2000年全球气象灾害评价

2000年,全球气候持续较常年偏暖。赤道中,东太平洋地区强拉尼娜事件在1月再次达到峰值后,迅速减弱,强拉尼娜事件对全球,特别是对热带地区产生了较大的影响,北半球许多地区冬季出现严寒天气,夏季又遭热浪袭击,严重的高温、干旱、森林大火困扰全球。而亚洲南部、西欧、南部非洲、南美北部的许多地区暴雨频繁。估计气象自然灾害造成全球经济损失达上千亿美元。数亿人口受灾。     

汶川地震后绵竹、都江堰市房屋震害调查与分析

依据笔者在四川省绵竹市、都江堰市参加建设部组织的汶川地震震后房屋应急评估所取得的资料,对该次地震造成的建筑结构破坏进行了分析,从砖砌体结构、混凝土框架结构、构造问题以及设计与施工缺陷4个方面,论述了各种破坏的形态与成因,讨论了现行设计方法中存在的问题,提出了相应的改进建议。主要结论有:砌体结构的底层墙体强度不足、开间过大、形体复杂是导致其破坏的重要原因,在设计中,应适当提高砌体结构底层墙体的强度,控制房屋的开间,加强形体变化部位;砌体结构的窗下墙应作为连接墙肢的连梁考虑,可以将其设计成抗震设防的第一道防线;框架结构设计中,应考虑框架与填充墙的相互作用,考虑楼梯斜梁或斜板参与结构的整体受力;在现行规范要求的基础上,要适当增加框架柱的截面,节点附近的箍筋应采用焊接封闭箍或螺旋箍;应加强非结构构件的连接与锚固;对乡村房屋建设应予以监管,杜绝结构体系不明确的房屋出现。     

Relative Influence of Streamflows in Assessing Temporal Variability in Stream Habitat1

Abstract: The effects of streamflows on temporal variation in stream habitat were analyzed from the data collected 6‐11 years apart at 38 sites across the United States. Multiple linear regression was used to assess the variation in habitat caused by streamflow at the time of sampling and high flows between sampling. In addition to flow variables, the model also contained geomorphic and land use factors. The regression model was statistically significant (p < 0.05; R² = 0.31‐0.46) for 5 of 14 habitat variables: mean wetted stream depth, mean bankfull depth, mean wetted stream width, coefficient of variation of wetted stream width, and the percent frequency of bank erosion. High flows between samples accounted for about 16% of the total variation in the frequency of bank erosion. Streamflow at the time of sampling was the main source of variation in mean stream depth and contributed to the variation in mean stream width and the frequency of bank erosion. Urban land use (population change) accounted for over 20% of the total variation in mean bankfull depth, 15% of the total variation in the coefficient of variation of stream width, and about 10% of the variation in mean stream width.     

某油田稠油采出液生产分离器机械消泡技术研究

目的 针对某油田进行机械消泡技术研究,进而为促进分离器内稠油的快速消泡提供解决办法.方法 利用Fluent软件模拟不同导流板参数下分离器中气液分离效果及泡沫变化情况,并采用动态解析法发泡方式,利用高压溶气消泡测试系统对不同类型和型号的金属规整填料和两种化学消泡剂,进行消泡测试以及组合消泡实验.结果 导流板放置角度为45°时,分离效率和泡沫聚并效果均较好,而导流板放置距离影响不大;随着消泡填料的高度增加,消泡效果随之增加.结论 分离器入口导流板最佳放置角度为45°,孔板波纹填料SM250*12 cm为优选的机械消泡构件.实际生产采用机械消泡方法即可满足消泡需求,要求更好的消泡效果时,可将机械消泡与化学消泡剂消泡两种方式结合使用.     

日光温室夏季休闲期间的土壤管理对可溶性氮的影响

日光温室夏季休闲期间大水漫灌和高温闷棚是普遍的土壤处理措施,该过程灌水多、温度高,对氮素循环影响大.为了探明休闲期间土壤管理对氮素保持与损失的影响,通过田间试验揭示夏季休闲期间大水漫灌、高温闷棚对不同灌溉施肥模式(滴灌、漫灌)和不同有机物料还田处理(单施有机肥、有机肥配施小麦秸秆、有机肥配施玉米秸秆)土壤可溶性氮的影响.结果表明:作物收获后,滴灌和漫灌各处理平均w(矿质氮)分别为103.9和68.6 mg/kg,大水漫灌使滴灌0~30 cm土层w(矿质氮)显著降低30%,漫灌w(矿质氮)变化不大.日光温室夏季休闲期w(SON)(SON为可溶性有机氮)为16.3~69.1 mg/kg,SON相对含量为15%~48%.大水漫灌使滴灌和漫灌w(SON)分别显著增加2.9和2.5倍;高温闷棚使滴灌和漫灌w(SON)显著降低107.1和72.4 kg/hm2,降幅分别为41%和34%,同时w(矿质氮)分别显著增加117.9和126.7 kg/hm2,土壤氮素矿化速率分别为1.7和1.8 mg/(kg·d).与单施有机肥相比,长期有机肥配施玉米或小麦秸秆可显著增加滴灌w(矿质氮),但对w(SON)无影响.综上,休闲期间的土壤管理对土壤表层氮素含量的影响较大,其中大水漫灌容易造成滴灌残留氮素的大量损失,而随后的高温闷棚加速了SON的矿化.      

