Risk analysis system of geo-hazard based on GIS technique

The socio-economic attribute of geo-hazard made us distinguish it from the traditional engineering geology study. It will get more social benefit from the analysis of the geo-hazard in the socio-economic attribute. The hazard and the vulnerability of the element controls the risk level of the regional geo-hazard. The risk analysis supported by GIS in geo-hazard study is one of the most important directions. Based on the author’s studies in recent years, a risk analysis system of regional geo-hazard (RiskAnly) has been developed on the basis of software MAPGIS. The paper introduces the train of system design, the structure and the workflow of RiskAnly. As a case study, the paper also deals with the risk zonation of the regional landslide hazard of China.

Influence of adverse weather on drivers’ perceived risk during car following based on driving simulations

Adverse weather has a considerable impact on the behavior of drivers,which puts vehicles and drivers in hazardous situations that can easily cause traffic accidents.This research examines how drivers'perceived risk changes during car following under different adverse weather conditions by using driving simulation experiment.An expressway road scenario was built in a driving simulator.Eleven types of weather conditions,including clear sky,four levels of fog,four levels of rain and two levels of snow,were designed.Furthermore,to simulate the carfollowing behavior,three car-following situations were designed according to the motion of the lead car.Seven car-following indicators were extracted based on risk homeostasis theory.Then,the entropy weight method was used to integrate the selected indicators into an index to represent the drivers'perceived risk.Multiple linear regression was applied to measure the influence of adverse weather conditions on perceived risk,and the coefficients were considered as indicators.The results demonstrate that both the weather conditions and road type have significant effects on car-following behavior.Drivers'perceived risk tends to increase with the worsening weather conditions.Under conditions of extremely poor visibility,such as heavy dense fog,the measured drivers'perceived risk is low due to the difficulties in vehicle operation and limited visibility.

Early warning for geo-hazards based on the weather condition in China

According to national early warning practice for geo-hazards from 2003 to 2005,it is systematically concluded that the basic characteristics of geo-hazards,early warning method and forecast result based on the geological maps of China in a scale 1∶6 000 000.With the contrast of different characters between sustained rainfall and typhoon rainfall inducing geo-hazards,the disaster reduction result and some problems are preliminarily analyzed.Some basic recognition is that early warning to geo-hazards is feasible,national scale forecast is only to call attention,but can't immediately be used to disaster reduction decision-making.And,the future direction is to build a united disaster reduction framework of early warning system including national,provincial and county levels based on weather factors in different scale of area.

Financial Innovation Way of Avoiding Risk of Climate Disasters in Food Production:Weather Index Insurance

Climate change will lead to a variety of climate disasters, and climate disasters have a greater impact on China＇s food production. Weather index insurance is a new financial way to avoid risk of climate disasters effectively in China＇s food production. Firstly, the relationship between weather index insurance and food production in China was elaborated, and then the development status, advantages and disadvantages of weather index insurance in China at present were analyzed. Finally, some countermeasures against the problems of weather index insurance in China were put forward.

Evaluation of groundwater quality using pollution index of groundwater(PIG) and non-carcinogenic health risk assessment in part of the Gangetic Basin

The aim of this study was to evaluate groundwater fitness for domestic and irrigational use in Unnao district of Uttar Pradesh,India.For this study,block wise(n=16)groundwater samples were collected,and measured parameters were analyzed using the pollution index of groundwater(PIG),various irrigational indices,and graphical techniques.The results of this study suggested that most of the parameters were within the prescribed limits of WHO and BIS,excluding F-(0.4 to 2.6 mg L^(-1))and Fe^(2+)(0.1 to 1.7 mg L^(-1)).Concentrations of total dissolved solids(TDS)were exceeded the desirable limit(>500 mg L^(-1))in 43.75%of samples at some sites.The Gibbs plot revealed that groundwater chemistry was governed by rock-water interaction in the region,especially silicate weathering.The Piper plot suggested that Ca^(2+)-HCO3-is dominant hydrochemical facies in the area followed by mixed Ca^(2+)-Na^(+)-HCO_(3)^(-)type,Na^(+)-Cl^(-)type,and Na^(+)-HCO_(3)^(-)type.PIG evaluation revealed that the contribution of F-and Fe2+in groundwater degradation is high in comparison to other elements in the region,about 18.75%samples showed low pollution,while about 6.25%samples shows moderate pollution,and 6.25%samples reflected high pollution.The human health risk(HHR)assessment finding suggested that children(mean:1.36)are more vulnerable than adults(mean:1.01).Sodium absorption ration(SAR),Residual sodium carbonate(RSC),and Permeability index(PI)indicated that most of the groundwater was suitable for irrigation,whereas,Magnesium hazard ration(MHR)and Potential salinity(PS)indices suggested that only 37.5%and 56.25%of the samples were suitable for irrigational use,respectively.This regional study would help in decision making for stakeholders and relevant authorities in the execution of groundwater management and remediation plans in the area.

