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墒情诊断模型的评价

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

Evaluation of diagnostic models of soil moisture.

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

  1. (1Agro-Environmental Protection Institute, Ministry of Agriculture, Tianjin 300191, China; 2Key Laboratory of Environment Change and Resources Use in Beibu Gulf (Guangxi Tea-chers 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: 对6个模型进行系统评价的目的是为模型优选提供理论、方法和参数依据。采用优选模型比例、验证方法指标、验证方式指标和异常值指标4项指标对6个独立墒情诊断模型进行了综合评价。结果是:模型优先顺序为差减统计模型、间隔天数统计模型>移动统计模型>比值统计模型>统计模型>平衡模型;逐日模型好于时段模型。合格率高的模型包括4个特征,即自变量数量不超过3个、自变量相对独立、模型能处理不固定监测天数、人为不决定参数。按监测点建模的6个独立模型都可以单独使用,解决了模型不通用的问题;可以实现逐日诊断和预测,方便与遥感信息、作物长势信息等实时匹配;差减统计模型和间隔天数统计模型精度最高,前者是基于质量守恒定律的统计模型,后者有效地解决了间隔天数不固定所带来的预测误差。

关键词: 轮虫, 水环境, 水生植物, 群落结构

Abstract: The purpose of this paper was to evaluate the performance of 6 models so as to provide theoretical, methodological and parametric basis for model optimization. Six independent diagnostic models of soil moisture were evaluated by using 4 indexes including preferred model ratio, verification method index, verification mode index and outlier index. The results showed that the model priority was as follows: subtractive statistical diagnostic model and interval days statistical diagnostic model > movable statistical diagnostic model > ratio statistical diagnostic model > statistical diagnostic model > balance diagnostic model. For model type, the daily time series models performed better than the time interval models. The model with high qualification rate included 4 characteristics, that is, the number of independent variables was not more than 3, the independent variables were relatively independent, the model could deal with the uncertain monitoring days, and the parameters were not artificially determined. The six independent models established by the monitoring points can be used separately and solve the nonuniversal of the model. The daily diagnosis and prediction of soil moisture can be realized for real-time matching with remote sensing information and crop growth information. Prediction accuracy of the subtractive statistical diagnostic model and the interval days statistical diagnostic model were the highest, the former was a statistical model based on the law of mass conservation, and the latter effectively solved the prediction error caused by irregular interval days.

Key words: rotifer, water environment, community structure., macrophyte