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徐莹莹,汤洁,祝惠,林英姿,金明兰,朱心悦.东北城市露水凝结观测及其与常规气象要素的关系.生态学报,2017,37(7):2382~2391 本文二维码信息
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东北城市露水凝结观测及其与常规气象要素的关系
Monitoring dew condensation and its response to conventional meteorological factors in an urban ecosystem of northeastern China
投稿时间:2016-01-12  最后修改时间:2016-07-09
DOI: 10.5846/stxb201601120074
关键词东北城市  露水凝结  暖干趋势  影响因子  多元线性回归
Key Wordsurban ecosystem  dew condensation  trend of warm and dry  impact factors  stepwise linear multi-variation regression model
基金项目国家自然科学青年基金项目(41401229);国家水体污染控制与治理科技重大专项(2012ZX07201004);吉林省教育厅"十三五"科学技术研究项目(2016161)
作者单位E-mail
徐莹莹 吉林建筑大学, 松辽流域水环境教育部重点实验室, 长春 130118;吉林大学资源与环境学院, 长春 130021 xuyingying.1019@aliyun.com 
汤洁 吉林大学资源与环境学院, 长春 130021  
祝惠 中国科学院东北地理与农业生态研究所, 长春 130102  
林英姿 吉林建筑大学, 松辽流域水环境教育部重点实验室, 长春 130118  
金明兰 吉林建筑大学, 松辽流域水环境教育部重点实验室, 长春 130118  
朱心悦 吉林建筑大学, 松辽流域水环境教育部重点实验室, 长春 130118  
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摘要:
露水是城市生态系统水循环组成部分,是重要的凝结水资源和湿度来源,为了揭示"全球变暖"对我国东北地区城市露水凝结的影响,于2014和2015年植物生长季对长春市绿地区、道路区及裸土区露水强度和气象因子进行监测和相关性分析。结果表明,东北城市绿地区露水强度与相对湿度、露点温度、气温、风寒温度、太阳辐射(n=254,P<0.01)正相关,与PM2.5、PM10、空气质量指数、夜间风速、大气压(n=254,P<0.01)负相关。东北城市年露日数为132-136 d,占无霜期的62.5%左右。绿地区是城市生态系统水汽凝结的主要区域,绿地区占市区面积的比例是城市年露水量的决定因子,长春市年露水凝结量约为23-35 mm,如城市绿地区所占比例降低至5%,年露水量基本可忽略不计。东北城市露水强度可通过I=(-5.9+0.156RH-0.86Vnight+0.117Rn)×10-2R2=0.857)模型进行模拟。结合研究区1965-2015年植物生长期夜间凝露段气候因子的变化趋势,判断东北城市生态系统露水量的变化率为-1.07 mm/10a(P<0.01)。在相对湿度、夜间风速和太阳辐射共同影响条件下,研究区气候变化对露水凝结影响不大。提供了城市不同下垫面露水监测及计算的方法,完善了不同生态系统露水监测体系,通过间接模型法构建了露水强度模拟模型,进一步明确了气候变化对近地表水循环的影响。
Abstract:
In northeastern China, over the last four decades, global warming has resulted in a decrease in precipitation and increase in temperature that have intensified evaporation, resulting in a decline in soil moisture. Dew is a crucial factor in the water and nutrient cycle of urban ecosystems; thus, could exert considerable influence on the water cycle by affecting the vapor condensation. To reveal the effects of global warming on dew variation in urban ecosystems, dew was monitored daily using poplar wooden sticks, asphalt blocks, and soil blocks in the complex urban landscapes of Changchun, northeastern China. A correlation analysis was conducted between meteorological factors and dew intensity in greenbelt areas of the urban ecosystem. The results indicated that dew intensity correlated positively with relative humidity, dew point temperature, air temperature, wind chill temperature, and solar radiation (n=254, P<0.01), whereas it correlated negatively with PM2.5, PM10, air quality index, nocturnal wind speed, and atmospheric pressure (n=254, P<0.01). During the monitoring period, there were 132-136 dew days per year, which accounted for 62.5% of the frost-free season in Changchun. Substrate plays a notable roll in the formation of dew, and at night, dew intensity showed different patterns among the various landscapes. The landscapes in descending order of average dew intensity were greenbelt (0.0607 mm), bare land (0.0100 mm), and roads (0.0049 mm) (P<0.01). Dew plays an important role in the greenbelt water balance and the dewfall in July, August, and September was equal to 22.52% and 23.61% of the rainfall for the same period in 2014 and 2015, respectively. According to the proportion of each landscape, dewfall was 23-25 mm/ac in Changchun. The results suggested that increased vegetation coverage could enhance the amount of water vapor available to condense on the ground surface. However, water could not condense if the proportion of the urban greenbelt area was reduced to 5%. Based on synchronous meteorological data, a stepwise linear multiple regression model was established to predict the dew amount. The model successfully revealed the relationship between simulated and measured dew intensities. The results suggested that a warmer and drier climate would not lead to a substantial reduction in dew. Combining the model and climate data for the study area from 1965-2015 during the dew condensation period, annual dewfall showed a decreasing trend of -1.07 mm/10 ac (P<0.01). However, under the mutual influence of relative humidity, wind speed, and solar radiation, the impact of climate change on dew condensation was not obvious. The present study developed a method for monitoring and calculating dew on different underlying surfaces of an urban ecosystem, and it improved the system of dew surveillance. The established empirical model could be used to predict dew intensity and help clarify the impact of climate change on the near-surface water cycle.
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