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| DOI：10.13522/j.cnki.ggps.2022286 |
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| Optimization of PSEB Evapotranspiration Model Based on Energy Imbalance |
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WANG Fubing, LIU Hongjuan, TIAN Qianglong, ZHAO Zijing, QIU Zhongqi, YANG Zewei, WEI Guoxiao
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College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
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| Abstract: |
| 【Objective】The purpose of this paper is to optimize the parameters of PSEB evapotranspiration model and improve the performance of the model.【Method】The optimization of PSEB model was studied using the data of the field scientific observation station of surface process and environmental change in the semi-arid area of Lanzhou University. The differential evolution adaptive algorithm is adopted, and its core idea is Bayesian theory. The correction term of energy imbalance（αRn）is introduced into PSEB model, by constructing multiple Markov chains to estimate the posterior information of parameters. The traditional evaluation indexes including determination coefficient (R2), linear regression slope (Slope), consistency coefficient (IA), model efficiency (EF), mean bias error (MBE) and root mean square error (RMSE) are used to evaluate the performance of the optimized PSEB model.【Result】After using dream algorithm, some parameters and energy imbalance correction items（αRn）of the model is well constrained. During the model calibration period, the linear regression slopes of the two schemes are 0.76 and 0.91 respectively, and the root mean square error (RMSE) values are 91.24 and 78.33 respectively. The slope of scheme 2 is closer to 1 than that of scheme 1, and the RMSE value of scheme 2 is 14% lower than that of scheme 1, and the consistency index (IA) of the two schemes is 0.93. In the validation period, the linear regression slopes of the two schemes are 0.51 and 0.54 respectively, and the root mean square errors are 73.14 and 67.02 respectively. Compared with scheme 1, the slope of scheme 2 is closer to 1, the root mean square error (RMSE) is reduced by 8%, and the consistency index (IA) is 0.87 and 0.88 respectively. 【Conclusion】DREAM algorithm reduces the uncertainty of some parameters and energy imbalance correction items in PSEB model and improves the performance of the model. |
| Key words: evapotranspiration model; parameter optimization; energy imbalance |
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