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移动统计法土壤墒情诊断模型

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

Mobile statistical diagnostic model of soil moisture.

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

  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: 本文介绍了基于土壤初始含水量和时段每日平均降水量所建立的移动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。移动统计法墒情诊断模型分为时段模型和逐日模型。结果表明:时段模型预测的合格率为90%左右,逐日模型预测的合格率为70%左右;时段模型优于逐日模型。移动统计法模型单独作为墒情诊断模型,使用时需要进一步完善,可以在土壤初始含水量分段的基础上应用其他5个独立模型进行尝试。

关键词: 城镇化质量, 资源利用, 包容性, 协调性

Abstract:

The principle and modeling method of movable statistical diagnostic model of soil moisture based on soil initial water content and time period daily average precipitation were introduced. Model was established by the data of 87 monitoring sites in 23 counties in 7 provinces during 2012-2014, and validated by the data of 2015. The movable statistical diagnostic model includes time interval model and daily time series model. The results showed that the qualification rate of the time interval model prediction was about 90%, and the qualification rate of daily time series model prediction was about 70%, indicating that time interval model was better than daily time series model. movable statistical diagnostic model needs to be further improved as a soil moisture diagnostic model. The soil initial water content and precipitation should be segmented separately and then modeled respectively with other models.
 

Key words: urbanization quality, inclusiveness., coordination, resource utilization