不同冠层阻力模型在夏玉米蒸散发计算中的优化应用
【目的】更精确地估算怀来地区夏玉米蒸散量（ET）。【方法】利用怀来站点2013年的气象数据与涡度相关数据，分别采用最小二乘法与蚁群算法优化冠层阻力Jarvis模型（JA模型）和耦合表层阻力模型（CO模型）中的经验参数，使用BP神经网络模型分析冠层阻力（rc）对各气象因子的敏感程度。再利用2014年的气象数据计算ET，并以涡度相关系统实测的ET为标准验证参数优化的结果。【结果】①rc对各影响因子敏感程度从大到小顺序为：Rn>LAI>θ>T>VPD。②使用蚁群算法优化的CO模型拟合rc结果最好（R2=0.89，RMSE=410.90 s/m，d=0.88）。③使用蚁群算法优...
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| Published in | Guanʻgai paishui xuebao Vol. 40; no. 6; pp. 28 - 35 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | Chinese English |
| Published |
Xinxiang City
Chinese Academy of Agricultural Sciences (CAAS) Farmland Irrigation Research Institute Editorial Office of Journal of Irrigation and Drainage
01.06.2021
四川大学,成都 610065%四川大学,成都 610065 南方丘区节水农业研究四川省重点实验室,成都 610066 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1672-3317 |
| DOI | 10.13522/j.cnki.ggps.2020450 |
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| Abstract | 【目的】更精确地估算怀来地区夏玉米蒸散量（ET）。【方法】利用怀来站点2013年的气象数据与涡度相关数据，分别采用最小二乘法与蚁群算法优化冠层阻力Jarvis模型（JA模型）和耦合表层阻力模型（CO模型）中的经验参数，使用BP神经网络模型分析冠层阻力（rc）对各气象因子的敏感程度。再利用2014年的气象数据计算ET，并以涡度相关系统实测的ET为标准验证参数优化的结果。【结果】①rc对各影响因子敏感程度从大到小顺序为：Rn>LAI>θ>T>VPD。②使用蚁群算法优化的CO模型拟合rc结果最好（R2=0.89，RMSE=410.90 s/m，d=0.88）。③使用蚁群算法优化后的CO模型模拟ET精度最高（R2=0.72，RMSE=1.07 mm，d=0.75）。【结论】Rn和LAI是影响夏玉米rc的主要因素，使用蚁群算法优化CO模型中的参数，可以获得精度最高的rc拟合结果和ET估计值，可为夏玉米精量用水提供理论依据。 |
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| AbstractList | S161.4; [目的]更精确地估算怀来地区夏玉米蒸散量(ET).[方法]利用怀来站点2013年的气象数据与涡度相关数据,分别采用最小二乘法与蚁群算法优化冠层阻力Jarvis模型(JA模型)和耦合表层阻力模型(CO模型)中的经验参数,使用BP神经网络模型分析冠层阻力(rc)对各气象因子的敏感程度.再利用2014年的气象数据计算ET,并以涡度相关系统实测的ET为标准验证参数优化的结果.[结果]①r c对各影响因子敏感程度从大到小顺序为:Rn>LAI>θ>T>VPD.②使用蚁群算法优化的CO模型拟合rc结果最好(R2=0.89,RMSE=410.90 s/m,d=0.88).③使用蚁群算法优化后的CO模型模拟ET精度最高(R2=0.72,RMSE=1.07 mm,d=0.75).[结论]Rn和LAI是影响夏玉米rc的主要因素,使用蚁群算法优化CO模型中的参数,可以获得精度最高的r c拟合结果和ET估计值,可为夏玉米精量用水提供理论依据. 【目的】更精确地估算怀来地区夏玉米蒸散量（ET）。【方法】利用怀来站点2013年的气象数据与涡度相关数据，分别采用最小二乘法与蚁群算法优化冠层阻力Jarvis模型（JA模型）和耦合表层阻力模型（CO模型）中的经验参数，使用BP神经网络模型分析冠层阻力（rc）对各气象因子的敏感程度。再利用2014年的气象数据计算ET，并以涡度相关系统实测的ET为标准验证参数优化的结果。【结果】①rc对各影响因子敏感程度从大到小顺序为：Rn>LAI>θ>T>VPD。②使用蚁群算法优化的CO模型拟合rc结果最好（R2=0.89，RMSE=410.90 s/m，d=0.88）。③使用蚁群算法优化后的CO模型模拟ET精度最高（R2=0.72，RMSE=1.07 mm，d=0.75）。【结论】Rn和LAI是影响夏玉米rc的主要因素，使用蚁群算法优化CO模型中的参数，可以获得精度最高的rc拟合结果和ET估计值，可为夏玉米精量用水提供理论依据。 |
| Author | LIN Xinbei LIANG, Chuan ZHOU, Gang ZHENG Zetao ZHAO, Lu |
| AuthorAffiliation | 四川大学,成都 610065%四川大学,成都 610065;南方丘区节水农业研究四川省重点实验室,成都 610066 |
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| Author_FL | LIN Xinbei ZHENG Zetao LIANG Chuan ZHAO Lu ZHOU Gang |
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| Copyright | Copyright Chinese Academy of Agricultural Sciences (CAAS) Farmland Irrigation Research Institute Editorial Office of Journal of Irrigation and Drainage 2021 Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| SubjectTerms | Ant colony optimization Back propagation networks Calibration Canopies Corn Covariance Evapotranspiration Meteorological data Neural networks Radiation Radiation measurement Sensitivity analysis Summer Surface resistance Vortices Weather stations Wind measurement Wind speed |
| Title | 不同冠层阻力模型在夏玉米蒸散发计算中的优化应用 |
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