Weather index insurance for wind energy

As China vigorously promotes the development of new energy,photovoltaic power curtailment and wind power curtailment have been effectively resolved.At the same time,the yield from new energy power generation is becoming an important factor that affects the scale of investment in new energy.This paper focuses on the weather risks faced by wind power producers.By studying current research on weather index insurance in China and abroad,the functions and design methods for weather index insurance have been clarified.In addition,the feasibility of wind-power generation index insurance is discussed.The calculation methods for wind power generation index and the weather index insurance pricing methods for wind power enterprises are proposed.A weather index insurance model for wind power generation was established.The rationality and feasibility of the weather index insurance model proposed in this paper were verified using data from an existing power plant.The simulation results show that wind power enterprises can effectively avoid economic losses caused by weather risks through weather index insurance.

Study on Forecasting Method of Forest Fire Risk Grade in Putian City, China

From January 1, 2014, the basic stations of meteorological observation countries have changed from small evaporation observations to large-scale evaporation observations. National general weather stations have canceled observations on evaporation, but small evaporation is very important for forest fire risk prediction. In order to make the prediction of forest fire risk level objectively, weather data in Putian City, China and the multi-linear regression analysis method is used to calculate the daily evaporation amount in the more advanced SPSS16.0 software (English version), and the data of the last 5 years of each site are selected and fitted. Results showed that we accurately calculated the evaporation of the next day to make up for the lack of data due to the adjustment of the evaporation observation project. According to the forest fire risk weather index corresponding to many meteorological factors such as evaporation, temperature, humidity, sunshine and wind speed, the forest fire risk meteorological grade standard was designed to make a more accurate forest fire risk grade forecast.

极端天气场景下基于天气衍生品的电价风险管理

极端天气会导致线路跳闸,发电能力损失,负荷激增。市场环境下用户将承受高昂电价冲击,危及社会稳定。此时抑制电价飙升,管理电价风险十分重要。为此,设计了一种基于电价和发电损失容量的电力天气衍生品,可有效管理电价风险。首先,依据风险分解结构原理从源网荷及发电商报价角度分析了极端天气对电价的影响。其次,设计了考虑电价、天气为执行条件和基于损失容量与电价为收益函数的天气衍生品,并从理论层面分析了其效果。最后,结合预期效用最大化目标和电力市场出清模型进行发电商决策与电价模拟,使用条件风险价值(CVaR)指标评估了电价风险及衍生品效果。结果显示,所提电力天气衍生品使极端天气对发电商收入的冲击降低80%以上,用户电价均值降低约0.2%,用户电价风险降低20%以上,说明可有效控制电价风险,助力电力市场建设以及新型电力系统构建。

四川盆区油菜花期低温阴雨灾损评估及风险区划

花期低温阴雨是四川盆区油菜生产的主要气象灾害之一。本研究利用四川盆区1961-2020年101个气象站的逐日气象资料,1983-2000年油菜产量资料和干旱、低温冷害、连阴雨、大风、冰雹灾情资料,筛选花期低温阴雨灾害年份,采用数理统计方法获取花期低温阴雨产量灾损率和致灾因子,通过相关性分析确定油菜花期低温阴雨致灾指标,并利用2001-2020年灾情资料进行检验;基于筛选出的低温、连阴雨关键致灾因子,采用回归分析法建立油菜花期低温阴雨灾害损失评估模型,并进行回代和预测检验,分析灾害风险区划和变化趋势。结果表明:(1)日平均气温≤7℃、过程持续天数≥1d是四川盆区油菜花期低温致灾指标,日平均气温≤7℃的负积温和≥3d过程持续降水量共同组成低温阴雨灾害的关键致灾因子;选用2001-2020年油菜花期低温冷害发生情况对指标进行检验,与实际情况相符。(2)选用1983-2000年的气象、产量、灾情数据建立四川盆区油菜花期低温阴雨灾损评估模型,模型对轻度、中度灾害损失评估等级与实际等级相同或相差1级的评估准确率在96%以上;重度、特重评估等级与实际等级相同的准确率为0,与实际等级相差1级的准确率为75%和0。选用2001-2020年油菜花期低温阴雨灾害发生情况对模型检验,与实际情况基本相符。(3)1961-2020年四川盆区油菜花期低温阴雨灾害风险偏高区域主要分布于盆区西南部、南部及东北部,以中-高风险为主;灾害风险偏低的区域集中于盆区西北部及中部,以低-次低风险为主。(4)气候变暖背景下,四川盆区油菜花期低温阴雨灾害高风险区域呈减少趋势,低风险区域呈增加趋势。综上分析,四川盆区油菜花期低温冷害指标结果可靠,低温阴雨灾损评估模型能够较好地评估灾害损失,可应用于农业气象业务服务;灾害风险区划结果可为四川油菜生产布局提供参考依据。