杨树人工林碳循环对淹水的响应

本文采用涡度相关方法对安徽怀宁杨树人工林的碳通量进行监测,得到微气象数据和通量数据。通过坐标旋转、密度校正和数据插补处理提高数据质量。选取2005年9月淹水期间的数据。分六个阶段进行分析。得到以下结果：（1）在半小时尺度上,在淹水期表观量子效率α随着土壤含水量SWC上升而降低;在未淹水期最大。为-0．0023μmolphoton^-1;退水期最低,为-0．00122mgCO2μmolphoton^-1,退水后恢复到-0,00168mgCO2μmolphoton^-1;α在淹水中2期降低速率最大,在整个淹水期间α的恢复速率是大于降低速率的。（2）生态系统总初级生产力（GEP）在淹水后不断降低．耒淹水期为8．0gCm^-2day^-1．退水后为4．7gCm^-2day^-1,在淹水中2期降幅是最大的达到了16％。（3）生态系统呼吸（Reco）在完全被水淹的第一天降到最低,仅为2．2gCm^-2day^-1;由于温度的影响其变化规律比较复杂。在淹水中2期的变化幅度是最大的;退水后Reco为4．2gCm^-2day^-1恢复到来淹水前的87％。（4）淹水期净生态系统CO2交换量（NEE）的变化表现为先上升后下降的趋势,在淹水中1期最大为-4．2gCm^-2day^-1。退水后最低。为0．6gCm^-2day^-1是未淹水前的17％。     

New policies to deal with climate change and other drivers impacting on resilience to flooding in urban areas: the CORFU approach

In the context of urban flood management, resilience is equal to resisting, recovering, reflecting and responding. The variety of causes of flooding and their consequences underpin the need for increased and internationally coordinated efforts to enhance technologies and policies for dealing with floods. This paper addresses this issue and presents some novel research ideas related to resilience to flooding in urban areas, which are under development within the EU FP7 project ‘Collaborative research on flood resilience in urban areas’ (CORFU). The approach adopted in this project aims to quantify the cost-effectiveness of resilience measures and integrative and adaptable flood management plans for different scenarios of relevant drivers: urban development, socio-economic trends and climate changes. It is believed that the way in which the different models are being put together, combined with the variability of conditions in case study areas in Asia and in Europe, will ultimately enable more scientifically sound policies for the management of the consequences of urban flooding.     

Risk,vulnerability, and pragmatic inevitability: the conflict–disaster nexus and urban governance in Johannesburg,South Africa

This paper spotlights post-disaster relief provision in Johannesburg, South Africa, following the floods of 2016 in a bid to explore how local government and non-governmental actors in the country conceive of compounding vulnerability and conflict within urban disaster governance. It reveals the diverse strategies employed to navigate violent conflict during the cyclical occurrence of disaster and reconstruction that the predominantly migrant population experiences in the Setswetla informal settlement, adjacent to the Alexandra township in northern Johannesburg. Rendered visible in moments of disaster and recovery are the spatial politics and multidimensional nature of conflict. These phenomena unfold across various levels of urban governance and in the affected community and effectively construct a disaster citizenship that makes risk reduction and community cohesion impossible in the eyes of disaster managers. This research, based on a set of expert interviews, integrates conflict and disaster studies to shed light on how the conflict–disaster interface materialises, and is operationalised, in an urban setting.     

Streamflow Hydrology Estimate Using Machine Learning (SHEM)

Continuity and accuracy of near real‐time streamflow gauge (streamgage) data are critical for flood forecasting, assessing imminent risk, and implementing flood mitigation activities. Without these data, decision makers and first responders are limited in their ability to effectively allocate resources, implement evacuations to save lives, and reduce property losses. The Streamflow Hydrology Estimate using Machine Learning (SHEM) is a new predictive model for providing accurate and timely proxy streamflow data for inoperative streamgages. SHEM relies on machine learning (“training”) to process and interpret large volumes (“big data”) of historic complex hydrologic information. Continually updated with real‐time streamflow data, the model constructs a virtual dataset index of correlations and groups (clusters) of relationship correlations between selected streamgages in a watershed and under differing flow conditions. Using these datasets, SHEM interpolates estimated discharge and time data for any indexed streamgage that stops transmitting data. These estimates are continuously tested, scored, and revised using multiple regression analysis processes and methodologies. The SHEM model was tested in Idaho and Washington in four diverse watersheds, and the model's estimates were then compared to the actual recorded data for the same time period. Results from all watersheds revealed a high correlation, validating both the degree of accuracy and reliability of the model.