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DOI | 10.1002/joc.6050 |
Integrating precipitation zoning with random forest regression for the spatial downscaling of satellite-based precipitation: A case study of the Lancang-Mekong River basin | |
Zhang, Jing1,2; Fan, Hui1,2; He, Daming1,2; Chen, Jiwei3 | |
2019-08-01 | |
发表期刊 | INTERNATIONAL JOURNAL OF CLIMATOLOGY |
ISSN | 0899-8418 |
EISSN | 1097-0088 |
出版年 | 2019 |
卷号 | 39期号:10页码:3947-3961 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China |
英文摘要 | Downscaling satellite-based precipitation to fine scales is crucial for deepening our understanding of global hydrologic cycles and water-related issues. In this study, a novel approach that integrates precipitation zoning with random forest regression is proposed for the spatial downscaling of satellite-based precipitation. Precipitation zoning is delineated through iterative rotated empirical orthogonal function (REOF) analyses of ground- and satellite-based precipitation observations. Random forest regression is applied to link the satellite-based precipitation to 1-km-resolution predictors such as latitude (Lat), longitude (Lon), elevation, aspect, slope, and the normalized difference vegetation index (NDVI). The accuracy of the resultant downscaled precipitation is evaluated based on five statistical evaluation indices. The performance of the proposed approach is exemplified in the Lancang-Mekong River basin, taking Tropical Rainfall Measuring Mission (TRMM) 3B43 and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) products acquired in 2001 (wet year), 2005 (normal year), and 2009 (dry year) as the databases. The results show that seven precipitation subregions can be roughly distinguished in the study basin. Zoning-based downscaling outperforms non-zoning-based downscaling in terms of accuracy, resulting in statistically significant reductions in root mean square error (RMSE) and mean absolute error (MAE) of 1-17% across the entire basin. Among the selected predictors, the variables Lat and Lon are the most important for precipitation estimation, whereas the remaining variables have lesser and subregion-dependent importance. The proposed approach is promising for generating high-spatial-resolution precipitation data in regions with sparse ground-based observations and differentiated climatic regimes. |
英文关键词 | lancang-mekong river basin precipitation zoning random forest regression REOF analysis spatial downscaling |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000479031900004 |
WOS关键词 | NCEP-CFSR ; TRMM ; PRODUCTS ; ROTATION ; NDVI ; REGIONALIZATION ; TEMPERATURE ; ALGORITHM ; REGIONS ; FUTURE |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/185683 |
专题 | 气候变化 |
作者单位 | 1.Yunnan Univ, Yunnan Key Lab Int Rivers & Transboundary Ecosecu, Kunming 650091, Yunnan, Peoples R China; 2.Yunnan Univ, Inst Int Rivers & Ecosecur, Kunming, Yunnan, Peoples R China; 3.Minist Water Resources, Int Econ & Tech Cooperat & Exchange Ctr, Beijing, Peoples R China |
推荐引用方式 GB/T 7714 | Zhang, Jing,Fan, Hui,He, Daming,et al. Integrating precipitation zoning with random forest regression for the spatial downscaling of satellite-based precipitation: A case study of the Lancang-Mekong River basin[J]. INTERNATIONAL JOURNAL OF CLIMATOLOGY,2019,39(10):3947-3961. |
APA | Zhang, Jing,Fan, Hui,He, Daming,&Chen, Jiwei.(2019).Integrating precipitation zoning with random forest regression for the spatial downscaling of satellite-based precipitation: A case study of the Lancang-Mekong River basin.INTERNATIONAL JOURNAL OF CLIMATOLOGY,39(10),3947-3961. |
MLA | Zhang, Jing,et al."Integrating precipitation zoning with random forest regression for the spatial downscaling of satellite-based precipitation: A case study of the Lancang-Mekong River basin".INTERNATIONAL JOURNAL OF CLIMATOLOGY 39.10(2019):3947-3961. |
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