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基于华北区山西省监测数据的土壤墒情综合诊断模型验证

郑宏艳1,丁健1,王铄今2,侯彦林1,2*,米长虹1,黄治平1,刘书田1,2,侯显达2#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 verification of integrated diagnostic model of soil moisture based on the monitoring data in Shanxi Province.

ZHENG Hong-yan1, DING Jian1, WANG Shuo-jin2, HOU Yan-lin1,2*, MI Chang-hong1, HUANG Zhi-ping1, LIU Shu-tian1,2, 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: 本文应用山西省2个市县的16个墒情监测点的数据验证土壤墒情综合诊断模型在华北区半湿润地区的适应性,建模使用2012—2014年的数据,模型验证使用2015年的数据。结果表明:模型在山西省半湿润地区具有较好的适应性,验证合格率为90%以上;由于2个监测县降水量接近,模型预测合格率与纬度、经度之间没有明显的规律。本研究表明,墒情综合诊断模型建模数据量大,数据范围涵盖了作物整个生长季的数据,相对于其他模型有一定的优势,可以应用于华北半湿润地区。

关键词: InVEST模型, 景观格局, 生境质量, CA-Markov模型, 驱动力, Logistic回归模型

Abstract: The data of 16 soil moisture monitoring sites in two counties in Shanxi Province were applied to verify the adaptability of integrated diagnostic model of soil moisture in semi-humid regions in North China. The model was established by the data of 16 monitoring sites in 2 counties in Shanxi Province during the period of 2012-2014, and was validated by the data of 2015. The results showed that the model had good adaptability in semi-humid regions of Shanxi Province, with the qualification rates being over 90%. There was no obvious pattern between the qualification rate of model prediction and latitude and longitude, because the precipitation in the two monitoring counties was close. In conclusion, the integrated diagnostic model used a large amount of data, and the data range covered the whole crop growing season. It has relative advantages over other models and can be applied to semi-humid areas of North China

Key words: InVEST model, CA-Markov model, habitat quality, Logistic regression model, landscape pattern, driving force.