灾害天气下计及一二次设备耦合故障的电网短时风险评估

灾害天气下电网发生短时多重故障有可能超出保护系统预设的反应能力,一二次设备耦合影响将助推多重故障风险发展和蔓延。为此,提出了计及一二次设备耦合故障的短时风险评估方法。首先,分析了一次设备与二次设备之间的交互作用及其对电网短时运行风险的影响。其次,构建了一次设备和二次设备的故障概率模型,重点分析了一二次设备耦合故障引发保护拒动的概率。然后,结合保护系统对超预期故障的反应能力,提出了灾害天气下电网短时多重故障风险评估方法。最后,采用改造的IEEE39节点系统对所提方法进行测试。算例结果表明:受外界气象环境影响,保护系统缺陷暴露导致非期望动作使得电网多重故障风险更为严重;综合考虑灾害天气对一二次设备影响下的短时运行风险,对于发现和消除保护系统薄弱环节及制定电网降风险运行策略具有一定参考价值。

基于危险天气不确定性的最小风险路径规划方法

为降低飞行过程中遭遇危险天气的概率,同时避免大范围绕飞导致的路径与耗油增加,针对航路中的雷暴、积冰和颠簸天气,使用数值预报和概率预报,面向航前飞行计划,提出一种基于危险天气不确定性的最小风险路径规划方法.首先,基于概率预报数据使用配料法和C-F模型计算雷暴发生概率,基于数值预报数据计算积冰预测指数和颠簸预测指数;然后,融合多类型危险天气,提出一种具备风险标识的栅格化地图;在此基础上,改进传统路径最短的规划算法,构建以风险最小化为目标的Dijkstra和A^(*)算法;最后,使用2023年4月3日华中地区强对流天气预测数据建立风险地图,使用上述改进算法与传统Dijkstra、A^(*)和RRT算法进行路径规划并对比分析.结果表明,传统Dijkstra和A^(*)算法可计算得到最短飞行路径,而改进的A^(*)算法可计算得到总风险最小路径;若综合考虑飞行风险与路径长度,改进的Dijkstra算法最为适合.

考虑气候风险耦合影响的中长期电力资源规划

考虑气候风险对电力供应和需求的影响进行电力系统气候适应性规划研究十分重要且紧迫。文中从规划视角开展电力系统气候适应性研究,构建考虑气候风险耦合影响的中长期电力资源规划模型,通过电力资源优化配置提升电力系统气候适应水平。结果表明:考虑气候长期变化趋势的规划方案中可再生能源发电量占比在装机容量减少的情况下反而有所增加,可再生能源利用水平提升,系统年均总投资建设成本下降;而考虑极端天气气候风险的规划方案中可控电源年均装机容量和发电量增加,以支撑可再生能源消纳和系统安全稳定运行,可再生能源年均发电量略有减少,系统年均总投资建设成本增加。考虑气候风险耦合影响的电力规划方案能够统筹提升可再生能源利用和系统安全水平,推动可再生能源高质量发展。

印度农作物天气指数保险计划的制度安排与政策启示

农作物天气指数保险因其在赔付效率、交易成本等方面的优势,被认为是传统农业保险产品的有益补充甚至是替代。在实施天气指数保险的国家当中,印度是最具有代表性的国家之一。本文在简要梳理印度农作物天气指数保险计划(RWBCIS)在其农业保险体系构建中的定位及其演进历程的基础上,系统阐述其产品设计、运营体系以及监督约束机制等具体措施和实施经验。研究发现,印度将RWBCIS定位为传统农作物产量险的有效补充,并结合地理位置、耕地分布及气候类型下的适用性,发挥二者的协同效应;RWBCIS的有序实施得益于完备的保险基础数据、健全透明的信息平台、精细化的产品设计和覆盖面广泛的气象网络的共同支撑;通过制定投保作物日历以及监督惩罚机制明确权责分配,提高保险市场的稳定性及可持续性。印度实施RWBCIS的政策思路及其在保险运行机制设计上的具体安排,或为中国农业保险顶层制度设计以及产品创新提供值得借鉴的启示。

