差减统计法土壤墒情诊断模型  

Subtractive statistical diagnostic model of soil moisture

作　　者：刘书田[1,2] 郑宏艳 王铄今[2] 侯彦林 丁健[1] 米长虹 黄治平[1] 侯显达[2] 

机构地区：[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　　处：《生态学杂志》2017年第12期3352-3358,共7页Chinese Journal of Ecology

基　　金：天津市科技支撑计划项目(15ZCZDNC00700);“中国农业科学院科技创新工程”项目(2016-cxgc-hyl);广西科技开发项目(14125008-2-24);全国农业技术推广中心节水处项目(2016-hx-hyl-5)资助

摘　　要：迄今为止,墒情诊断与预测模型由于缺乏通用性难以应用。本文介绍专栏6个独立模型中的差减统计法模型。差减统计法模型中2次监测的土壤含水量变化量为因变量,土壤初始含水量和时段降水量(含灌溉量)为自变量。应用7个省23个县87个监测点2012—2014年的数据建模,应用2015年的数据进行验证。结果表明:差减统计法模型诊断和预测合格率达90%左右,表明该模型适用性好;合格率高的主要原因是该模型遵循质量守恒定律和统计学规律;差减统计法预测误差主要来源于异地降水量数据和缺少灌溉记录数据。与传统模型相比,差减统计法具有以下特点:参数少、参数容易获得,参数具有统计意义、模型覆盖全部降水量范围、模型按点建模不受下垫面因素影响等。因此,差减统计法模型作为墒情诊断和预报模型是科学和实用的,可以单独使用。So far, the moisture diagnosis and prediction model, due to lack of versatility, is difficult to apply. This paper introduces the subtractive statistical model of the six independent models in the feature column of this issue. The change of soil water content between two times of monitoring is the dependent variable in the subtractive statistical method, and the initial soil water content and the precipitation （including irrigation quantity） are the independent variables. Models were established by the data of 87 monitoring sites in 23 counties from 7 provinces during 2012- 2014, and validated by the data of 2015. The results showed that the qualified rate of diagnosis and prediction of the subtractive statistical model was about 90%, indicating that the model was applicable. The main reason responsible for the high qualified rate was that the model followed the law of mass conservation and statistics. The errors of the subtractive statistical method were mainly derived from long-distance data of precipitation and lack of irrigation records. Compared with the traditional models, the subtractive statistic method has the following characteristics： less parameters, easy to obtain parameters, parameters having statistical significance, covering full scope of precipitation, and not affected by underlying surface factors. In conclusion, the subtractive statistical model is scientific and practical for the diagnosis and prediction model of soil moisture, and can be used alone.

关 键 词：土壤含水量 降水量 时段模型 逐日模型 验证 

分 类 号：S152.7[农业科学—土壤学]