| 引用本文: | 邱中齐,周琳琳,刘红娟, 等..玉米农田生态系统蒸散发模型参数优化[J].灌溉排水学报,2022,41(1):33-40. |
| QIU Zhongqi,ZHOU Linlin,LIU Hongjuan, et al..玉米农田生态系统蒸散发模型参数优化[J].灌溉排水学报,2022,41(1):33-40. |
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| 摘要: |
| 【目的】在无法根据实测值得到具体模型参数的地域，对经验参数进行优化以提高区域蒸散发模型的精度。【方法】通过黑河流域生态水文过程综合遥感试验水文气象观测数据集中的大满超级站气象要素梯度观测系统的数据，研究玉米农田生态系统的蒸散发模型优化问题。采用差分进化自适应算法，以潜热通量和感热通量为优化目标，引入能量闭合因子对模型参数的优化，核心思想为贝叶斯理论，通过构造多条马尔科夫链来估计参数的后验信息；引入传统评价指标包括决定系数（R2）、线性回归斜率、均方根误差（RMSE）、一致性指数（IA）、纳什系数（NSE），对Shuttleworth-Wallace原模型和优化后模型的潜热通量和感热通量的模拟性能进行评价。【结果】模型校准期，优化后模型相对于Shuttleworth-Wallace原模型在模拟潜热通量时，均方根误差降低52.46%，一致性指数提高17.3%；优化后模型在模拟潜热通量时，纳什系数达到0.82。在模拟感热通量时，优化后模型相对于Shuttleworth-Wallace原模型的评价指数提高不明显。模型验证期，优化后模型相对于Shuttleworth-Wallace原模型在模拟潜热通量时，均方根误差降低50.51%，一致性指数提高14.46%；优化后模型模拟潜热通量时，纳什系数达到0.80。在模拟感热通量时，优化后模型相对于Shuttleworth-Wallace原模型的评价指数提高不明显。【结论】对于不能根据实测值得到具体模型参数的地域，根据条件相似获得该区域的经验参数，将经验参数代入Shuttleworth-Wallace原模型中模拟该区域蒸散发，效果不理想，模型评价数据较差；相对于Shuttleworth-Wallace原模型的评价数据，优化方案对经验参数进行优化，优化后模型对潜热通量的模拟有了较大的提高，但是对感热通量模拟性能提升的效果不明显。 |
| 关键词: 参数优化；蒸散发模型；能量闭合 |
| DOI：10.13522/j.cnki.ggps.2021294 |
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| Optimizing Parameter Estimation to Improve Evapotranspiration Calculation for Maize Fields |
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QIU Zhongqi, ZHOU Linlin, LIU Hongjuan, et al.
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(1.College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China;
2. Taipingdian Town People’s Government of Huining County, Agricultural Comprehensive Service Center, Baiyin 730799, China
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| Abstract: |
| 【Objective】Evapotranspiration is an important factor in agricultural water management, but its calculation is not trial, especially in areas lacking weather stations where measured meteorological data are incomplete or unavailable. The purpose of this paper is to propose an optimal method to estimate parameters which cannot be measured directly but required for estimating evapotranspiration.【Method】The analysis was based meteorological data measured from weather stations at Daman in the basin of Hei River We took corn fields in the basin as an example and assumed latent heat flux and sensible heat flux were the parameters. Differential evolution adaptive algorithms were compared to optimize the parameters in the evapotranspiration model by introducing an energy-unclosed-factor to the multi-objective function in the parameter estimation. The model was built on the Bayesian inference with the values of the parameters calculated by the Markov chain Markov chain Monte Carlo method. Based on traditional indexes including coefficient of determination (R2), linear regression slope, root mean square error (RMSE), consistency index (IA) and Nash coefficient (NSE), we evaluated the model against the original Shuttleworth-Wallace model.【Result】We separated the comparison into two phases: a calibration phase and a prediction phase. Comparing with the original Shuttleworth Wallace model showed the optimized model reduced the root mean square error by 52.46% and increased the consistency index by 17.3% in the calibration phase; the optimized model also improved the Nash coefficient of the latent heat flux to 0.82, though it did not show significant improvement over the original Shuttleworth Wallace model for estimating the sensible heat flux. For prediction, the optimized model reduced the root mean square error by 50.51% and increased the consistency index by 14.46%, compared with the original Shuttleworth Wallace model. The proposed model improved the Nash coefficient of the latent flux to 0.80, but it did not show significant difference from the original Shuttleworth Wallace model in other evaluation indexes.【Conclusion】We proposed a model to estimate parameters which are required for calculating evapotranspiration but cannot be measured or are missing. Tests against measured latent heat flux and sensible heat flux showed that the proposed model was superior to existing models for estimating the two parameters. |
| Key words: parameter optimization; evapotranspiration model; Markov chain Monte Carlo |
