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陈玉敏、硕士生徐仁的论文在ATMOSPHERE 刊出
发布时间:2020-01-13 15:47:08     发布者:易真     浏览次数:

标题: Future Changes of Precipitation over the Han River Basin Using NEX-GDDP Dataset and the SVR_QM Method

作者: Xu, R (Xu, Ren); Chen, YM (Chen, Yumin); Chen, ZQ (Chen, Zeqiang)

来源出版物: ATMOSPHERE  : 10  : 11  文献号: 688  DOI: 10.3390/atmos10110688  出版年: NOV 2019

摘要: After the release of the high-resolution downscaled National Aeronautics and Space Administration (NASA) Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset, it is worth exploiting this dataset to improve the simulation and projection of local precipitation. This study developed support vector regression (SVR) and quantile mapping (SVR_QM) ensemble and correction models on the basis of historic precipitation in the Han River basin and the 21 NEX-GDDP models. The generated SVR_QM models were applied to project changes of precipitation during the 21st century for the region. Several statistical metrics, including Pearson's correlation coefficient (PCC), root mean squared error (RMSE), and relative bias (Rbias), were used for evaluation and comparative analyses. The results demonstrated the superior performance of SVR_QM compared with multi-layer perceptron (MLP), SVR, and random forest (RF), as well as simple model average (MME) ensemble methods and single NEX-GDDP models. PCC was up to 0.84 from 0.61-0.71 for the single NEX-GDDP models, RMSE was up to 34.02 mm from 48-51 mm, and Rbias values were almost removed. Additionally, the projected precipitation changes during the 21st century in most stations had an increasing trend under both Representative Concentration Pathway RCP4.5 and RCP8.5 emissions scenarios; the regional average precipitation during the middle (2040-2059) and late (2070-2089) 21st century increased by 3.54% and 5.12% under RCP4.5 and by 7.44% and 9.52% under RCP8.5, respectively.

入藏号: WOS:000502272000050

语言: English

文献类型: Article

作者关键词: machine learning; quantile mapping; NEX-GDDP; precipitation; Han River basin

地址: [Xu, Ren; Chen, Yumin] Wuhan Univ, Sch Resource & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.

[Xu, Ren; Chen, Zeqiang] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, 129 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.

[Chen, Zeqiang] Wuhan Univ, Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Hubei, Peoples R China.

通讯作者地址: Chen, ZQ (通讯作者)Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, 129 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.

Chen, ZQ (通讯作者)Wuhan Univ, Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Hubei, Peoples R China.

电子邮件地址: rennxu@whu.edu.cn; ymchen@whu.edu.cn; ZeqiangChen@whu.edu.cn

影响因子:2.046



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