recognized as a region highly susceptible to mass movement. Because of the dynamic nature of this landscape, accurate methods are needed to predict susceptibility to movement of these slopes. The area was evaluated by coupling a geographic information system (GIS) with logistic regression methods to assess susceptibility to landslides. We mapped 735 shallow landslides in the area. Seventeen factors, as predictor variables of landslides, were mapped from aerial photographs, available public data archives, ETM + satellite data, published literature, and frequent field surveys. A logistic regression model was run using landslides as the dependent factor and landslide-causing factors as independent factors (covariates). Landslide data were sampled from the landslide masses, landslide scarps, center of mass of the landslides, and center of scarp of the landslides, and an equal amount of data were collected from areas void of discernible mass movement. Models of susceptibility to landslides for each sampling technique were developed first. Second, landslides were classified as debris flows, debris slides, rock slides, and soil slides and then models of susceptibility to landslides were created for each type of landslide. The prediction accuracies of each model were compared using the Receiver Operating Characteristic (ROC) curve technique. The model, using samples from landslide scarps, has the highest prediction accuracy (85 %), and the model, using samples from landslide mass centers, has the lowest prediction accuracy (83 %) among the models developed from the four techniques of data sampling. Likewise, the model developed for debris slides has the highest prediction accuracy (92 %), and the model developed for soil slides has the lowest prediction accuracy (83 %) among the four types of landslides. Furthermore, prediction from a model developed by combining the four models of the four types of landslides (86 %) is better than the prediction from a model developed by using all landslides together (85 %).     

A Bayesian vulnerability assessment tool for drinking water mains under extreme events

Drinking water security is a life safety issue as an adequate supply of safe water is essential for economic, social and sanitary reasons. Damage to any element of a water system, as well as corruption of resource quality, may have significant effects on the population it serves and on all other dependent resources and activities. As well as an analysis of the reliability of water distribution systems in ordinary conditions, it is also crucial to assess system vulnerability in the event of natural disasters and of malicious or accidental anthropogenic acts. The present work summarizes the initial results of research activities that are underway with the intention of developing a vulnerability assessment methodology for drinking water infrastructures subject to hazardous events. The main aim of the work was therefore to provide decision makers with an effective operational tool which could support them mainly to increase risk awareness and preparedness and, possibly, to ease emergency management. The proposed tool is based on Bayesian Belief Networks (BBN), a probabilistic methodology which has demonstrated outstanding potential to integrate a range of sources of knowledge, a great flexibility and the ability to handle in a mathematically sound way uncertainty due to data scarcity and/or limited knowledge of the system to be managed. The tool was implemented to analyze the vulnerability of two of the most important water supply systems in the Apulia region (southern Italy) which have been damaged in the past by natural hazards. As well as being useful for testing and improving the predictive capabilities of the methodology and for possibly modifying its structure and features, the case studies have also helped to underline its strengths and weaknesses. Particularly, the experiences carried out demonstrated how the use of BBN was consistent with the lack of data reliability, quality and accessibility which are typical of complex infrastructures, such as the water distribution networks. The potential applications and future developments of the proposed tool have been also discussed accordingly.     

气候变暖背景下极端气候对青海祁连山水文水资源的影响

利用青海祁连山区极端气候要素和青海湖、哈拉湖及主要河流的水文资料,研究表明：冷夜日数（10%）呈显著减少趋势,暖夜日数（90%）呈显著增加趋势;年大风日数显著减少;年降水量21世纪初增加趋势最为显著并发生突变,降水量增加幅度中西段大于东段;≥ 5 mm、≥ 10 mm、≥ 25 mm年降水日数呈显著增加趋势,进入21世纪后更为明显,而≥ 0.1 mm年降水日数呈减少趋势;年平均大风日数与湖泊水位、河流流量变化呈负相关,大风天气的减少,可以缓解湖面和土壤因蒸发而导致的水分损失,对植被的改善可增加径流的产生,流入湖泊的流量增加;降水量与湖泊水位、河流流量呈正相关,受21世纪降水量增加的影响青海湖水位逐年上升,共上升1.67 m,达到20世纪70年代末的水位,中西部主要河流流量近几年也达到最大值,而东段流量增加不明显;祁连山区≥ 5 mm、≥ 10 mm、≥ 25 mm年平均降水量与湖泊、河流流量变化呈正相关,各量级年降水量对湖泊水位、河流流量的增加贡献显著。     

A proposal for an approach to mapping susceptibility to landslides using natural language processing and machine learning

Landslides - Compiling an inventory is a fundamental step for carrying out assessments of landslide hazards. However, data in sufficient quantity and quality are not always available. Thus, this...     

