气候变化领域集成服务门户(CCPortal): Evaluating satellite-based and reanalysis precipitation datasets with gauge-observed data and hydrological modeling in the Xihe River Basin, China

CCPortal

DOI	10.1016/j.atmosres.2019.104746
	Evaluating satellite-based and reanalysis precipitation datasets with gauge-observed data and hydrological modeling in the Xihe River Basin, China
	Wang N.; Liu W.; Sun F.; Yao Z.; Wang H.; Liu W.
发表日期	2020
ISSN	0169-8095
卷号	234
英文摘要	The wide application of satellite-based and reanalysis-based precipitation data has greatly promoted hydrometeorological research in areas where precipitation observations are scarce. However, the suitability of such precipitation products needs to be carefully evaluated before applications in certain basins because their inherited errors vary with different climate zones, seasonal cycles and land surface conditions; in addition, precipitation products have not been evaluated in the Xihe River basin, China. In this paper, two representative satellite-based precipitation products (Tropical Rainfall Measuring Mission (TRMM) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (CDR)) and two reanalysis-based precipitation products (China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) and National Centers for Environmental Prediction - Climate Forecast System Reanalysis (CFSR)) were selected for evaluation and corrected against gauge-observed data (OBS). Furthermore, the performances of precipitation products in hydrological simulations were also assessed using the SWAT model calibrated with OBS forcing and not with individual precipitation products. The results show that satellite-based precipitation has a higher quality than reanalysis-based precipitation. The CFSR and CDR overestimate precipitation (the overestimation of CDR precipitation is mainly concentrated in the precipitation intensity range of 1 mm/d to 5 mm/d), while the TRMM and CMADS underestimate precipitation in the Xihe River basin. The TRMM precipitation performs best during the wet season, while the CDR precipitation performed best during the dry season. After bias correction, the quality of TRMM precipitation improves significantly. The Nash-Sutcliffe coefficient (NS) (the percent bias (\|PBIAS\|)) increases (decreases) by 0.61 (77.27%) and 0.7 (39.15%) under the two different correction scenarios during 2009–2015. Overall, the above original precipitation products cannot be used as supplements to the OBS in the Xihe River basin unless they are corrected by the OBS. © 2019 Elsevier B.V.
英文关键词	Error correction; Hydrological model; Reanalysis-based precipitation; Satellite-based precipitation; Xihe River Basin
学科领域	Climate models; Clock and data recovery circuits (CDR circuits); Error correction; Neural networks; Rain gages; Rivers; Satellites; Watersheds; Hydrological modeling; Nash-Sutcliffe coefficient; National centers for environmental predictions; Precipitation estimation from remotely sensed information; Reanalysis; River basins; Soil and water assessment tool; Tropical rainfall measuring missions; Precipitation (meteorology); data set; error correction; gauge; hydrological modeling; precipitation assessment; river basin; satellite data; satellite imagery; China; Nei Monggol; Xihe Basin
语种	英语
scopus关键词	Climate models; Clock and data recovery circuits (CDR circuits); Error correction; Neural networks; Rain gages; Rivers; Satellites; Watersheds; Hydrological modeling; Nash-Sutcliffe coefficient; National centers for environmental predictions; Precipitation estimation from remotely sensed information; Reanalysis; River basins; Soil and water assessment tool; Tropical rainfall measuring missions; Precipitation (meteorology); data set; error correction; gauge; hydrological modeling; precipitation assessment; river basin; satellite data; satellite imagery; China; Nei Monggol; Xihe Basin
来源期刊	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/120520
作者单位	College of Urban and Environmental Sciences, Northwest University, Xi'an, 710127, China; Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou, 450045, China; Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China
推荐引用方式 GB/T 7714	Wang N.,Liu W.,Sun F.,et al. Evaluating satellite-based and reanalysis precipitation datasets with gauge-observed data and hydrological modeling in the Xihe River Basin, China[J],2020,234.
APA	Wang N.,Liu W.,Sun F.,Yao Z.,Wang H.,&Liu W..(2020).Evaluating satellite-based and reanalysis precipitation datasets with gauge-observed data and hydrological modeling in the Xihe River Basin, China.Atmospheric Research,234.
MLA	Wang N.,et al."Evaluating satellite-based and reanalysis precipitation datasets with gauge-observed data and hydrological modeling in the Xihe River Basin, China".Atmospheric Research 234(2020).