Climatology and Hydrology

Performance evaluation of ERA5 reanalysis precipitation data and spatiotemporal characteristics of extreme precipitation in Inner Mongolia

  • Yiying NIU ,
  • Chunlan LI ,
  • Jun WANG ,
  • Hanqing XU ,
  • Qing LIU
Expand
  • 1. Key Laboratory of Geographic Science of Ministry of Education, East China Normal University, Shanghai 200241, China
    2. School of Geographic Science, East China Normal University, Shanghai 200241, China
    3. Research Center for Urban Public Security, East China Normal University, Shanghai 200241, China
    4. School of Urban and Regional Science, East China Normal University, Shanghai 200241, China
    5. Institute of Eco-Chongming, East China Normal University, Shanghai 202162, China

Received date: 2022-11-17

  Revised date: 2023-02-09

  Online published: 2023-09-28

Abstract

ERA5 is a new generation reanalysis product launched by the European Center for Medium-Range Weather Forecasts that can provide a new source of precipitation data for areas with few ground observation stations. Based on the daily precipitation data of 45 ground stations in Inner Mongolia, China from 2008 to 2017, we evaluated the accuracy of ERA5 reanalysis precipitation data on multiple temporal and spatial scales using multiple evaluation indicators and applied a comprehensive weighting model to construct an extreme precipitation danger index by integrating 13 extreme precipitation indices. We then used principal component analysis, Sen’s slope method, and a Mann-Kendall trend test to analyze the temporal and spatial changes of extreme precipitation and extreme precipitation danger in the region from 1981 to 2021. The results show that: (1) ERA5 can reproduce the precipitation process better; the ERA5 precipitation reanalysis dataset performs better at a monthly time scale than at a daily time scale, and its accuracy is highest in summer and lowest in winter. ERA5 precipitation data are highly correlated with observed data at monthly and seasonal scales (correlation coefficient>0.85) and are strongly correlated with observed data at a daily scale (correlation coefficient=0.68). The detection accuracy of ERA5 data is better for the eastern stations than for the western stations. (2) Extreme precipitation indices showed a downward trend except for daily wet precipitation intensity, total heavy precipitation, and continuous dry days. The annual total wet day precipitation declined fastest, and the Sen’s slope value was -15.74 mm·(10a)-1. (3) The extreme precipitation indices show clear regional spatial differentiation. Extreme precipitation shows an increase in intensity in western Inner Mongolia, a decrease in frequency, intensity, and duration in mid-Inner Mongolia, and an increase in intensity, frequency, and duration in eastern Inner Mongolia. (4) The extreme precipitation danger index has high central values and shows significant upward trends in Ordos City, Hulun Buir City, Bayannur City, Hinggan League, and other cities/leagues with relatively dense populations and rapid economic development and should therefore receive special attention. The results of this study can facilitate the discovery of better datasets for the analysis of climate factors in Inner Mongolia, and the study provides a theoretical basis for the formulation of climate change adaptation measures and future climate prediction.

Cite this article

Yiying NIU , Chunlan LI , Jun WANG , Hanqing XU , Qing LIU . Performance evaluation of ERA5 reanalysis precipitation data and spatiotemporal characteristics of extreme precipitation in Inner Mongolia[J]. Arid Land Geography, 2023 , 46(9) : 1418 -1431 . DOI: 10.12118/j.issn.1000-6060.2022.607

