| 基于BCC_CPSv2模式的淮河流域月参考作物蒸散概率订正预报 |
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| 引用本文： | 曹雯, 段春锋, 徐祥, 程智, 汪栩加. 基于BCC_CPSv2模式的淮河流域月参考作物蒸散概率订正预报[J]. 农业工程学报, 2022, 38(23): 61-69. DOI: 10.11975/j.issn.1002-6819.2022.23.007 |
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| 作者姓名： | 曹雯 段春锋 徐祥 程智 汪栩加 |
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| 作者单位： | 1.安徽省农业气象中心/安徽省农业生态大数据工程实验室，合肥 230031;2.安徽省气象科学研究所，安徽省大气科学与卫星遥感重点实验室，合肥 230031;3.安徽省气候中心，合肥 230031 |
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| 基金项目： | 国家自然科学基金（41405111）；国家自然科学基金（41605068）；中国气象局创新发展专项（CXFZ2021Z011）；安徽省气象局研究型业务科技攻关项目（YJG202005） |
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| 摘 要： | 参考作物蒸散（Reference Crop Evapotranspiration，ET0）预报在农业水资源配置、区域干湿演变评估方面有着重要作用。该研究基于国家气候中心第二代气候预测系统（Beijing Climate Center Second-Generation Climate Prediction System，BCC_CPSv2）模式预报数据和1991－2020年淮河流域地面气象观测数据，利用分位数映射法对模式预报的气象要素进行概率订正，采用Penman-Monteith公式计算ET0，并评估了订正前后BCC_CPSv2模式对淮河流域月ET0和气象要素的预报性能。结果表明：1）模式对平均气温、净辐射和相对湿度的预报值较观测值偏小，风速预报值在3－6月偏小，其他月份偏大，4个气象要素预报的均方根误差（Root Mean Square Error，RMSE）分别为1.84 ℃、1.70 MJ/m2d、15.79%和1.39 m/s；气象要素预报偏差导致2－6月ET0预报值较计算值偏小，1月和7－12月偏大，区域平均RMSE为0.59 mm/d，绝对百分比误差（Mean Absolute Percentage Error，MAPE）为21.9%。2）概率订正有效降低了气象要素和ET0的预报误差。气温、净辐射、相对湿度和风速预报订正值的RMSE均小于订正前；80%月份ET0预报订正值的RMSE小于订正前，区域平均RMSE减小了0.23 mm/d，MAPE减小了11.2%。3）夏半年和冬半年ET0预报误差的首要来源分别是净辐射和相对湿度，主要是由于模式对这2个要素的预报精度较低且ET0对其敏感，误差容易传递。可见，基于模式概率订正的月尺度ET0预报方法精度较高，可以为水资源优化管理、灌溉制度制定和农业中长期需水决策提供参考。
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| 关 键 词： | 蒸散 气候 模式 预报 概率订正 淮河流域 |
| 收稿时间： | 2022-09-01 |
| 修稿时间： | 2022-12-10 |
Probability correction in monthly reference crop evapotranspiration prediction in Huaihe River Basin using BCC_CPSv2 model |
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| Cao Wen, Duan Chunfeng, Xu Xiang, Cheng Zhi, Wang Xujia. Probability correction in monthly reference crop evapotranspiration prediction in Huaihe River Basin using BCC_CPSv2 model[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2022, 38(23): 61-69. DOI: 10.11975/j.issn.1002-6819.2022.23.007 |
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| Authors: | Cao Wen Duan Chunfeng Xu Xiang Cheng Zhi Wang Xujia |
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| Affiliation: | 1.Anhui Agrometeorological Center/Anhui Engineering Laboratory of Agro-Ecological Big Data, Hefei 230031, China;2.Atmospheric Science and Satellite Remote Sensing Key Laboratory of Anhui Province, Anhui Meteorological Institute, Hefei 230031, China;3.Anhui Climate Center, Hefei 230031, China |
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| Abstract: | Reference crop evapotranspiration (ET0) is one of the most important hydroclimatic variables for the hydrologic and crop models, as well as the actual evapotranspiration in the irrigation schedule. The ET0 prediction a few months in advance can be beneficial to the decision-making on the long-term planning in the water management and irrigation communities. In this research, the monthly predictions of ET0 were realized over the Huaihe River basin using the model data of Beijing Climate Center Second-Generation Climate Prediction System (BCC_CPSv2), and the surface meteorological observations at 172 stations during 1991 to 2020. The bilinear interpolation and quantile mapping were also selected to downscale and then correct the mean air temperature, net radiation, relative humidity, and wind speed of BCC_CPSv2. The obtained four variables were used to calculate the ET0 predictions using the Penman-Monteith equation. The correlation coefficient (r), Mean Bias Error (MBE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) were employed to evaluate the ET0 prediction performances of the model, and the four climatic variables before and after correction. Results showed that the mean air temperature, net radiation, and relative humidity from the model were significantly smaller than those from the observations before correction in each month with the RMSE of 1.84℃, 1.70 MJ/(m2·d), and 15.79%, respectively. The wind speed was also smaller during March to June, but larger in other months, with the RMSE of 1.39 m/s. Errors in the climatic variables led to the lower ET0 during February to June, and the larger ET0 in January, as well as in July to December, compared with the calculations. The RMSE of ET0 before correction was ranged from 0.11 to 1.70 mm/d for each month, among which was the highest in summer. The larger RMSE exceeding 0.50 mm/d occurred in eastern coastal areas in January and October, while the southwestern mountainous areas presented the higher RMSE exceeding 0.70 mm/d in April and July. The model skills on predicting climatic variables and ET0 were improved effectively by the quantile mapping. The RMSE of air temperature, net radiation, relative humidity, and wind speed decreased to 1.32℃, 0.74 MJ/(m2·d), 7.38%, and 0.53 m/s, respectively. Especially, the MAPE of wind speed and net radiation was decreased by more than 19%. The RMSE of ET0 after correction was ranged between 0.05 and 1.22 mm/d for each month, indicating the decrease in 80% of the months. The performances of the model were improved significantly for the eastern coastal areas in January, and the southwest mountainous areas in April, July and October, where the RMSE decreased by more than 0.30 mm/d. Before and after the correction, the net radiation and relative humidity were the primary factors for the ET0 prediction errors in summer half year and winter half year, respectively, due to the larger errors of the model and the higher sensitivity of ET0 to them. A better performance was achieved in the monthly ET0 prediction after the model correction with the error about 10.7%. The finding can provide a strong reference for the water resources management, irrigation schedule planning, and agricultural water demand decision-making. |
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| Keywords: | evapotranspiration climatic model prediction probability correction Huaihe River basin |
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