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统计法土壤墒情诊断模型

黄治平1,王铄今2,侯彦林1,2,刘书田1,2*,郑宏艳1,丁健1,米长虹1,侯显达2   

  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

Statistical diagnostic model of soil moisture.

HUANG Zhi-ping1, WANG Shuo-jin2, HOU Yan-lin1,2, LIU Shu-tian1,2*, ZHENG Hong-yan1, DING Jian1, MI Chang-hong1, HOU Xian-da2#br#   

  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), Ministry of Education/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: 本文介绍了统计法土壤墒情诊断模型的原理和建模方法,该模型是基于时段降水量和土壤初始含水量以及二者的交互作用项建立的,并应用7个省23个县87个监测点2012—2014年的数据建模,以及2015年的数据进行验证。结果表明:统计法模型诊断和预测合格率较高,合格率达到85%以上;相邻墒情监测日的时间间隔有时不固定将导致异常值的出现,这是影响模型预测合格率的主要原因;逐日模型法可以实现逐日土壤墒情的预测。本研究表明,统计法模型可以单独作为墒情诊断模型使用。

关键词: 黑垆土, 有机碳, 互馈关系, 黄土高原, 全氮

Abstract: The principle and modeling method of statistical diagnostic model were introduced. The model is based on precipitation in different periods, soil initial water content and their interaction. Models were established by the data of 87 monitoring sites in 23 counties of 7 provinces during 2012-2014, and were validated by the data of 2015. The results showed that the statistical diagnostic model had high qualification rates (more than 85%) in diagnosis and prediction. The non-fixed time interval of the adjacent soil moisture monitoring date would lead to the emergence of outliers, which was the main reason affecting the qualification rate of model Prediction. Statistical diagnostic model based on daily time series data can predict daily soil water content. In conclusion, statistical diagnostic model can be used alone as a soil moisture diagnosis model.

Key words: black loessial soil, organic carbon, feedback relationships., Loess Plateau, total nitrogen