淮北平原基于水文气象多因子的土壤水分动态预测
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摘要
为研究土壤水分动态变化,利用五道沟水文实验站1989-2015年水文气象和大田土壤水实测资料,采用灰色关联度和线性回归分析,建立了冬小麦各生长阶段不同土层土壤水分预测模型。结果表明:不同土层土壤水分与气象因子的关联度一致;不同生长阶段土壤水分与气温和地下水埋深关联度最强,分别达0. 92和0. 95;分蘖-越冬期,土壤水分与地下水埋深和日照时数关联度最强,其他生长阶段,土壤水分与气温和地下水埋深关联度最强。通过水文气象因子模拟土壤水分拟合度较高,R~2达0. 94。不同生长阶段不同土层,土壤水分计算模型均具有良好的预测能力,R~2达0. 80。成果为实施作物不同生长阶段的灌溉计划提供科学依据。
To study the dynamic changes of soil moisture,the gray correlation and multiple linear regression analysis were used based on hydrological meteorological and soil water measurements data from 1989 to 2015 at Wudaogou Hydrological Experiment Station,and Hydrological and meteorological factors and soil moisture prediction models in different soil layers at each growth stage of winter wheat were established. The results indicated that the correlation degree between soil moisture and meteorological factors in different soil layers at the same growth stage was the same. The correlation degree between soil moisture and temperature and groundwater depth was the strongest,with correlation coefficient 0. 92 and0. 95,respectively. During the tillering-overwintering period,the correlations between soil moisture and groundwater depth,sunshine hours was the strongest,while the correlation degree between soil moisture and temperature,groundwater depth was the strongest in other growth stages. The fitting of soil moisture by hydrometeorological factors was relatively high( R~2= 0. 94). The soil moisture calculation models have different prediction ability in different soil layers at different growth stages( R~2= 0. 80). This study provides a basis for the implementation of irrigation plans at different stages of crop growth.
To study the dynamic changes of soil moisture,the gray correlation and multiple linear regression analysis were used based on hydrological meteorological and soil water measurements data from 1989 to 2015 at Wudaogou Hydrological Experiment Station,and Hydrological and meteorological factors and soil moisture prediction models in different soil layers at each growth stage of winter wheat were established. The results indicated that the correlation degree between soil moisture and meteorological factors in different soil layers at the same growth stage was the same. The correlation degree between soil moisture and temperature and groundwater depth was the strongest,with correlation coefficient 0. 92 and0. 95,respectively. During the tillering-overwintering period,the correlations between soil moisture and groundwater depth,sunshine hours was the strongest,while the correlation degree between soil moisture and temperature,groundwater depth was the strongest in other growth stages. The fitting of soil moisture by hydrometeorological factors was relatively high( R~2= 0. 94). The soil moisture calculation models have different prediction ability in different soil layers at different growth stages( R~2= 0. 80). This study provides a basis for the implementation of irrigation plans at different stages of crop growth.
引文
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[2]孙秀邦,严平,黄勇,等.淮北地区土壤墒情动态预测[J].合肥工业大学学报(自然科学版),2007,30(9):1144-1147.
[3]CEPPI A,RAVAZZANI G,CORBARI C,et al. Realtime drought forecasting system for irrigation management[C]//Egu General Assembly Conference. Copernicus Publications,2014.
[4]Zh ANG Xiumei,PAN Yuchun,GAO Bingbo,et al. Preliminary study on soil moisture forecast of winter wheat[C]//International Conference on Computer and Computing Technologies in Agriculture. Springer,Berlin,Heidelberg:2011.
[5]张聪聪,陈效民,张勇,等.气象因子对太湖地区旱作农田土壤水分动态的影响[J].中国农业科学,2013,46(21):4454-4463.
[6]顾天真,徐云,张琪,等.基于气象因子的启东市土壤墒情预报研究[J].安徽农业科学,2016,44(34):174-176.
[7]舒素芳,钱华峰,邱小伟.基于气象因子的金华市土壤墒情预测模型[J].中国农业气象,2009,30(2):180-184.
[8]PRASAD R,DEO R C,LI Y,et al. Ensemble committee-based data intelligent approach for generating soil moisture forecasts with multivariate hydro-meteorological predictors[J]. Soil and Tillage Research,2018,181:63-81.
[9]HUANG Caojun,LI Lin,REN Souhua,et al. Research of soil moisture content forecast model based on genetic algorithm BP Neural Network[M]//Computer and Computing Technologies in Agriculture IV. Springer,Berlin,Heidelberg:2011.
[10]BAI Dongmei,GUO Zhongsheng,GUO Mancai. Application of wavelet analysis and narx neural network in the forecast of soil moisture[C]//Science and Engineering Research Center. International Conference on Advances in Management Science and Engineering. Destech Publications. 2015:145-149.
[11]LIU Min,HE Zhimin. Research and prediction of yellow soil moisture content in Guizhou province based on arima model[J]. Advanced Materials Research,2013,690-693:3076-3081.
[12]张和喜,杨静,方小宇,等.时间序列分析在土壤墒情预测中的应用研究[J].水土保持研究,2008,15(4):82-84.
[13]BHUIYAN H,MCNAIRN H,POWERS J,et al. Application of HEC-HMS in a cold region watershed and use of RADARSAT-2 soil moisture in initializing the model[J]. Hydrology,2017,4(1):1-19.
[14]白燕英,魏占民,刘全明,等.基于ETM+遥感影像的农田土壤含水率反演研究[J].灌溉排水学报,2013,32(4):76-78+102.
[15]张成才,吴泽宁,余弘婧.遥感计算土壤含水量方法的比较研究[J].灌溉排水学报,2004,23(2):69-72.
[16]彭致功,刘钰,许迪,等.基于遥感ET数据的区域水资源状况及典型农作物耗水分析[J].灌溉排水学报,2008,27(6):6-9.
[17]胡玮,严昌荣,李迎春,等.气候变化对华北冬小麦生育期和灌溉需水量的影响[J].生态学报,2014,34(9):2367-2377.
[18]刘荣花,朱自玺,方文松,等.冬小麦根系分布规律[J].生态学杂志,2008,27(11):2024-2027.