基于GIS的长沙市洪涝灾害风险动态评估

运用GIS技术的空间分析与建模功能构建洪涝灾害风险评估模型,以长沙市地理基础数据、遥感数据、气象水文、社会统计数据为基础,从洪涝灾害的危险性、暴露性、敏感性三个角度出发,对长沙市洪灾风险进行动态评价.结果表明：与1994年相比,2010年长沙市洪灾敏感性有所增加,暴露性上升,洪灾风险增大;从空间分布上看,长沙市区及望城区由于地势平坦,湘江干流贯穿其中,加之经济发达,因而洪灾风险较高;而西部的宁乡县和东部的浏阳市洪灾风险较低;提出了有针对性的防洪措施.该评价结果与实际情况相符,可为洪灾风险管理与决策提供科学依据.     

基于距离相关系数和支持向量机回归的PM2.5浓度滚动统计预报方案

针对目前空气质量统计预报方法存在的主要缺陷,本文提出了距离相关系数和支持向量机回归相结合的统计预报方案DC-SVR.利用淮安市2013年1—12月PM_(2.5)观测资料和常规气象观测资料,首先在选入预报当日气象要素的基础上,增加选取前期污染物和气象要素作为预报因子,再采用距离相关系数分季节从预报因子中筛选出重要预报因子,最后采用支持向量机回归对PM_(2.5)浓度值进行逐日滚动统计预报.研究发现,淮安地区气温和气压对PM_(2.5)的距离相关性要高于其他气象要素,夏秋季PM_(2.5)与气象要素的距离相关性较春冬季好.基于距离相关系数和支持向量机回归建立DC-SVR模型,PM_(2.5)的试预报值和实测值的全年相关系数高达0.76,平均偏差仅为1.13μg·m~(-3),平均绝对误差为23.47μg·m~(-3).通过与支持向量机回归、人工神经网络的统计预报效果对比,DC-SVR模型有效降低预报因子维数且能自适应选取最佳参数,预报精度显著优于其他3种统计预报方案,可为业务化预报提供参考.     

El Niño platforms: participatory disaster response in Peru

Climate change is expected to lead to greater extremes (droughts and floods) in river regimes around the world. While the number of major calamities is predicted to rise, the efforts of the public sector, experts and local stakeholders are badly coordinated. Consequently, aid does not reach target groups, resulting in unnecessary losses. Hence, there is a need for more participatory and integrative approaches. To ensure a more concerted response to climate-induced disasters, stakeholders could coordinate and negotiate within Multi-Stakeholder Platforms. Such roundtables are increasingly being established for vision-building and integrated water resource management, but could be employed in disaster management as well. After discussing the advantages and disadvantages of participation, this article trace the rise of and the problems facing two 'El Ni?o' platforms: one in Ica, a city on the Peruvian coast that flooded unexpectedly in January 1998, and one in Ayacucho, which saw a climate change-induced drought around the same time. The issue of internal and external legitimacy receives particular emphasis.     

Hurricane Harvey and Greater Houston households: comparing pre-event preparedness with post-event health effects,event exposures,and recovery

Most disaster studies rely on convenience sampling and ‘after-only’ designs to assess impacts. This paper, focusing on Hurricane Harvey (2017) and leveraging a pre-/post-event sample of Greater Houston households (n=71) in the United States, establishes baselines for disaster preparedness and home structure flood hazard mitigation, explores household-level ramifications, and examines how preparedness and mitigation relate to health effects, event exposures, and recovery. Between 70 and 80 per cent of participants instituted preparedness measures. Mitigation actions varied: six per cent had interior drainage systems and 83 per cent had elevated indoor heating/cooling components. Sixty per cent reported home damage. One-half highlighted allergies and two-thirds indicated some level of post-traumatic stress (PTS). Three-quarters worried about family members/friends. The results of generalised linear models revealed that greater pre- event mitigation was associated with fewer physical health problems and adverse experiences, lower PTS, and faster recovery. The study design exposed the broad benefits of home structure flood hazard mitigation for households after Harvey.     

西江溶解有机碳的输送对典型洪水过程的响应

对2005年6月西江干流出现特大洪水期间及其前后的溶解有机碳(DOC)进行连续采样分析.结果表明,DOC浓度对流量变化的响应不敏感(n=43,R2=0.23,p＞0.05),尽管流量有6～7倍的变化,但DOC浓度的变化不到30%.洪水前夕、洪水期间及洪水过后DOC平均浓度逐渐降低,且洪水过后西江下游各个采样点的DOC浓度明显低于洪水前夕.DOC的输出量主要受地表径流深度的影响,二者之间存在着显著的线性相关关系.DOC荷载通量与流量密切相关,通过建立二者之间的线性回归方程可以计算出DOC的输出量,洪水期间DOC的输出量为4.5×1010g.     

分形理论在三峡水库汛期洪水分期中的应用

水库分期汛限水位控制能在不增加新的防洪风险条件下提高水库的兴利效益,汛期洪水的划分则是分期汛限水位控制的前提.介绍了分形理论及容量维数的计算方法,以三峡水库宜昌站为例,利用传统统计学法和动态分维数法,确定了洪水分期数目,并计算了各分期的容量维数,从而最终确定三峡水库的洪水分期.结果显示,用分形方法划分的三峡水库的汛期洪水和传统研究分期基本一致,但分形理论计算较为客观,进一步表明分形理论在洪水分期中是可行的,值得深入探讨和应用.     

