干旱区生态环境知识资源中心(Arid): Evaluation of Eight Global Precipitation Datasets in Hydrological Modeling

Arid

DOI	10.3390/rs13142831
	Evaluation of Eight Global Precipitation Datasets in Hydrological Modeling
	Xiang, Yiheng; Chen, Jie; Li, Lu; Peng, Tao; Yin, Zhiyuan
通讯作者	Chen, J (corresponding author), Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China.
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2021
卷号	13 期号:14
英文摘要	The number of global precipitation datasets (PPs) is on the rise and they are commonly used for hydrological applications. A comprehensive evaluation on their performance in hydrological modeling is required to improve their performance. This study comprehensively evaluates the performance of eight widely used PPs in hydrological modeling by comparing with gauge-observed precipitation for a large number of catchments. These PPs include the Global Precipitation Climatology Centre (GPCC), Climate Hazards Group Infrared Precipitation with Station dataset (CHIRPS) V2.0, Climate Prediction Center Morphing Gauge Blended dataset (CMORPH BLD), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN CDR), Tropical Rainfall Measuring Mission multi-satellite Precipitation Analysis 3B42RT (TMPA 3B42RT), Multi-Source Weighted-Ensemble Precipitation (MSWEP V2.0), European Center for Medium-range Weather Forecast Reanalysis 5 (ERA5) and WATCH Forcing Data methodology applied to ERA-Interim Data (WFDEI). Specifically, the evaluation is conducted over 1382 catchments in China, Europe and North America for the 1998-2015 period at a daily temporal scale. The reliabilities of PPs in hydrological modeling are evaluated with a calibrated hydrological model using rain gauge observations. The effectiveness of PPs-specific calibration and bias correction in hydrological modeling performances are also investigated for all PPs. The results show that: (1) compared with the rain gauge observations, GPCC provides the best performance overall, followed by MSWEP V2.0; (2) among the eight PPs, the ones incorporating daily gauge data (MSWEP V2.0 and CMORPH BLD) provide superior hydrological performance, followed by those incorporating 5-day (CHIRPS V2.0) and monthly (TMPA 3B42RT, WFDEI, and PERSIANN CDR) gauge data. MSWEP V2.0 and CMORPH BLD perform better than GPCC, underscoring the effectiveness of merging multiple satellite and reanalysis datasets; (3) regionally, all PPs exhibit better performances in temperate regions than in arid or topographically complex mountainous regions; and (4) PPs-specific calibration and bias correction both can improve the streamflow simulations for all eight PPs in terms of the Nash and Sutcliffe efficiency and the absolute bias. This study provides insights on the reliabilities of PPs in hydrological modeling and the approaches to improve their performance, which is expected to provide a reference for the applications of global precipitation datasets.
英文关键词	global precipitation datasets (PPs) precipitation evaluation hydrological modeling PPs-specific calibration bias correction
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000677079400001
WOS关键词	SATELLITE RAINFALL PRODUCTS ; GAUGE OBSERVATIONS ; COMPLEX TERRAIN ; BASIN ; UNCERTAINTY ; ACCURACY ; QUANTIFICATION ; EVAPORATION ; PERFORMANCE ; VALIDATION
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/368639
作者单位	[Xiang, Yiheng; Peng, Tao; Yin, Zhiyuan] China Meteorol Adm CMA, Inst Heavy Rain, Wuhan 430205, Peoples R China; [Xiang, Yiheng; Chen, Jie] Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China; [Li, Lu] NORCE Norwegian Res Ctr, Bjerknes Ctr Climate Res, Jahnebakken 5, NO-5007 Bergen, Norway
推荐引用方式 GB/T 7714	Xiang, Yiheng,Chen, Jie,Li, Lu,et al. Evaluation of Eight Global Precipitation Datasets in Hydrological Modeling[J],2021,13(14).
APA	Xiang, Yiheng,Chen, Jie,Li, Lu,Peng, Tao,&Yin, Zhiyuan.(2021).Evaluation of Eight Global Precipitation Datasets in Hydrological Modeling.REMOTE SENSING,13(14).
MLA	Xiang, Yiheng,et al."Evaluation of Eight Global Precipitation Datasets in Hydrological Modeling".REMOTE SENSING 13.14(2021).