Oil dispersal modelling: reanalysis of the Rena oil spill using open-source modelling tools

ABSTRACT

Oil spill forecast modelling is typically used immediately after a spill to predict oil dispersal and promote mobilisation of more effective response operations. The aim of this work was to map oil dispersal after the grounding of the MV Rena on Astrolabe Reef and to verify the results against observations. Model predictions were broadly consistent with observed distribution of oil contamination. However, some hot spots of oil accumulation, likely due to surf-zone and rip current circulation, were not well represented. Additionally, the model was run with 81 differing wind conditions to show that the events occurring during the grounding represented the typical likely behaviour of an oil spill on Astrolabe Reef. Oil dispersal was highly dependent on prevailing wind patterns; more accurate prediction would require better observations of local wind patterns. However, comparison of predictions with observations indicated that the GNOME model was an effective low-cost approach.     

Evaluating the impact of visualization of wildfire hazard upon decision-making under uncertainty

The paper investigates whether the methods chosen for representing uncertain geographic information aid or impair decision-making in the context of wildfire hazard. Through a series of three human subject experiments, utilizing 180 subjects and employing increasingly difficult tasks, this research evaluates the effect of five different visualizations and a text-based representation on decision-making under uncertainty. Our quantitative experiments focus specifically on the task of decision-making under uncertainty, rather than the task of reading levels of uncertainty from the map. To guard against the potential for generosity and risk seeking in decision-making under uncertainty, the experimental design uses performance-based incentives. The experiments showed that the choice of representation makes little difference to performance in cases where subjects are allowed the time and focus to consider their decisions. However, with the increasing difficulty of time pressure, subjects performed best using a spectral color hue-based representation, rather than more carefully designed cartographic representations. Text-based and simplified boundary encodings were among the worst performers. The results have implications for the performance of decision-making under uncertainty using static maps, especially in the stressful environments surrounding an emergency.     

Hispanic Immigrants' Vulnerabilities to Flood and Hurricane Hazards in Two United States Metropolitan Areas

Research reveals that disasters are disproportionately debilitating for marginalized social groups. Numerous studies have examined racial/ethnic dimensions of disaster vulnerability, but few have focused on Hispanic immigrants. More research on Hispanic immigrants is needed, since they constitute a major component of the Hispanic population—the largest and fastest‐growing minority group in the U.S.—and because they experience distinctive cultural and immigration status disadvantages. We examine the flood/hurricane vulnerabilities of Hispanic immigrants in comparison to U.S.–born Hispanics and non‐Hispanic whites. Using mixed methods to analyze data from 429 surveys and 31 interviews with residents living in flood zones, we examine differences in self‐protective action, risk perception, and hazard knowledge between the three groups in Houston and Miami. Hispanic immigrants exhibited lower levels of self‐protection and hazard knowledge, and higher perceptions of risk, which reflects their heightened vulnerability. Risk‐reduction programs should target the particular vulnerabilities of Hispanic immigrants, and future studies should examine their vulnerabilities in other contexts.     

Evaluation of coseismic landslide hazard on the proposed Haast-Hollyford Highway,South Island,New Zealand

Coseismic landsliding presents a major hazard to infrastructure in mountains during large earthquakes. This is particularly true for road networks, as historically coseismic landsliding has resulted in road losses larger than those due to ground shaking. Assessing the exposure of current and planned highway links to coseismic landsliding for future earthquake scenarios is therefore vital for disaster risk reduction. This study presents a method to evaluate the exposure of critical infrastructure to landsliding from scenario earthquakes from an underlying quantitative landslide hazard assessment. The method is applied to a proposed new highway link in South Island, New Zealand, for a scenario Alpine Fault earthquake and compared to the current network. Exposure (the likelihood of a network being affected by one or more landslides) is evaluated from a regional-scale coseismic landslide hazard model and assessed on a relative basis from 0 to 1. The results show that the proposed Haast-Hollyford Highway (HHH) would be highly exposed to coseismic landsliding with at least 30–40?km likely to be badly affected (the Simonin Pass route being the worse affected of the two routes). In the current South Island State Highway network, the HHH would be the link most exposed to landsliding and would increase the total network exposure by 50–70% despite increasing the total road length by just 3%. The present work is intended to provide an effective method to assess coseismic landslide hazard of infrastructure in mountains with seismic hazard, and potentially identify mitigation options and critical network segments.     

Rainfall flood hazard at nuclear power plants in India

Flood hazard evaluation is an important input for Nuclear Power Plants external events safety studies. In the present study, flood hazard at various nuclear sites in India due to rainfall has been evaluated. Hazard estimation is a statistical procedure by which rainfall intensity versus occurrence frequency is estimated from historical records of rainfall data and extrapolated with asymptotic extreme value distribution. Rainfall data needed for flood hazard assessment are daily annual maximum rainfall (24?h data). The observed data points have been fitted using Gumbel, power law and exponential distribution, and return period has been estimated. To study the stationarity of rainfall data, a moving window estimate of the parameters has been performed. The rainfall pattern is stationary in both coastal and inland regions over the period of observation. The coastal regions show intense rainfall and higher variability than inland regions. Based on the plant layout, catchment area and drainage capacity, the prototype fast breeder reactor (PFBR) site is unlikely to be flooded.     

