巢湖沿岸圩区稻季营养盐的输出特征研究

巢湖流域沿岸分布着很多圩区,人们在圩内进行各种农业活动,圩区营养盐的输出是造成巢湖流域非点源污染的重要因素。为了解圩区农业非点源营养盐的输出特征,笔者通过在巢湖流域河网地区选择比较典型的圩区,于2014年水稻生长期进行了较系统的野外调查观测、取样和室内水质分析,探讨在自然降雨-径流的条件下圩区各类营养盐的浓度变化及输出特征。2014年稻季共进行了9次排水,排水量总计73.09 mm,径流系数为0.37,9次排水事件总氮(TN)和总磷(TP)的平均浓度分别是3.42、0.22 mg/L,圩区水体处于富营养化水平。降雨-径流强度是影响营养盐浓度变化的重要因素,稻季生长期内TN和TP的输出量是0.28、0.017 kg/hm2,占稻季施肥总量的1.7%和0.16%。径流量是影响营养盐输出总量的关键因素,同时施肥量和施肥至排水事件的间隔天数也是是影响营养盐输出的重要因素。     

基于气温预报和神经网络的参考作物腾发量预报

采用反向传播人工神经网络(BP-ANN)逼近气象因子-参考作物腾发量ET0函数关系,以天气预报中的最高和最低气温为输入进行短期ET0预报。收集了南京站实测的2010年7月1日至2013年7月7日逐日气象数据和2012年7月1日至2013年6月30日逐日对未来7d的气象预报数据,以最高、最低气温及相应的日序数为3个输入因子,ET0为输出建立一个包含一个隐含层的3层BP网络,以2010年7月1日至2012年6月30日实测气象数据及通过FAO-56PM公式计算的ET0进行网络,以2012年7月1日至2013年6月30日实测气象数据及通过FAO-56PM公式计算的ET0进行网络验证。将2012年7月1日至2013年6月30日逐日对未来7d的气象预报中的最高、最低气温输入训练及验证后的网络,得到2012年7月1日至2013年6月30日逐日对未来7d的ET0预报值,并与FAO-56PM公式计算的ET0值进行比较以验证预报精度。结果表明,预见期1~7d内,预报的ET0和计算的ET0变化趋势基本一致,预报精度随着预见期的增加而降低;平均准确率(±1.5mm/d以内)达88.08%,相关系数为0.77,均方根误差为1.28mm/d,显示出了较高的预报精度。在局部时间段内出现的ET0,PM和预报ET0的较大差别的原因是该时段内的ET0更多地受到除了日最高和最低气温之外的其他因素的影响。提出的方法 ET0预报,随着气象预报准确度的提高,可实现较为精确的ET0预报。     

面向农田物联网复杂事件处理的时空事件模型

在农田物联网中,控制命令往往由具有多维度信息的复杂事件触发,因此需要根据传感器所监测的大量单一事件检测复杂事件,即复杂事件处理。复杂事件模型描述了原子事件组合成复杂事件的组合模式,是复杂事件处理的基础。现有的复杂事件模型主要考虑原子事件的时间分布特性,而未考虑农田事件具有的空间分布特性。本文研究了面向农田物联网复杂事件处理的时空事件模型并定义了合适的事件模型描述语言,首先建立了描述复杂事件模式的时空事件模型,针对多个复杂事件间的关系,通过有向图建立了复杂事件层次模型;针对一个复杂事件内部关系,通过对农田物联网事件常见时序逻辑关系和空间拓扑关系的分析,定义了9类时间关系算符、8类空间关系算符用于判断子事件与复杂事件间的时空关系,并定义了5类时间耦合算符、7类空间耦合算符用于计算复杂事件的时空属性,为描述事件组合模式提供了基础;在此基础上,考虑可读性和易解析性,设计了基于XML的事件模型描述语言;基于案例,通过与其他常用的复杂事件模型相比较,说明了本文事件模型和描述语言更加适用于描述农田物联网复杂事件。     

大坝安全风险评估初探

根据风险理论在其它领域的应用实践，以及大坝安全风险管理在国外的应用经验，将大坝安全风险评估的原理、实施步骤等进行了阐述，探讨了大坝风险因素、失事树分析法及事件概率计算方法等诸多关键问题，并通过算例对文中所探讨的问题进行了较为明确的应用与说明。     

浅析气象夜间云的观测

随着气象现代化的飞速发展,很多项要素均已进入自动化观测或进入自动化与人工对比观测.云是重要的气象要素之一,且种类繁多,演变复杂,千变万化,云的变化又与天气紧密相连,对天气形势的变化有着极其奥妙的前瞻性和预见性,而夜间云的观测由于受多方面的条件限制,夜间云的准确观测就变成目测项目中最为困难的一个项目.     

Prediction of daily reference evapotranspiration by a multiple regression method based on weather forecast data

Prediction of daily reference evapotranspiration (ET ₀) is the basis of real-time irrigation scheduling. A multiple regression method for ET ₀ prediction based on its seasonal variation pattern and public weather forecast data was presented for application in East China. The forecasted maximum temperature (T _max), minimum temperature (T _min) and weather condition index (WCI) were adopted to calculate the correction coefficient by multilinear regression under five time-division regimes (10 days, monthly, seasonal, semi-annual and annual). The multiple regression method was tested for its feasibility for ET ₀ prediction using forecasted weather data as the input, and the monthly regime was selected as the most suitable. Average absolute error (AAE) and root mean square error (RMSE) were 0.395 and 0.522 mm d^?1, respectively. ET ₀ prediction errors increased linearly with the increase in temperature prediction error. A temperature error within 3 K is likely to result in acceptable ET ₀ predictions, with AAE and average absolute relative error (AARE) <0.142 mm d^?1 and 5.8%, respectively. However, one rank error in WCI results in a much larger error in ET ₀ prediction due to the high sensitivity of the correction coefficient to WCI and the large relative error in WCI caused by one rank deviation. Improving the accuracy of weather forecasts, especially for WCI prediction, is helpful in obtaining better estimations of ET ₀ based on public weather data.     