我国本土气温指数期货产品的研发设计——源于京津冀“首都经济圈”核心功能区的气温数据

我国幅员辽阔,对天气变化非常敏感,迫切需要对天气风险进行有效管理,以消除或减少天气风险对各行业的不利影响。从产品研发设计视角出发,以京津冀“首都经济圈”核心功能区为例,参考国外成熟产品,对我国本土气温指数期货产品进行合约设计和模拟定价,根据行业需求阐述产品的应用价值,提出产品在天气风险管理中的显著作用。研究贡献有,搭建起我国本土气温指数期货产品的研发体系;基于国外成熟市场规则构建出符合我国国情的气温指数期货产品标准化合约框架;运用ARMA模型构建出精确度高的气温预测模型;采用蒙特卡罗模拟法制定出气温指数期货两款产品价格;明确产品的应用推广需求和风险管理作用。

安徽主要粮食作物天气指数保险模型研究与应用

气象灾害是农业生产面临的主要风险之一,给农业安全生产带来巨大损失。农业保险是农业保障体系的重要组成部分,是有效分散农业风险或弥补灾害损失的重要救灾手段之一。20世纪90年代后期出现的天气指数保险,因其客观、标准化和流通性强等特点在全球引起广泛关注、研究与应用。本文在回顾了国内外天气指数保险技术的发展历程基础上,详细分析了天气指数保险与传统农业保险的差异,以及各自的优势与劣势;以安徽粮食作物(冬小麦、一季中稻、夏玉米)生长过程中特定农业气象灾害的天气指数保险为例,介绍天气指数模型研制与产品开发。通过天气指数农业保险产品在安徽多年实践应用,探索天气指数模型的改进方法,以降低基差风险;并对天气指数保险未来发展进行探讨与展望。

输电线路气象风险精细化建模及气象灾害的在线预警防御策略

气象因素在输电线路稳定运行中起着重要作用,在评估风险时应予以考虑。为此,提出一种输电线路气象风险预警及防护方法。该方法考虑时空发电预测、设备健康和可靠性评估以及概率负荷预测等方面对气象风险进行精细化建模,并基于气象危害、电网脆弱性和灾后恢复成本提出一种新的风险度量标准。此外,针对负荷中断恢复以及缓解用电拥堵,提出一种气象灾害的在线预警防御策略。最后,在案例中对所提出的方法和策略进行测试分析,其结果可以验证方法和策略的有效性。

考虑长周期供需不平衡风险的新型电力系统规划方法

受高比例新能源并网带来的波动性和间歇性影响,新型电力系统的长周期供需不平衡矛盾日益突出。该文将电力系统的长周期供需不平衡风险分为两部分:连续多日无风无光的极端天气场景和月电量供需不平衡风险。首先,选取连续多日无风无光的极端天气场景,提出基于条件风险价值理论(conditional value at risk,CvaR)的月电量不平衡风险评估模型。在此基础上,提出考虑长周期供需不平衡风险的新型电力系统规划方法,通过季节性储能等灵活性资源的优化配置,可有效提升电力系统的长周期平衡能力。最后,基于IEEE RTS-79算例分析论证了所提方法的有效性,并初步讨论季节性储能在平抑长周期供需不平衡风险方面的作用。

基于海洋环境影响风险分析的海上搜救风险评估研究及典型海域示范应用

针对我国海上遇险事故快速增长的严峻态势,筛选出风、浪、能见度、海流4个影响海上搜救行动安全的主要影响要素,基于层次分析理论建立了海上搜救风险评估等级划分标准和海上搜救风险评估方法。根据我国海上专业救助船舶、飞机的性能参数计算其在复杂气象条件下实施海上搜救行动时所能应对的最大风险阈值,选取我国4处典型海域,使用统计学方法分别计算了海上搜救环境风险值。

天气数据驱动下基于深度主动学习的新型电力系统供需失衡风险快速评估方法

相较于极端气象,天气因素对电力系统供需平衡的冲击常被忽视,但多日无风无光等事件同样可带来保供问题。提出了一种天气数据赋能、深度主动学习赋智的新型电力系统供需失衡风险快速评估方法,较传统方法更高效、更准确。首先,考虑源网荷储多环节建立以天为尺度的电力系统生产模拟模型,以进行无风无光等异常气象下系统供需失衡的分析。同时,针对传统可靠性指标的不足,提出以天为尺度的新型分布式指标,并进一步利用风险曲线描述系统长期风险。然后,提出应用深度主动学习的电力系统风险快速评估框架,建立风险预测主网络和误差预测副网络耦合的双深度神经网络,并构建相应的损失函数和训练流程。最后,基于IEEE标准算例进行了效果验证与各方法对比,结果验证了其高效、准确、可拓展等特点。该研究为新型电力系统的风险快速评估提出了一种新颖有效的思路。