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Open AccessArticle

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

by Ren Xu 1,2, Yumin Chen 1 and Zeqiang Chen 2,3,*
1
School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
2
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
3
Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Atmosphere 2019, 10(11), 688; https://doi.org/10.3390/atmos10110688
Received: 17 October 2019 / Revised: 3 November 2019 / Accepted: 5 November 2019 / Published: 8 November 2019
(This article belongs to the Section Meteorology)
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. View Full-Text
Keywords: machine learning; quantile mapping; NEX-GDDP; precipitation; Han River basin machine learning; quantile mapping; NEX-GDDP; precipitation; Han River basin
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Xu, R.; Chen, Y.; Chen, Z. Future Changes of Precipitation over the Han River Basin Using NEX-GDDP Dataset and the SVR_QM Method. Atmosphere 2019, 10, 688.

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