Soil depth reconstruction for the assessment of the susceptibility to shallow landslides in fine-grained slopes

This paper proposes a methodology aimed at reconstructing the maximum thickness mobilized by shallow landslides in fine-grained soils with the aid of geological and geotechnical analyses. The methodology, implemented within a geographic information system (GIS) environment, is composed of two stages for map reconstruction and two stages for map validation. The first stage of map reconstruction is aimed at individuating the soil thickness on the basis of only topographical and geological analyses; the second stage improves the previously obtained map with the aid of morphological and geotechnical analyses that provide a thickness map usable for shallow landslide susceptibility assessment. This map is validated with the aid of both in situ investigations (stage I), and geotechnical models able to back-analyse shallow precipitation-induced landslides over a wide area (stage II). An application of the proposed methodology is provided for a test area of the Calabria region (southern Italy) that is representative of the Catanzaro Strait, where widely diffused shallow landslides in fine-grained soils systematically occur. The results highlight the usefulness and reliability of the geotechnical models when implemented with the aid of a database representative of fine-grained soils while a secondary role is played by in situ investigations that in the test site have been performed only in a few representative and accessible areas.     

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.     

Assessing landslide volume using two generic models: application to landslides in Whatcom County,Washington, USA

Landslides - Geomorphological analysis of landslide processes in mountainous terrains with difficult access has benefited from virtual representation of topography through the use of...     

Anisotropy of magnetic susceptibility analysis of deformed kaolinite: implications for evaluating landslides

Plane strain tests were performed on seven kaolinite blocks, each of which developed shear bands. Anisotropy of magnetic susceptibility (AMS) analysis of the kaolinite reveals a threshold degree of magnetic anisotropy (P′) exceeding which shear bands develop. Since P′ is a strain-intensity gauge and soils are known to develop shear bands prior to landsliding, it is concluded that soil in every landslide-prone region must have its unique threshold P′ exceeding which it develops shear bands before failing. Therefore, AMS monitoring of soil in landslide prone regions is proposed as a potential tool in the management of natural hazard zones.     

基于不同耦合模型的区域地质灾害易发性评价——以河南商城县为例

为探索效果最优的地质灾害易发性评价模型,以商城县为研究区,结合其孕灾地质条件与地质灾害发育特征,分析地质灾害影响因素,从地理环境、地质环境、人类活动3个方面选取高程、坡度、坡向、剖面曲率、植被覆盖率、工程地质岩组、断层、道路、水系9个因子构建评价指标体系,运用证据权(weights of evidence,WofE)模型、信息量(information value,IV)模型、层次分析-信息量(analytic hierarchy process-information value,AHP-IV)耦合模型分别进行了地质灾害易发性分析。研究表明: AHP-IV耦合模型下的受试者工作特征(receiver operating characteristic,ROC)曲线的线下面积(area under curve,AUC)值最大,评价效果更为准确,更适用于商城县地质灾害易发性评价。通过评价可知,商城县地质灾害极高易发区沿沟谷、道路呈条带状分布,高易发区呈团状包围在极高易发区边缘,中、低易发区多分布在地势平坦、岩性较单一的北部平原地区。研究成果可为当地的地质灾害防治管控工作提供科学依据,也可为类似区域的地质灾害易发性分区提供参考。     

A new region-based preparatory factor for landslide susceptibility models: the total flux

This paper presents a new region-based preparatory factor, total flux (TF), for landslide susceptibility models (LSMs). TF takes into account the topography and hydrology conditions upstream of each gridded data cell and represents the total flux of water in the stream. The results show that TF is strongly associated with the occurrence of landslides and is a good preparatory factor for LSM. Using TF instead of a drainage distance factor in I-Lan region in Taiwan shows an improvement in the accuracy of the cumulative percentage of landslide occurrence of 44 and 14 % for the top 1 and 10 % susceptible areas, respectively. This significant improvement in accuracy in these high-risk areas is critical for preventing and mitigating the economic and human losses due to landslides.     