基于高分二号卫星数据的煤矿区地质灾害信息提取研究

随着遥感数据获取技术和能力的全面提高,遥感数据呈现出明显的大数据特征。发展适应于遥感大数据的智能分析和信息挖掘技术,成为当前遥感技术研究的前沿。高分二号（GF-2）卫星数据是我国首颗自主研发的亚米级高分辨率卫星数据,具有观测幅宽、重访周期短、高辐射精度、高定位精度等优势,为未来我国地质灾害的长期、动态地监测和研究提供了高精度、稳定可靠的数据源。本文选取安徽谢桥煤矿2015年1月8日的GF-2卫星影像为研究数据,在对煤矿区主要地质灾害遥感地学分析的基础上,采用面向对象的影像分析方法对研究区由采煤活动所诱发的地质灾害信息进行自动提取。结果表明：利用GF-2卫星数据能够有效地识别地质灾害体的位置、范围、形态等空间分布特征;面向对象的自动提取方法对于煤矿区大面积的积水塌陷盆地、小规模的塌陷坑和线性的地裂缝都有很高的提取精度,识别精度达90% 以上;基于逐层剔除的思路构建的提取规则,为GF-2数据在地质灾害调查和大数据分析中的应用提供了很好的技术支持,也为其它地物目标的提取提供了参考,但在特征的选择和阈值的设定上需要具体分析。     

高亚洲地区冰崩灾害的研究进展

在全球气候变化的背景下,冰崩灾害极有可能成为人类面临的新常态。特别是在高亚洲地区,冰崩灾害事件严重威胁"亚洲水塔"的命运和"第三极"的生态安全。因此,研究冰崩灾害事件对于保障"一带一路"国家战略的顺利实施和保护"一带一路"沿线国家的生产与生存环境具有重要的现实意义。通过梳理历史上有记录的几次冰崩灾害事件,系统介绍冰崩的发生过程;再从冰崩体的物质组成、冰崩体的运动特征、冰崩发生的可能原因以及冰崩的影响等方面总结了冰崩的研究内容;重点阐述了冰崩的研究方法;最后讨论了当前冰崩研究存在的问题,并从冰崩研究方法等角度展望了未来冰崩灾害事件的研究方向。     

Modelling tropical cyclone risks for present and future climate change scenarios using geospatial techniques

Tropical cyclones and their devastating impacts are of great concern to coastal communities globally. An appropriate approach integrating climate change scenarios at local scales is essential for producing detailed risk models to support cyclone mitigation measures. This study developed a simple cyclone risk-modelling approach under present and future climate change scenarios using geospatial techniques at local scales, and tested using a case study in Sarankhola Upazila from coastal Bangladesh. Linear storm-surge models were developed up to 100-year return periods. A local sea level rise scenario of 0.34?m for the year 2050 was integrated with surge models to assess the climate change impact. The resultant storm-surge models were used in the risk-modelling procedures. The developed risk models successfully identified the spatial extent and levels of risk that match with actual extent and levels within an acceptable limit of deviation. The result showed that cyclone risk areas increased with the increase of return period. The study also revealed that climate change scenario intensified the cyclone risk area by 5–10% in every return period. The findings indicate this approach has the potential to model cyclone risk in other similar coastal environments for developing mitigation plans and strategies.     

基于逻辑回归的四川青川县区域滑坡灾害预警模型

四川省青川县滑坡灾害群发,点多面广,区域滑坡灾害预警是有效防灾减灾的重要手段,预警模型是成功预警的核心。由于研究区滑坡诱发机理复杂、调查监测大数据及分析方法不足等原因,传统区域地质灾害预警模型存在预警精度有限、精细化不足等问题。文章在青川县地质灾害调查监测和降水监测成果集成整理与数据清洗基础上,构建了青川县区域滑坡灾害训练样本集,样本集包括地质环境、降雨等27个输入特征属性和1个输出特征属性,涵盖了青川县近9年（2010—2018年）全部样本,数量达1 826个（其中,正样本613个,负样本1 213个）。基于逻辑回归算法,对样本集进行5折交叉验证学习训练,采用贝叶斯优化算法进行模型优化,采用精确度、ROC曲线和AUC值等指标校验模型准确度和模型泛化能力。其中,ROC曲线也称为“受试者工作特征”曲线;AUC值表示ROC曲线下的面积。校验结果显示,基于逻辑回归算法的模型训练结果准确率和泛化能力均较好（准确率94.3%,AUC为0.980）。开展区域滑坡实际预警时,按训练样本特征属性格式,输入研究区各预警单元27个特征属性,调用预先学习训练好的模型,输出滑坡灾害发生概率,根据输出概率分段确定滑坡灾害预警等级。当输出概率P≥40%且P<60%时,发布黄色预警;当输出概率P≥60%且P<80%时,发布橙色预警;当输出概率P≥80%时,发布红色预警。     

降雨型地质灾害预报方法研究进展

开展降雨型地质灾害预报是减少灾害损失的有效方法。该文在讨论降雨型地质灾害预报相关概念的基础上,结合国内外已有的研究成果,系统总结了隐式统计模型、显式统计模型和动力模型等降雨型地质灾害预报模型的特点和适用条件。近几年区域降雨型地质灾害的预报技术研究有以下新特点:统计模型简单实用,目前已经由单一考虑降雨特征的第1代隐式统计模型,进一步发展为考虑地质、地貌等静态因子的显示统计模型;动力模型逐渐由基于垂直入渗理论的边坡稳定性模型开始向基于水土耦合机制的复杂预报模型发展;降雨型地质灾害业务预报预警的核心是地质灾害预报模型的本地化运行,我国已经基于统计模型搭建了服务于不同区域的业务预报预警系统。结合多源预报降雨资料,搭建基于水土耦合机制的降雨型地质灾害集合预报预警系统是未来可能的发展方向。