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间隔天数统计法土壤墒情诊断模型

李敬亚1,侯显达2,侯彦林1,2,刘书田1,2*,郑宏艳1,米长虹1,黄治平1,丁健1#br#   

  1. (1农业部环境保护科研监测所, 天津 300191; 2北部湾环境演变与资源利用教育部重点实验室 (广西师范学院), 广西地表过程与智能模拟重点实验室 (广西师范学院), 南宁 530001)
  • 出版日期:2017-12-10 发布日期:2017-12-10

The interval days statistical diagnostic model of soil moisture.

LI Jing-ya1, HOU Xian-da2, HOU Yan-lin1,2, LIU Shu-tian1,2*, ZHENG Hong-yan1, MI Chang-hong1, HUANG Zhi-ping1, DING Jian1   

  1. (1Agro-Environmental Protection Institute, Ministry of Agriculture, Tianjin 300191, China; 2Key Laboratory of Environment Change and Resources Use in Beibu Gulf (Guangxi Teachers Education University); Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation (Guangxi Teachers Education University), Nanning 530001, China)
  • Online:2017-12-10 Published:2017-12-10

摘要:

本文介绍所建立的基于时段降水量、土壤初始含水量和两次监测时段天数的间隔天数统计法土壤墒情诊断模型的原理和建模方法,并应用7个省23个县87个监测点2012—2014年的数据建模,应用2015年的数据进行了验证。结果表明:间隔天数统计法模型诊断和预测精度高达到90%以上;间隔天数统计法模型诊断和预测合格率高,其原因是增加了间隔天数变量,有效地解决了测定间隔天数不固定的问题;逐日模型法可以实现逐日土壤墒情的预测。初步结论是:间隔天数统计法模型可以单独作为墒情诊断模型使用。

关键词: 景观格局, 脆弱度, 人为干扰度, 秦岭地区

Abstract:

The principle and modeling method of the interval days statistical diagnostic model of soil moisture were introduced. Models were established by the data of 87 monitoring sites in 23 counties from 7 provinces during the period of 2012-2014, and validated by the data of 2015. The results showed that the accuracy of diagnosis and prediction of the interval days statistical diagnostic model was high, reaching up to 90%. Interval days statistics diagnostic model had a high diagnosis and prediction rate because of the addition of the interval days variable, which effectively solved the problem of unfixed interval. The daily time series model could achieve daily prediction of soil moisture. In conclusion, the interval days statistical diagnostic model can be used alone as a soil moisture diagnosis model.
 

Key words: Qinling Mountains, vulnerability, human disturbance., landscape pattern