Using an extreme learning machine to predict the displacement of step-like landslides in relation to controlling factors

In the evolution of landslides, besides the geological conditions, displacement depends on the variation of the controlling factors. Due to the periodic fluctuation of the reservoir water level and the precipitation, the shape of cumulative displacement-time curves of the colluvial landslides in the Three Gorges Reservoir follows a step function. The Baijiabao landslide in the Three Gorges region was selected as a case study. By analysing the response relationship between the landslide deformation, the rainfall, the reservoir water level and the groundwater level, an extreme learning machine was proposed in order to establish the landslide displacement prediction model in relation to controlling factors. The result demonstrated that the curves of the predicted and measured values were very similar, with a correlation coefficient of 0.984. They showed a distinctive step-like deformation characteristic, which underlined the role of the influencing factors in the displacement of the landslide. In relation to controlling factors, the proposed extreme learning machine (ELM) model showed a great ability to predict the Baijiabao landslide and is thus an effective displacement prediction method for colluvial landslides with step-like deformation in the Three Gorges Reservoir region.     

Use of an artificial neural network for analysis of the susceptibility to landslides at Boun,Korea

The purpose of this study was to develop landslide susceptibility analysis techniques using artificial neural networks and to apply the resulting techniques to the study area of Boun in Korea. Landslide locations were identified in the study area from interpretation of aerial photographs and field survey data. A spatial database of the topography, soil type, timber cover, geology, and land cover was constructed and the landslide-related factors were extracted from the spatial database. Using these factors, the susceptibility to landslides was analyzed by artificial neural network methods. The results of the landslide susceptibility maps were compared and verified using known landslide locations at another area, Yongin, in Korea. A Geographic Information System (GIS) was used to analyze efficiently the vast amount of data and an artificial neural network turned out to be an effective tool to analyze the landslide susceptibility.     

Assessment of susceptibility to landslides through geographic information systems and the logistic regression model

Natural Hazards - The increase in the frequency of natural disasters in recent years and its consequent social, economic and environmental impacts make it possible to prioritize areas of risk as an...     

A novel genetic algorithm for optimization of conditioning factors in shallow translational landslides and susceptibility mapping

Genetic algorithm (GA) is an effective approach in selecting the best factors without considering all possible combinations in landslide susceptibility mapping (LSM). The approach experienced a local optimal solution for hazard mapping. In this study, we propose a novel genetic algorithm (NGA) for solving the problems of optimal precision in selecting conditioning factors based on the crossover and mutation. In the southwestern part of China, including Wenchuan, Ludshan, and Ludian areas, the findings of this study confirm the applicability of NGA, which has a strong robustness compared to GA obviously. Results indicated that the highest area under curve (AUC) of GA is 93.47, 83.45, and 82.21% in Wenchuan, Lushan, and Ludian, respectively. Cumulative error of the precision (?R) is 3.19, 10.48, and 6.05%, and error of the highest precision (?P) is 0.01, 0.03, and 0.12% for Wenchuan, Lushan, and Ludian, respectively. Compared to the GA, the highest accuracy of NGA is 93.48% (Wenchuan), 83.48% (Lushan), and 82.28% (Ludian). It also revealed that ?R is 0.77, 1.26, and 1.82%, and ?P is 0.00, 0.04, and 0.05% for Wenchuan, Lushan, and Ludian, respectively. By comparing with GA, the novel approach of NGA has stronger robustness and higher accuracy on selecting the optimal conditioning factors of landslide. Additionally, the relationship of landslide occurrence with controlling factors was assessed in every study area. According to the results, lithology, distance to roads, elevation, and slope were regarded as the most effective factors for shallow translational landslides. These factors implied that internal structure and composition of rock, anthropogenic activity, and topography factors posed the main impacts on landslide occurrence. Finally, we implemented landslide susceptibility assessment in three study areas. Results showed that high landslide susceptibility was in the east and northeastern parts of Wenchuan; central region northward of Lushan; and southwest, central region, and west of Ludian.