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。     

River channel network design for drought and flood control: A case study of Xiaoqinghe River basin, Jinan City, China

Vulnerability of river channels to urbanization has been lessened by the extensive construction of artificial water control improvements. The challenge, however, is that traditional engineering practices on isolated parts of a river may disturb the hydrologic continuity and interrupt the natural state of ecosystems. Taking the Xiaoqinghe River basin as a whole, we developed a river channel network design to mitigate river risks while sustaining the river in a state as natural as possible. The river channel risk from drought during low-flow periods and flood during high-flow periods as well as the potential for water diversion were articulated in detail. On the basis of the above investigation, a network with “nodes” and “edges” could be designed to relieve drought hazard and flood risk respectively. Subsequently, the shortest path algorithm in the graph theory was applied to optimize the low-flow network by searching for the shortest path. The effectiveness assessment was then performed for the low-flow and high-flow networks, respectively. For the former, the network connectedness was evaluated by calculating the “gamma index of connectivity” and “alpha index of circuitry”; for the latter, the ratio of flood-control capacity to projected flood level was devised and calculated. Results show that the design boosted network connectivity and circuitry during the low-flow periods, indicating a more fluent flow pathway, and reduced the flood risk during the high-flow periods.     

Climate dynamics and extreme precipitation and flood events in Central Europe

Past changes and possible future variations in the nature of extreme precipitation and flood events in Central Europe and the Alpine region are examined from a physical standpoint. An overview is given of the following key contributory physical processes: (1) the variability of the large-scale atmospheric flow and the associated changes of the North-Atlantic storm track; (2) the feedback process between climate warming and the water cycle, and in particular the potential for more frequent heavy precipitation events; and (3) the catchment-scale hydrological processes associated with variations in major river flooding events and that are related to land-use changes, river training measures, and shifts in the proportion of rain to snowfall. In this context an account is provided of the possible future forecasting and warning methodologies based upon high-resolution weather prediction and runoff models. Also consideration is given to the detectability of past (future) changes in observed (modeled) extreme events. It is shown that their rarity and natural fluctuation largely impedes a detection of systematic variations. These effects restrict trend analysis of such events to return periods of below a few months. An illustration using daily precipitation from the Swiss Alps does yield evidence for pronounced trends of intense precipitation events (return period 30 days), while trends of stronger event classes are not detectable (but nevertheless can not be excluded). The small detection probability for extreme events limits possible mitigation of future damage costs through an abatement of climate change alone, and points to the desirability of developing improved early forecasting/warning systems as an additional no-regret strategy. This revised version was published online in July 2006 with corrections to the Cover Date.     

防洪物资储备决策方法初探

根据防洪抢险物资储备属于一种信息不全型决策问题的特点,本文提出了一种用于确定每年防灾物资准备量概率排序决策模型,并以孙不河流泥石流灾害预报火灾决策为例进行说明。     

洪灾对柳州可持续发展的影响及减灾对策

通过对柳州市洪灾特点的分析,探讨了洪灾对柳州市经济、社会可持续发展的制约作用,提出洪灾的治理必须走可发展的道路,采用工程措施和非工程措施相结合的对策。     

对地观测技术在重大自然灾害监测与评估中的应用实例分析

对地观测技术可提供大范围、多时相、高分辨率的海量数据,用于自然灾害的监测与评估有其极大的优越性。本文叙述了对地观测技术在洪涝灾害、沙尘天气、森林火灾的监测与评估和地震形变场研究中的应用,并指出对地观测技术是建立数字减灾系统不可或缺的强大信息源。     

1961~2015年西南地区降水及洪涝指数空间分布特征

利用西南地区98个气象站连续完整的日降水序列数据,整合降水强度、持续性指数及等级指数形成降水指数体系并研究该区域降水及洪涝指数的空间分布特征,得到以下主要结论：（1） 1961~2015年,西南地区年降水量（PRCPTOT）与极端降水量（R95PTOT）都呈现出“东多西少、南多北少”的分布形态;持续降水日数（CWD）则表现为“南高北低、西高东低”的分布格局。区域多年平 均PRCPTOT、R95PTOT、CWD分别以-13.12 mm/10 a、1.34 mm/10 a、-0.29 d/10 a的速率变化。（2）西南地区不同等级降水日数具有相似的空间分布特征,均呈现出“南高北低、东高西低”的分布形态。（3）西南地区洪涝强度指数呈由东北向西南递减的分布特征;降水总量越多的地区,洪涝强度反而越低,主要由于单站洪涝强度表征的是降水的波动情况,降水量越多波动越不明显。21世纪以来,该 地区洪涝等级以重级为主,2010年以来连续多年出现特重级洪涝。此外,洪涝强度越大,区域性年度灾害等级越高。该研究对于掌握西南地区极端气候变化规律,从而服务于防灾减灾具有一定的理论意义。     

Probabilistic Flood Inundation Forecasting Using Rating Curve Libraries

One approach for performing uncertainty assessment in flood inundation modeling is to use an ensemble of models with different conceptualizations, parameters, and initial and boundary conditions that capture the factors contributing to uncertainty. However, the high computational expense of many hydraulic models renders their use impractical for ensemble forecasting. To address this challenge, we developed a rating curve library method for flood inundation forecasting. This method involves pre‐running a hydraulic model using multiple inflows and extracting rating curves, which prescribe a relation between streamflow and stage at various cross sections along a river reach. For a given streamflow, flood stage at each cross section is interpolated from the pre‐computed rating curve library to delineate flood inundation depths and extents at a lower computational cost. In this article, we describe the workflow for our rating curve library method and the Rating Curve based Automatic Flood Forecasting (RCAFF) software that automates this workflow. We also investigate the feasibility of using this method to transform ensemble streamflow forecasts into local, probabilistic flood inundation delineations for the Onion and Shoal Creeks in Austin, Texas. While our results show water surface elevations from RCAFF are comparable to those from the hydraulic models, the ensemble streamflow forecasts used as inputs to RCAFF are the largest source of uncertainty in predicting observed floods.