References

[1] 白美兰, 郝润全, 高建峰, 等. 内蒙古地区极端气候事件分布特征及对农业影响评估[J]. 干旱地区农业研究, 2009, 27(2): 21-27.
[1] [Bai Meilan, Hao Runquan, Gao Jianfeng, et al. Distribution character of extreme climatic events and evaluation of its influence on agriculture in Inner Mongolia[J]. Agricultural Research in the Arid Areas, 2009, 27(2): 21-27.]
[2] 尤莉, 戴新刚, 张宇. 1961—2008年内蒙古降水极端事件分析[J]. 气候变化研究进展, 2010, 6(6): 411-416.
[2] [You li, Dai Xingang, Zhang Yu. Extreme precipitation events in Inner Mongolia in 1961—2008[J]. Climate Change Research, 2010, 6(6): 411-416.]
[3] 刘泓志, 肖长来, 张岩祥, 等. 内蒙古50余年降水量分布演变特征及趋势[J]. 水土保持研究, 2015, 22(2): 74-78.
[3] [Liu Hongzhi, Xiao Changlai, Zhang Yanxiang, et al. Analysis on temporal characteristics and trend of precipitation over the past 50 years in Inner Mongolia[J]. Research of Soil and Water Conservation, 2015, 22(2): 74-78.]
[4] Haile A T, Yan F, Habib E. Accuracy of the CMORPH satellite-rainfall product over Lake Tana Basin in eastern Africa[J]. Atmospheric Research, 2015, 163: 177-187.
[5] Ma Q, Li Y, Feng H, et al. Performance evaluation and correction of precipitation data using the 20-year IMERG and TMPA precipitation products in diverse subregions of China[J]. Atmospheric Research, 2021, 249: 105304, doi: 10.1016/j.atmosres.2020.105304.
[6] Mantas V M, Liu Z, Caro C, et al. Validation of TRMM multi-satellite precipitation analysis (TMPA) products in the Peruvian Andes[J]. Atmospheric Research, 2015, 163: 132-145.
[7] Yong B, Chen B, Gourley J J, et al. Intercomparison of the Version-6 and Version-7 TMPA precipitation products over high and low latitudes basins with independent gauge networks: Is the newer version better in both real-time and post-real-time analysis for water resources and hydrologic extremes?[J]. Journal of Hydrology, 2014, 508: 77-87.
[8] 唐国强, 万玮, 曾子悦, 等. 全球降水测量(GPM)计划及其最新进展综述[J]. 遥感技术与应用, 2015, 30(4): 607-615.
[8] [Tang Guoqiang, Wan Wei, Zeng Ziyue, et al. An overview of the global precipitation measurement (GPM) Mission and it’s latest development[J]. Remote Sensing Technology and Application, 2015, 30(4): 607-615.]
[9] 黄依之, 张行南, 方园皓. CMORPH卫星反演降水数据质量评估及水文过程模拟[J]. 水电能源科学, 2020, 38(9): 1-4.
[9] [Huang Yizhi, Zhang Xingnan, Fang Yuanhao. Evaluation of CMORPH satellite rainfall data and its application in hydrologic process simulation[J]. Water Resources and Power, 2020, 38(9): 1-4.]
[10] Tan M L, Santo H. Comparison of GPM IMERG, TMPA 3B42 and PERSIANN-CDR satellite precipitation products over Malaysia[J]. Atmospheric Research, 2018, 202: 63-76.
[11] 岳书平, 闫业超, 张树文, 等. 基于ERA5-LAND的中国东北地区近地表土壤冻融状态时空变化特征[J]. 地理学报, 2021, 76(11): 2765-2779.
[11] [Yue Shuping, Yan Yechao, Zhang Shuwen, et al. Spatiotemporal variations of soil freeze-thaw state in northeast China based on the ERA5-LAND dataset[J]. Acta Geographica Sinica, 2021, 76(11): 2765-2779.]
[12] Jiao D L, Xu N N, Yang F, et al. Evaluation of spatial-temporal variation performance of ERA5 precipitation data in China[J]. Scientific Reports, 2021, 11(1): 17956, doi: 10.1038/s41598-021-97432-y.
[13] Chen G X, Iwasaki T, Qin H L, et al. Evaluation of the warm-season diurnal variability over East Asia in recent reanalyses JRA-55, ERA-Interim, NCEP CFSR, and NASA MERRA[J]. Journal of Climate, 2014, 27(14): 5517-5537.