一种新的汛期降水集中期划分方法

汛期降水集中期是近期气象学者提出的表征汛期气候的一种新的特征量,它在气候研究中体现了较好的灵活性、客观性,通过对其分析,可为汛期气候的诊断和预测提供依据。但现在普遍使用的降水集中期在计算方法和时间长度上存在缺陷,特别是运用到时间跨度较长时段的气候分析时,特征量表征作用就有所缺失,而且计算方法较为复杂。为更好地使用降水集中期这一特征量,提出了以15天作为时长,用滑动统计来划定汛期降水集中期的新方法,并运用统计方法、天气气候学方法进行了论证,同时在长江下游主雨季降水集中期分析和金华地区汛期分析两个实例中进行了应用检验。结果表明,汛期降水集中期新方法划定的特征量与汛期降水总量存在时间上的相对独立性和总趋势上的显著相关性,且在汛期气候极端灾害事件上有较强的描述能力。因此认为,15天滑动统计新方法划定的汛期降水集中期使用便捷,天气气候意义明确,在实际应用中更为客观有效。     

基于BP神经网络的鄱阳湖水位模拟

考虑到鄱阳湖水位受流域五河与长江来水等多因素的共同作用而表现出高度非线性响应,采用典型的三层BPNN神经网络模型来模拟鄱阳湖水位与其主控因子之间的响应关系。分别将湖口、星子、都昌、棠荫和康山水位作为目标变量进行BPNN模型构建和适用性评估。结果显示：综合考虑流域五河及长江来水（汉口或九江）的BPNN水位模型,空间站点水位模拟精度（R2和Ens）可达090以上,各站点的均方根误差（RMSE）变化范围约050～10 m,若忽略长江来水的影响作用,仅将流域五河来水作为湖泊水位的主控影响因子,模型训练期与测试期的纳希效率系数（Ens）和确定性系数（R2）显著降低,且低于050,均方根误差（RMSE）也明显增大（124～288 m）,意味着综合考虑流域五河与长江来水是获取结构合理、精度保证的鄱阳湖水位模型的重要前提。同时建议针对鄱阳湖湖盆变化对水位的影响,尽可能选择一致性较好的长序列数据集来训练和测试BPNN模型。所构建的BPNN神经网络模型可进一步结合流域水文模型,用来预测气候变化与人类活动下流域径流变化对湖泊水位的潜在影响,也可作为一种有效的模型工具来回答当前鄱阳湖一些备受关注的热点问题,如定量区分流域五河与长江来水对湖泊洪枯水位的贡献分量,为湖泊洪涝灾害的防治和对策制定提供科学依据     

社会减灾能力信任及水灾风险感知的区域对比——基于江西九江和宜春公众的调查

研究公众对社会减灾能力及灾害风险的认识,有助于从公众视角揭示风险潜在因素,不仅是进行风险沟通的必备环节,还可以为开展有效的减灾宣传教育、提高公众减灾意识提供决策依据。通过社会调查（221份样本）和统计分析方法,比较了江西九江、宜春公众对于社会减灾能力的信任及水灾风险感知。结果表明,公众对于社会减灾能力基本持信任态度,信任度高低排序为：灾害监测预报>政府应急>防灾工程>预警传播,其中宜春公众的信任度较高;公众对于水灾的风险感知较弱,尤其是宜春公众认为水灾发生、受灾的可能性很小;公众的信任与对区域减灾能力的了解无关,主要是受到受灾经历（受灾次数、灾情损失、灾后救援）的影响,即区域本底灾害风险的高低导致公众认知的差异,风险较高区域（九江）的公众具有更为明确的降低风险的行为倾向及意愿。     

Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations

The new method for the forecasting hourly concentrations of air pollutants is presented in the paper. The method was developed for a site in urban residential area in city of Zagreb, Croatia, for four air pollutants (NO₂, O₃, CO and PM₁₀). Meteorological variables and concentrations of the respective pollutant were taken as predictors. A novel approach, based on families of univariate regression models, was employed in selecting the averaging intervals for input variables. For each variable and each averaging period between 1 and 97 h, a separate model was built. By inspecting values of the coefficient of correlation between measured and modelled concentrations, optimal averaging periods for each variable were selected. A new dataset for building the forecasting model was then calculated as temporal moving averages (running means) of former variables. A multi-layer perceptron type of neural networks is used as the forecasting model. Index of agreement, calculated for the entire dataset including the data for model building, ranged from 0.91 to 0.97 for the respective pollutants. As suggested by the analysis of the relative importance of the input variables, different agreements for different pollutants are likely due to different sources and production mechanisms of investigated pollutants. A comparison of the new method with more traditional method, which takes hourly averages of the forecast hour as input variables, showed similar or better performance. The model was developed for the purpose of public-health-oriented air quality forecasting, aiming to use a numerical weather forecast model for the prediction of the part of input data yet unknown at the forecasting time. It is to expect that longer term averages used as inputs in the proposed method will contribute to smaller input errors and the greater accuracy of the model.