The Role of Disaggregation of Asset Values in Flood Loss Estimation: A Comparison of Different Modeling Approaches at the Mulde River,Germany

In loss estimation there is a spatial mismatch of hazard data that are commonly modeled on an explicit raster level and exposure data that are often available only for aggregated administrative units. Usually disaggregation methods that use ancillary information to distribute lumped exposure data in a finer spatial resolution help to bridge this gap. However, the actual influence of different mapping techniques and ancillary data on the final loss estimation has not been analyzed yet. In this paper three methods are applied to disaggregate residential building assets using two kinds of land use/land cover (LULC) data. The resulting disaggregated assets are validated and compared using census data of the residential building number on the community and constituency level. In addition, the disaggregated assets are taken to estimate residential building losses due to the flood in August 2002 in 21 municipalities on the River Mulde in Saxony, Germany. Losses are calculated with the help of four loss models. In general, disaggregation helps to decrease the error variance within the loss estimation. It must, however, be stated that the application of sophisticated disaggregation methods does not lead to significant improvements compared to the straightforward binary method. Therefore more effort should instead be put into the provision of high-resolution LULC data. Finally, the remaining uncertainties in loss estimation are high and demand further improvements in all modeling aspects.     

Invasive Species Research to Meet the Needs of Resource Management and Planning

Abstract: As zebra mussels (Dreissena polymorpha) continue to spread among inland lakes of the United States and Canada, there is growing interest from professionals, citizens, and other stakeholders to know which lakes are likely to be colonized by zebra mussels. Thus, we developed a classification of lake suitability for zebra mussels on the basis of measured or estimated concentrations of dissolved calcium in lake water and applied the classification to >11,500 lakes in Wisconsin and the Upper Peninsula of Michigan. The majority of lakes (58%) were classified as unsuitable (<10 mg/L Ca) for survival and reproduction of zebra mussels, 27% were identified as suitable (≥21 mg/L Ca), and 15% were classified as borderline suitable (≥10 and <21 mg/L Ca). Of the 77 inland lakes with confirmed zebra mussel records for which data on dissolved calcium were available, our method classified 74 as suitable and 3 as borderline suitable. To communicate this lake‐specific suitability information and to help prioritize regional efforts to monitor and prevent the expansion of zebra mussels and other invasive species, we developed a web‐based interface (available from http://www.aissmartprevention.wisc.edu/ ). Although we are still uncertain of how access to suitability information ultimately affects decision making, we believe this is a useful case study of building communication channels among researchers, practitioners, and the public.     

Predicting vehicle fuel consumption patterns using floating vehicle data

The status of energy consumption and air pollution in China is serious. It is important to analyze and predict the different fuel consumption of various types of vehicles under different influence factors. In order to fully describe the relationship between fuel consumption and the impact factors, massive amounts of floating vehicle data were used. The fuel consumption pattern and congestion pattern based on large samples of historical floating vehicle data were explored, drivers'' information and vehicles'' parameters from different group classification were probed, and the average velocity and average fuel consumption in the temporal dimension and spatial dimension were analyzed respectively. The fuel consumption forecasting model was established by using a Back Propagation Neural Network. Part of the sample set was used to train the forecasting model and the remaining part of the sample set was used as input to the forecasting model.     

太阳风暴对短波电子装备性能的影响及应对措施

目的掌握太阳风暴对短波电子装备性能的影响及应对措施,为系统设计提供参考。方法分析太阳风暴的表现形式,并从作用距离、目标检测、定位精度等方面给出太阳风暴对短波超视距雷达、短波通信等装备的影响。结果电离层SID、电离层暴可造成短波通信中断,短波通信可用频段变窄。电离层强吸收可降低天波超视距雷达作用距离和目标定位精度,电子浓度、电离层虚高快速变化影响超视距雷达检测性能和定位精度,负相电离层暴使天波超视距雷达可用频段严重变窄。地球磁暴期间,电磁场突变产生的强电压和电流有可能烧毁用于天、地波超视距雷达的电子设备。电离层非规则现象对超视距雷达有严重影响。结论太阳风暴对电子装备性能有利有弊,要分别对待。系统设计时应充分考虑太阳风暴的影响,在出现太阳风暴时,采取针对性措施降低其影响。     

CEMS比对监测数据偏差研究

简述CEMS比对监测技术背景,总结实际监测工作中导致数据偏差的各方面因素,分析问题产生原因并提出解决措施和建议。     

基于遥感监测的黄海绿潮漂移路径及分布面积特征分析

本文利用2008～2012年的多源卫星遥感序列图像,动态监测各年绿潮发生的漂移路径、影响海域面积,得到各年绿潮漂移趋势、分布面积和最大分布面积出现时间。根据监测结果,划分出影响绿潮漂移路径的关键区域,并分析了2008、2009和2010年关键区域内各年绿潮漂移方向和面积变化情况。     

基于DEA的上海市非中心城区工业生态效率研究

运用数据包络分析（DataEnvelopmentanalysis,DEA）的方法,从环境污染和资源消耗两个方面,建立了工业生态效率评价的指标体系,评价了2008年上海市非中心城区各区县的工业生态效率。结果表明,松江区处于上海市工业生态效率的前沿面,9个区县中有2／3区县的工业生态效率优于上海市平均水平。其中,崇明、嘉定和青浦处于规模报酬递增阶段,奉贤、闵行、浦东、金山、宝山则处于规模报酬递减阶段。最后,基于C2R模型对各区县的投入产出情况进行了优化调整,认为削减SO2的排放量和减少能源的消耗应是各区县提高工业生态效率的关键。     

舰载短波天线现存问题及对策

从多年工作经验出发,综述了某测量船舰载短波天线使用过程中出现的主要问题及潜在严重后果,从材料、结构等方面对其进行了深入分析,指出环境考虑不足、材料选择不当、结构存在缺陷是引发以上问题的根本原因。同时,结合工程实践,就如何在现有条件下提高舰载短波天线的可靠性,分别从如何选用合适材料、改进结构设计给出了意见建议。     

烟气排放连续监测系统的数据准确性研究

随着全国环境自动监控建设任务的完成,“十二五”期间要把探索环境监控数据的应用摆在突出位置,使环境监控数据在环境质量监管、排污费征收、污染物总量减排核查核算、环境执法和环境决策等方面发挥最大效能,最重要的是保证环境监控数据的真实性和准确性。CEMS作为废气自动监控系统,结合CEMS数据应用到环境管理的需求,探讨确保CEMS数据真实性和准确性应注意的事项,通过运用这些注意事项分析实时在线监控数据,能发现CEMS数据中存在的问题,提高CEMS数据的准确性和真实性。     

滁州市震害预测项目中地图数据处理方法研究

本文以滁州市震害预测项目为例,介绍了将DWG格式数据转换成SHAPE格式数据的方法和整个数据处理流程。总结了在数据处理过程中摸索出的数据错误检查和修改方法。