[14] Gabriela C R, Tereza C. Trends of daily extreme and non-extreme rainfall indices and intercomparison with different gridded data sets over Mexico and the southern United States[J]. International Journal of Climatology, 2021, 41(11): 5406-5430.
[15] 温婷婷, 郭英香, 董少睿, 等. 1979—2017年CRU、ERA5、CMFD格点降水数据在青藏高原适用性评估[J]. 干旱区研究, 2022, 39(3): 684-697.
[15] [Wen Tingting, Guo Yingxiang, Dong Shaorui, et al. Assessment of CRU, ERA5, CMFD grid precipitation data for the Tibetan Plateau from 1979 to 2017[J]. Arid Zone Research, 2022, 39(3): 684-697.]
[16] Amjad M, Yilmaz M T, Yucel I, et al. Performance evaluation of satellite- and model-based precipitation products over varying climate and complex topography[J]. Journal of Hydrology, 2020, 584: 124707, doi: 10.1016/j.jhydrol.2020.124707.
[17] Sharifi E, Eitzinger J, Dorigo W. Performance of the state-of-the-art gridded precipitation products over mountainous terrain: A regional study over Austria[J]. Remote Sensing, 2019, 11(17): 2018, doi: 10.3390/rs11172018.
[18] Jiang Q, Li W Y, Wen J H, et al. Evaluation of satellite-based products for extreme rainfall estimations in the eastern coastal areas of China[J]. Journal of Integrative Environmental Sciences, 2019, 16: 191-207.
[19] 尹红, 孙颖. 基于ETCCDI指数 2017 年中国极端温度和降水特征分析[J]. 气候变化研究进展, 2019, 15(4): 363-373.
[19] [Yin Hong, Sun Ying. Characteristics of extreme temperature and precipitation in China in 2017 based on ETCCDI indices[J]. Climate Change Research, 2019, 15(4): 363-373.]
[20] 马爱华, 岳大鹏, 赵景波, 等. 近60 a来内蒙古极端降水时空变化及其影响[J]. 干旱区研究, 2020, 37(1): 74-85.
[20] [Ma Aihua, Yue Dapeng, Zhao Jingbo, et al. Spatiotemporal variation and effect of extreme precipitation in Inner Mongolia in recent 60 years[J]. Arid Zone Research, 2020, 37(1): 74-85.]
[21] 许心怡, 李建柱, 冯平. 不同降水产品在滦河流域径流模拟中的适用性[J]. 水力发电学报, 2021, 40(12): 25-39.
[21] [Xu Xinyi, Li Jianzhu, Feng Ping. Applicability of different precipitation products to runoff simulations of Luanhe River Basin[J]. Journal of Hydroelectric Engineering, 2021, 40(12): 25-39.]
[22] Xu F L, Guo B, Ye B, et al. Systematical evaluation of GPM IMERG and TRMM 3b42v7 precipitation products in the Huang-Huai-Hai Plain, China[J]. Remote Sensing, 2019, 11(6): 697, doi: 10.3390/rs11060697.
[23] 甘富万, 李彦婕, 倪倩, 等. 五种卫星降水产品在沿海流域多时空尺度的综合精度评估[J]. 中国农村水利水电, 2022(4): 72-79.
[23] [Gan Fuwan, Li Yanjie, Ni Qian, et al. Comprehensive accuracy evaluation of five satellite precipitation products in the coastal basin at multiple spatio-temporal scales[J]. China Rural Water and Hydropower, 2022(4): 72-79.]
[24] 李彦妮, 黄昌, 庞国伟. 全球降雨计划GSMaP与IMERG卫星降雨产品在陕西地区的精度评估[J]. 干旱区地理, 2022, 45(1): 80-90.
[24] [Li Yanni, Huang Chang, Pang Guowei. Accuracy assessment of GSMaP and IMERG satellite precipitation products in Shaanxi Province[J]. Arid Land Geography, 2022, 45(1): 80-90.]
[25] 任英杰, 雍斌, 鹿德凯, 等. 全球降水计划多卫星降水联合反演IMERG卫星降水产品在中国大陆地区的多尺度精度评估[J]. 湖泊科学, 2019, 31(2): 560-572.
[25] [Ren Yingjie, Yong Bin, Lu Dekai, et al. Evaluation of the integrated multi-satellite retrievals (IMERG) for global precipitation measurement (GPM) mission over the mainland China at multiple scales[J]. Journal of Lake Sciences, 2019, 31(2): 560-572.]
[26] 姚飛, 杨秀芹, 刘慕嘉, 等. ERA5再分析降水数据在长江三角洲的性能评估[J]. 水土保持学报, 2022, 36(4): 178-189.
[26] [Yao Fei, Yang Xiuqin, Liu Mujia, et al. Performance evaluation of ERA5 reanalysis precipitation data in the Yangtze River Delta[J]. Journal of Soil and Water Conservation, 2022, 36(4): 178-189.]