Surveys of cereal diseases and pests in the Netherlands. 2. Stem-base diseases of winter wheat

In the period 1974–1986, ca. 100 commercial winter wheat fields were surveyed annually for stembase diseases. In these years, on average 6% of the tillers was infected with eyespot in spring at the first-second, node stage. Eyespot intensity in spring was high in years with high temperature during winter. Eyespot, intensity in the milky-ripe stage in July, averaged 13% of the culms and was high in years with high temperature in April and high precipitation in March, April and May. These data may improve forecasts.In the milky-ripe stage, 2% of the culms were infected with sharp eyespot, but its intensity has gradually increased during the survey years. No significant correlation of sharp eyespot intensity with dry periods in autumn, spring or summer was found.Symptoms of take-all were found on 0.2% of the stem-bases during ripening. Its intensity was low in years with high precipitation in March, April, May and June and high temperature in May and June.Samenvatting Een honderdtal percelen wintertarwe werd in 1974–86 jaarlijks op voetziekten geïnventariseerd. In het voorjaar, tijdens het, eerste en tweede knoopstadium van het gewas, was gemiddeld 6% van de spruiten aangetast door oogvlekkenziekte. De intensiteit van de ziekte was hoog in jaren met een hoge gemiddelde wintertemperatuur. Tijdens het melkrijpe stadium, in juli was gemiddeld 13% van de halmen aangetast door oogvlekkenziekte. De aantasting in juli was hoog in jaren met een hoge temperatuur in april en veel neerslag in de maanden maart, april en mei. Met deze gegevens kunnen adviessystemen worden verbeterd.Scherpe oogvlekkenziekte was op gemiddeld 2% van de halmen in juli aanwezig. De ziekte nam geleidelijk met de jaren toe. De jaarlijkse intensiteit was niet gecorreleerd met droge perioden in de herfst, voorjaar of zomer.Symptomen van halmdoder waren op gemiddeld 0.2% van de halmen aanwezig. De intensiteit van de ziekte was hoog in jaren met weinig neerslag in maart, april, mei en juni en met een lage temperatuur in mei en juni.     

Surveys of cereal diseases and pests in the Netherlands. 4. Occurrence of powdery mildew and rusts in winter wheat

Results of annual surveys of winter wheat fields from 1974 to 1986 were compiled to describe epidemics of powdery mildew and rusts in relation to weather and cultivar resistance.An average of 29 and 70% of fields were infected by powdery mildew in May and July, respectively. Mildew prevalence in May was positively correlated with average temperature in October and with average temperature over the months December, January, February and March. In addition, it was correlated negatively with the average grade of mildew resistance of the cultivars sown each year. Prevalence of mildew in July did not show consistent correlations with weather characteristics nor with mildew prevalence in May.Yellow rust was usually not detected in May and on average 18% of the fields was infected in July. The occurrence of yellow rust decreased after 1977, when the farmers adopted cultivars resistant or moderately resistant to yellow rust.Brown rust was usually not detected in May, while in July on average 48% of the fields was infected. Brown rust intensity in July was high in years with a high March temperature and high precipitation during April and May.Black rust was rare in the Netherlands, with 3 and 1% of the fields infected in July 1977 and 1981, respectively.     

烟草气候斑病的发生规律及防治

１９９０－１９９４年研究结果表明，低温是诱发气候斑病的主要因素，不同品种抗性差异显著，氮钾比失调，偏施碳铵地发病严重，当大面积种植感病品种，寄主处于感病阶段，遇气温突然下降和多雨时，最易流行。在防治上，采取以农业防治为主、化学辅的有效措施。     

宁夏大风日数气候变化及其对沙尘天气的影响

本文利用宁夏23个气象台站1961—2004年共44a的大风日数、沙尘暴日数、扬沙日数资料,统计分析了宁夏大风日数的空间分布和时间分布特征,结果表明:近44a来宁夏存在2个大风中心———北部的惠农和东部的麻黄山,西南部的隆德大风日数最少,春季是宁夏各站大风日数最多的季节;20世纪60、70年代是宁夏大风日数明显偏多期,80、90年代是偏少期,21世纪初期比20世纪80、90年代略有增加;大风日数气候倾向率16个站为负值,7个站为正值;大武口———石炭井一带、麻黄山、兴仁、泾源是大风日出现的沙尘暴次数占其沙尘暴出现总次数的百分率高值区,陶乐———盐池一带、中卫是低值区,且大风中心区与沙尘暴频发区不重合;惠农、麻黄山是大风日出现的扬沙次数占扬沙出现总次数的百分率高值区,陶乐、永宁、盐池、中卫是低值区;年平均大风日数、年大风日出现沙尘暴次数及扬沙次数均呈减少趋势,时间曲线变化趋势也相同。