A comprehensive investigation of three long-term precipitation datasets: Which performs better in the Yellow River basin?

被引:4
作者
Huang, Ruochen [1 ,2 ]
Yong, Bin [1 ,2 ,5 ]
Huang, Fan [2 ]
Wu, Hao [3 ]
Shen, Zhehui [4 ]
Qian, Da [1 ,2 ]
机构
[1] Hohai Univ, State Key Lab Hydrol Water Resources & Hydraul Eng, Natl Key Lab Water Disaster Prevent, Nanjing, Peoples R China
[2] Hohai Univ, Coll Hydrol & Water Resources, Nanjing, Peoples R China
[3] Chuzhou Univ, Sch Geog Informat & Tourism, Chuzhou, Peoples R China
[4] Nanjing Forestry Univ, Coll Civil Engn, Nanjing, Peoples R China
[5] Hohai Univ, Natl Key Lab Water Disaster Prevent, Nanjing 210098, Peoples R China
基金
中国国家自然科学基金;
关键词
CHIRPS; ERA5-Land; extreme precipitation; MSWEP; precipitation estimates; the Yellow River basin; SATELLITE-OBSERVATIONS; PRODUCTS; TMPA; REANALYSIS; EXTREMES; CMORPH; GAUGE; IMERG; TRMM; MULTISENSOR;
D O I
10.1002/joc.8383
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
The fifth generation European Centre for Medium-Range Weather Forecasts Reanalysis on global land surface (ERA5-Land), the Multi-Source Weighted-Ensemble Precipitation (MSWEP), and the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) are three representative precipitation estimates with quasi-global coverage, high-resolution and long-term record. This study concentrates on investigating, for the first time, the long-term spatiotemporal accuracy and regional applicability of these precipitation estimates at a daily scale in the Yellow River basin (YRB) using 39 complete years of data record (1981-2019), with a special focus on their capability on monitoring the extreme precipitation events with short duration and the continuous heavy precipitation events. Results indicate that MSWEP generally performs better than ERA5-Land and CHIRPS in almost all seasons and subregions, with the highest Pearson correlation coefficient and critical success index, lowest root mean square error and false alarm ratio. ERA5-Land presents a severe overestimation of precipitation amount, particularly in the plateau climate region (BIAS = 52.27%), but well reflects its spatial-temporal patterns in the YRB. As for the detecting capability, MSWEP shows the best accuracy in detecting extreme precipitation, particularly in maximum consecutive 5-day precipitation (RX5day). The MSWEP better represents the spatial distribution of maximum 1-day precipitation and maximum consecutive 5-day precipitation in the YRB, but it shows a significant overestimation in zone Southern Qinghai. MSWEP and CHIRPS have better performance of temporal variation consistency in annual precipitation with ground reference than ERA5-Land, while ERA5-Land performs well in capturing extreme precipitation temporal variation, especially for continuous heavy precipitation events. This study can provide useful guidance when choosing long-term precipitation products for hydrometeorological applications and climate-related studies in the YRB. This study concentrates on investigating, for the first time, the long-term (1981-2019) spatiotemporal accuracy and regional applicability of ERA5-Land, MSWEP and CHIRPS at a daily scale in the Yellow River basin (YRB), with a special focus on their capability on monitoring the extreme precipitation events with short duration and the continuous heavy precipitation events. image
引用
收藏
页码:1302 / 1325
页数:24
相关论文
共 37 条
  • [1] Evaluation of Three High-Resolution Satellite and Meteorological Reanalysis Precipitation Datasets over the Yellow River Basin in China
    Xie, Meixia
    Di, Zhenhua
    Liu, Jianguo
    Zhang, Wenjuan
    Sun, Huiying
    Tian, Xinling
    Meng, Hao
    Wang, Xurui
    WATER, 2024, 16 (22)
  • [2] Performance of Two Long-Term Satellite-Based and GPCC 8.0 Precipitation Products for Drought Monitoring over the Yellow River Basin in China
    Wei, Linyong
    Jiang, Shanhu
    Ren, Liliang
    Yuan, Fei
    Zhang, Linqi
    SUSTAINABILITY, 2019, 11 (18)
  • [3] Long-term spatiotemporal variability of precipitation and its linkages with atmospheric teleconnections in the Yellow River Basin, China
    Wang, Junjie
    Chi, Yuning
    Shi, Bing
    Yuan, Qingyun
    Wang, Xia
    Shen, Lijun
    JOURNAL OF WATER AND CLIMATE CHANGE, 2023, 14 (03) : 900 - 915
  • [4] Investigating twelve mainstream global precipitation datasets: Which one performs better on the Tibetan Plateau?
    Lyu, Yi
    Yong, Bin
    Huang, Fan
    Qi, Weiqing
    Tian, Fuqiang
    Wang, Guoqing
    Zhang, Jianyun
    JOURNAL OF HYDROLOGY, 2024, 633
  • [5] Long-term hydrological assessment of remote sensing precipitation from multiple sources over the lower Yangtze River basin, China
    Zhu, Dehua
    Ilyas, Abro M.
    Wang, Gaoxu
    Zeng, Biqiu
    METEOROLOGICAL APPLICATIONS, 2021, 28 (03)
  • [6] Comparison of two long-term and high-resolution satellite precipitation datasets in Xinjiang, China
    Gao, Feng
    Zhang, Yuhu
    Chen, Qiuhua
    Wang, Peng
    Yang, Huirong
    Yao, Yunjun
    Cai, Wanyuan
    ATMOSPHERIC RESEARCH, 2018, 212 : 150 - 157
  • [7] Long-term variation of water vapor content and precipitation in the Haihe river basin
    Chang, Chun
    Feng, Ping
    Li, Fawen
    Gao, Yunming
    JOURNAL OF WATER AND CLIMATE CHANGE, 2015, 6 (02) : 341 - 351
  • [8] Evaluating satellite-derived long-term historical precipitation datasets for drought monitoring in Chile
    Zambrano, Francisco
    Wardlow, Brian
    Tadesse, Tsegaye
    REMOTE SENSING FOR AGRICULTURE, ECOSYSTEMS, AND HYDROLOGY XVIII, 2016, 9998
  • [9] Evaluating satellite-derived long-term historical precipitation datasets for drought monitoring in Chile
    Zambrano, Francisco
    Wardlow, Brian
    Tadesse, Tsegaye
    Lillo-Saavedra, Mario
    Lagos, Octavio
    ATMOSPHERIC RESEARCH, 2017, 186 : 26 - 42
  • [10] Long-term variability of extreme precipitation with WRF model at a complex terrain River Basin
    Zhang, Yinchi
    Deng, Chao
    Xu, Wanling
    Zhuang, Yao
    Jiang, Lizhi
    Jiang, Caiying
    Guan, Xiaojun
    Wei, Jianhui
    Ma, Miaomiao
    Chen, Ying
    Peng, Jian
    Gao, Lu
    SCIENTIFIC REPORTS, 2025, 15 (01):