基于东北区吉林省监测数据的土壤墒情综合诊断模型验证

发布时间：2018-04-22 02:39

本文选题：土壤含水量 + 降水量　；参考：《生态学杂志》2017年12期

【摘要】：综合诊断模型是土壤墒情监测、诊断与预报的新方法,它包括6个独立模型和基于水分来源数量对土壤含水量判断的一些逻辑关系,6个独立模型分别基于墒情监测数据与邻近气象站降水量之间的定量关系诊断或预报土壤含水量。本文应用吉林省4个县/市的6个墒情监测点的数据验证综合诊断模型在东北半干旱和半湿润地区的应用精度和适应性,建模使用2013—2014年的数据,模型验证使用2015年的数据。为了模型更好地应用于实际,本文对每个监测点的模型参数均进行了校正。结果表明:按监测点建模情况下,不同气候和下垫面条件下的模型和参数不同;模型在东北区吉林省半干旱和半湿润地区具有较好的适应性,验证合格率为88%以上;降水量少的监测点的模型预测合格率高。墒情综合诊断模型在吉林省半干旱和半湿润地区应用精度较高,为模型在该地区应用提供了研究案例。
[Abstract]:Comprehensive diagnostic model is a new method for soil moisture monitoring, diagnosis and prediction. It includes six independent models and some logical relationships based on the quantity of water source to judge soil moisture content. The six independent models diagnose or forecast soil moisture content based on the quantitative relationship between soil moisture monitoring data and precipitation of adjacent meteorological stations respectively. In this paper, the data of 6 soil moisture monitoring points in 4 counties / cities of Jilin Province are used to verify the application accuracy and adaptability of the comprehensive diagnosis model in the semi-arid and sub-humid areas of northeast China. The data from 2013-2014 are used in the modeling and the data of 2015 are verified by the model. In order to better apply the model to practice, the model parameters of each monitoring point are corrected in this paper. The results show that the models and parameters are different under different climates and underlying conditions according to the monitoring points, and the model has good adaptability in semi-arid and sub-humid areas of Jilin Province in Northeast China, and the qualification rate is over 88%. The qualified rate of model prediction for monitoring points with less precipitation is high. The comprehensive diagnostic model of soil moisture is applied to semi-arid and sub-humid areas of Jilin Province with high precision, which provides a case study for the application of the model in this area.
【作者单位】：农业部环境保护科研监测所;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);
【基金】：天津市科技支撑计划(15ZCZDNC00700) 广西科技开发项目(14125008-2-24) “中国农业科学院科技创新工程”(2016-cxgc-hyl) 全国农业技术推广中心节水处项目(2016-hx-hyl-5)资助
【分类号】：S152.7

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