Improving daily precipitation estimations in a high mountainous watershed by developing a new downscaling method with spatially varying coefficients
文献类型：期刊论文
| 作者 | Zhao, Na1,2,3; Wu, Xiaoran1,2 |
| 刊名 | JOURNAL OF HYDROLOGY
 |
| 出版日期 | 2023-11-01 |
| 卷号 | 626页码:16 |
| ISSN号 | 0022-1694 |
| 关键词 | Precipitation Downscaling Satellite-based product High mountainous region |
| DOI | 10.1016/j.jhydrol.2023.130367 |
| 通讯作者 | Zhao, Na(zhaon@lreis.ac.cn) |
| 英文摘要 | Accurate estimates of spatial patterns of daily precipitation are critically important for numerous hydrologic applications. However, the estimation of daily precipitation is still challenging, especially in regions with sparse observation networks and complex terrain. Although satellite precipitation products can offer continuous pre-cipitation fields on a daily scale, the data contain large bias and coarse spatial resolution, which limit the application of these products in local studies. Here, we developed a new spatially varying coefficient downscaling approach to yield a daily precipitation field with high-resolution and high-accuracy in a high mountainous watershed. First, the random effect eigenvector spatial filtering (RESF) model was introduced and improved by considering the nonlinear term; second, the residuals were corrected by applying the simple inverse distance weighted (IDW) method. Using the proposed method, named RESF-IDW, the Global Satellite Mapping of Pre-cipitation (GSMaP) was downscaled from 0.1 degrees to 0.01 degrees by integrating reanalyzed atmospheric data and envi-ronmental variables as auxiliary information. The performance of RESF-IDW was evaluated and compared with the geographically weighted regression (GWR) method and the original GSMaP precipitation products at 20 national and automatic stations by using leave-one-out cross validation. The results show that compared to the original GSMaP products, RESF-IDW, RESF, GWR-IDW, and GWR exhibited improvements in their mean values of correlation coefficient (CC) by 24.5%, 17.9%, 12.0%, and 3.2%, respectively. Additionally, their mean values of mean absolute error (MAE) improved by 31.2%, 14.9%, 18.7%, and 2.4%, respectively, while their mean values of root mean square error (RMSE) improved by 24.1%, 18.5%, 9.6%, and 3.8%, respectively. The RESF-IDW model can be used to correctly improve the original GSMaP product under different precipitation intensities, with critical success index (CSI) improvements of 38.81%, 43.14% and 44.58% for low, medium and high precipitation intensities, respectively. The use of residual correction enhances the spatial accuracy (MAE de-creases by 19.43% on average) and the precipitation detection capability (CSI increases by 12.09% on average) of the RESF estimations. Based on the error metrics, the RESF-IDW model can be used to generate improved precipitation estimates on a daily scale and has a good capability for precipitation detection over different in-tensities. The proposed method in this study provides a promising way to generate accurate daily precipitation fields with high spatial resolution over data sparse and high mountain regions. |
| WOS关键词 | HEIHE RIVER-BASIN ; GEOGRAPHICALLY WEIGHTED REGRESSION ; HIGH-RESOLUTION ; CLIMATE ; MODEL ; RAINFALL ; TEMPERATURE ; PRODUCTS ; GSMAP |
| 资助项目 | Major Program of National Natural Science Foundation of China[42293270] ; National Program of National Natural Science Foundation of China[42071374] ; Key Project of Innovation LREIS[KPI001] |
| WOS研究方向 | Engineering ; Geology ; Water Resources |
| 语种 | 英语 |
| 出版者 | ELSEVIER |
| WOS记录号 | WOS:001105316600001 |
| 资助机构 | Major Program of National Natural Science Foundation of China ; National Program of National Natural Science Foundation of China ; Key Project of Innovation LREIS |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/200547]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Zhao, Na |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 3.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210023, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhao, Na,Wu, Xiaoran. Improving daily precipitation estimations in a high mountainous watershed by developing a new downscaling method with spatially varying coefficients[J]. JOURNAL OF HYDROLOGY,2023,626:16. |
| APA | Zhao, Na,&Wu, Xiaoran.(2023).Improving daily precipitation estimations in a high mountainous watershed by developing a new downscaling method with spatially varying coefficients.JOURNAL OF HYDROLOGY,626,16. |
| MLA | Zhao, Na,et al."Improving daily precipitation estimations in a high mountainous watershed by developing a new downscaling method with spatially varying coefficients".JOURNAL OF HYDROLOGY 626(2023):16. |
入库方式： OAI收割
来源：地理科学与资源研究所
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