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应用生态学报 ›› 1999, Vol. 10 ›› Issue (3): 312-316.

• 研究论文 • 上一篇    下一篇

磷氮在水田湿地中的迁移转化及径流流失过程

晏维金1, 尹澄清1, 孙濮2, 韩小勇2, 夏首先2   

  1. 1. 中国科学院生态环境研究中心, 北京 100085;
    2. 安徽省水文总站, 合肥 230022
  • 收稿日期:1997-01-27 修回日期:1998-10-22 出版日期:1999-05-25 发布日期:1999-05-25
  • 通讯作者: 晏维金,男,33岁,副研究员,博士后,从事环境生物地球化学等学科研究.已在国际SCI刊物发表论文3篇,国内核心刊物发表论文6篇.
  • 基金资助:

    国家自然科学基金资助项目(49371062).

Phosphorus and nitrogen transfers and runoff losses from rice field wetlands of Chaohu Lake

Yan Weijin1, Yin Chengqing1, Sun Pu2, Han Xiaoyong2, Xia Shouxian2   

  1. 1. Research Center for Eco Environmental Sciences, Academia Sinica, Beijing 100085;
    2. Anhui Hydrology Service, Hefei 230022
  • Received:1997-01-27 Revised:1998-10-22 Online:1999-05-25 Published:1999-05-25

摘要: 水稻田湿地系统是我国东南部高产农业区的主要土地利用类型,是我国特有的景观结构。在巢湖六叉河小流域进行的野外实验结果表明,这一湿地系统的水塘、水沟和水稻田都能有效地截留来自村庄、森林地和旱地的磷氮非点源污染物。实验同时研究了磷氮物质从水稻田中的径流流失方式和机理,结果发现磷氮物质从水稻田中的径流流失量与水稻田持水量、施肥量、降雨量、水稻生长过程和水稻田排水堰高度等因素有关,并提出了一个模型计算磷氮径流流失量,表明在施肥情况下的磷氮流失量分别高达0.69和11.2kg·hm-2,是最大的潜在非点源污染。

关键词: 磷氮, 水稻田, 模型, 非点源污染, 大气污染, 大数据, 气候变化, 生态环境, 生态网络

Abstract: Rice field wetland system is the main land use type in the high product agricultural watersheds of Southerneast China. Field experiments show that rice fields, ditches and multipond systems can effectively retain nonpoint phosphorus(P) and nitrogen (N) pollution from different land uses in a subwatershed of Chaohu Lake. The mechanisms of Pand Ntransfers and runoff losses from rice fields are mainly studied. By the analysis of Pand Ndynamics in rice fields, it was found that Pand Nloads in runoff were depended on field water level, applied fertilizer amount, precipitation, rice growth process, and height of field overflow mouth. Asimple model was built to calculate the quantity of Pand Nloads in runoff from rice fields. It shows that the total loads can reach 0.69 and 11.2kg·hm-2 for Pand Nrespectively under the condition of applying fertilizers, which is the potential effect on Chaohu Lake eutrophication.

Key words: Phosphorus, Nitrogen, Rice fields, Model, Nonpoint pollution, big data, ecological environment, ecological network, climate change, air pollution