[27] 王蕊, 余钟波, 杨传国, 等. TRMM/GPM卫星降水产品在淮河上游逐日和小时尺度的精度评估[J]. 水资源与水工程学报, 2018, 29(5): 109-115.
[27] [Wang Rui, Yu Zhongbo, Yang Chuanguo, et al. Accuracy evaluation of TRMM/GPM satellite precipitation products on daily and hourly scales in upper reaches of Huaihe River Basin[J]. Journal of Water Resources and Water Engineering, 2018, 29(5): 109-115.]
[28] Zhou C G, Gao W, Hu J, et al. Capability of IMERG V6 early, late, and final precipitation products for monitoring extreme precipitation events[J]. Remote Sensing, 2021, 13(4): 689, doi: 10.3390/rs13040689.
[29] Yong B, Ren L L, Hong Y, et al. Hydrologic evaluation of multi-satellite precipitation analysis standard precipitation products in basins beyond its inclined latitude band: A case study in Laohahe Basin, China[J]. Water Resources Research, 2010, 46(7): 759-768.
[30] 周旗, 张海宁, 任源鑫. 1961—2016 年渭河流域极端降水事件研究[J]. 地理科学, 2020, 40(5): 833-841.
[30] [Zhou Qi, Zhang Haining, Ren Yuanxin. Extreme precipitation events in the Weihe River Basin from 1961 to 2016[J]. Scientia Geographica Sinica, 2020, 40(5): 833-841.]
[31] 徐玉霞. 基于GIS的陕西省洪涝灾害风险评估及区划[J]. 灾害学, 2017, 32(2): 103-108.
[31] [Xu Yuxia. Assessment and regionalization of flood disaster risk in Shaanxi Province based on GIS[J]. Journal of Catastrophology, 2017, 32(2): 103-108.]
[32] 李思慧. 内蒙古东南部暴雨洪涝灾害风险评估与区划——以通辽市为例[J]. 内蒙古气象, 2019(1): 23-28.
[32] [Li Sihui. Risk assessment and zonation of rainstorm and flood disasters in the southeast region of Inner Mongolia: A case study of Tongliao[J]. Meteorology Journal of Inner Mongolia, 2019(1): 23-28.]
[33] 黄晓远, 李谢辉. 基于CMIP6的西南暴雨洪涝灾害风险未来预估[J]. 应用气象学报, 2022, 33(2): 231-243.
[33] [Huang Xiaoyuan, Li Xiehui. Future projection of rainstorm and flood disaster risk in southwest China based on CMIP6 models[J]. Journal of Applied Meteorological Science, 2022, 33(2): 231-243.]
[34] Sen P K. Estimates of the regression coefficient based on Kendall’s Tau[J]. Journal of the American Statistical Association, 1968, 63(324): 1379-1389.
[35] 金令, 王永芳, 郭恩亮, 等. 基于SPEIbase v.2.6数据集的内蒙古旱灾危险性评价[J]. 干旱区地理, 2022, 45(3): 695-705.
[35] [Jin Ling, Wang Yongfang, Guo Enliang, et al. Evaluation of drought hazards in Inner Mongolia based on SPEIbase v.2.6 dataset[J]. Arid Land Geography, 2022, 45(3): 695-705.]
[36] 肖杨, 周旭, 罗雪, 等. 黔中地区近60年潜在蒸散量时空变化特征及主导因素识别[J]. 水土保持研究, 2021, 28(6): 190-198.
[36] [Xiao Yang, Zhou Xu, Luo Xue, et al. Spatiotemporal variation characteristics of potential evapotranspiration and identification of leading factors in central Guizhou in recent 60 years[J]. Research of Soil and Water Conservation, 2021, 28(6): 190-198.]
[37] 范磊, 吕爱锋, 张文翔. 青海省干旱时空特征及与大气环流响应关系[J]. 干旱区资源与环境, 2021, 35(12): 60-65.
[37] [Fan Lei, Lü Aifeng, Zhang Wenxiang. Temporal-spatial variation characteristics of drought and its relationship with atmospheric circulation in Qinghai Province[J]. Journal of Arid Land Resources and Environment, 2021, 35(12): 60-65.]
[38] Jiang Q, Li W Y, Fan Z D, et al. Evaluation of ERA5 reanalysis precipitation dataset over Chinese Mainland[J]. Journal of Hydrology, 2021, 595: 125660, doi: 10.1016/j.jhydrol.2020.125660.
[39] 马梓策, 孙鹏, 姚蕊, 等. 内蒙古地区干旱时空变化特征及其对植被的影响[J]. 水土保持学报, 2022, 36(6): 231-240.
[39] [Ma Zice, Sun Peng, Yao Rui, et al. Temporal and spatial variation of drought and its impact on vegetation in Inner Mongolia[J]. Journal of Soil and Water Conservation, 2022, 36(6): 231-240.]
Outlines

/