多源遥感降水产品在西北干旱区的气象干旱性能评估

doi:10.12118/j.issn.1000－6060.2023.146

干旱区地理 ›› 2024, Vol. 47 ›› Issue (4): 549-560.doi: 10.12118/j.issn.1000－6060.2023.146

多源遥感降水产品在西北干旱区的气象干旱性能评估

黄曼捷¹^,²(), 李艳忠¹^,²(), 王渊刚³, 于志国¹^,², 庄稼成¹^,², 星寅聪¹^,²

1.南京信息工程大学水文与水资源工程学院，江苏南京 210044
2.水利部水文气象灾害机理与预警重点实验室，江苏南京 210044
3.中国科学院新疆生态与地理研究所，新疆乌鲁木齐 830011

收稿日期:2023-04-03 修回日期:2023-04-19 出版日期:2024-04-25 发布日期:2024-05-17
通讯作者: 李艳忠（1984-），男，博士，副教授，主要从事水文气象与3S技术应用等方面的研究. E-mail: liyz_egi@163.com
作者简介:黄曼捷（2000-），女，硕士研究生，主要从事水文气象等方面的研究. E-mail: huangmj0606@163.com
基金资助:
国家自然科学基金(41701019)

Evaluation of meteorological drought performance of multisource remote-sensing precipitation products in arid northwest China

HUANG Manjie¹^,²(), LI Yanzhong¹^,²(), WANG Yuangang³, YU Zhiguo¹^,², ZHUANG Jiacheng¹^,², XING Yincong¹^,²

1. School of Hydrology and Water Resources, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China
2. Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resources, Nanjing 210044, Jiangsu, China
3. Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, Xinjiang, China

Received:2023-04-03 Revised:2023-04-19 Published:2024-04-25 Online:2024-05-17

摘要/Abstract

摘要：

多源遥感降水产品在气象站点稀少或分布不均地区的干旱监测方面，发挥着至关重要的作用，比如地处中国西北的干旱区。选择5套典型遥感降水产品（PERSIANN、CHIRPS、CMORPH、TMPA和MSWEP），基于标准化降水蒸散指数（SPEI），评估了降水产品在3种时间尺度的气象干旱性能；通过游程理论识别干旱事件，阐释了遥感降水产品捕获干旱事件的能力。研究表明：（1）在西北干旱区，5套遥感降水产品均能较好地捕捉多年平均降水量的空间分布格局，但却难以准确地捕捉降水的变化趋势。（2）在捕获SPEI性能方面，MSWEP最优，其次为TMPA、PERSIANN和CHIRPS，CMORPH表现最差。1个月尺度（SPEI1）是遥感降水产品识别气象干旱的最佳时间尺度。（3）在刻画干旱事件的特性方面，CHIRPS对干旱事件数量的识别能力最佳，PERSIANN最差；MSWEP和TMPA表征干旱严重性最好，CHIRPS较差；除CMORPH外，其余4套产品均能较好地捕获干旱事件的强度和极值。综上所述，虽然5套遥感降水产品整体上可较好地捕获西北干旱区的干旱特征，但由于受降水产品反演算法、地形复杂性以及地面验证站点疏密程度的影响，较难以找到一种降水产品在捕获干旱特性所有方面均表现最优。研究成果可为区域气象干旱监测最佳降水产品的选择，以及遥感降水产品在极端干旱环境反演算法的改进方面提供参考。

关键词: 遥感降水, SPEI, 干旱特征, 游程理论, 西北干旱区

Abstract:

Multisource remote-sensing precipitation products play an important role in drought monitoring in regions with few or uneven meteorological stations, such as arid areas in northwest China. In this study, five sets of typical remote-sensing precipitation products (PERSIANN, CHIRPS, CMORPH, TMPA, and MSWEP) were selected. The meteorological drought performance of the precipitation products at three timescales was evaluated based on the standardized precipitation evapotranspiration index (SPEI). The capability of remote-sensing precipitation products to capture drought events was explained by identifying drought events using the run-course theory. The results showed the following: (1) In arid northwest China, the five sets of remote-sensing precipitation products could capture the spatial distribution pattern of annual mean precipitation well, but it was difficult to accurately capture the change trend of precipitation. (2) MSWEP had the best performance in capturing SPEI, followed by TMPA, PERSIANN, and CHIRPS, and CMORPH had the worst performance. SPEI1 was the best timescale for remote-sensing precipitation products to identify meteorological droughts. (3) CHIRPS had the best recognition capability for several drought events, whereas PERSIANN had the worst. MSWEP and TMPA were the best indicators of drought severity, whereas CHIRPS was the worst. Except for CMORPH, the other four sets of products captured the intensity and extreme values of the drought events well. In summary, although the five sets of remote-sensing precipitation products could capture the drought characteristics of the northwest arid region on the whole, finding a precipitation product with the best performance in all aspects of capturing drought characteristics was difficult because of the impact of the inversion algorithm of falling aquatic products, terrain complexity, and density of ground verification stations. The results of this study can provide a reference for the selection of the best precipitation products for regional meteorological drought monitoring and for the improvement of remote-sensing precipitation products in the inversion algorithm of extreme drought environments.

Key words: remote sensing precipitation, SPEI, drought characteristics, run theory, arid northwest China

黄曼捷, 李艳忠, 王渊刚, 于志国, 庄稼成, 星寅聪. 多源遥感降水产品在西北干旱区的气象干旱性能评估[J]. 干旱区地理, 2024, 47(4): 549-560.

HUANG Manjie, LI Yanzhong, WANG Yuangang, YU Zhiguo, ZHUANG Jiacheng, XING Yincong. Evaluation of meteorological drought performance of multisource remote-sensing precipitation products in arid northwest China[J]. Arid Land Geography, 2024, 47(4): 549-560.

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参考文献 39

[1]	Chiang F, Mazdiyasni O, AghaKouchak A. Evidence of anthropogenic impacts on global drought frequency, duration, and intensity[J]. Nature Communications, 2021, 12(1): 2754, doi: 10.1038/s41467-021-22314-w.
[2]	许昕彤, 朱丽, 吕潇雨, 等. MSWEP降水产品在黄河流域气象干旱监测中的适用性评价[J]. 干旱区地理, 2023, 46(3): 371-384.
	[Xu Xintong, Zhu Li, Lü Xiaoyu, et al. Applicability evaluation of MSWEP precipitation product for meteorological drought monitoring in the Yellow River Basin[J]. Arid Land Geography, 2023, 46(3): 371-384. ]
[3]	Guo H, Li M, Nzabarinda V, et al. Assessment of three long-term satellite-based precipitation estimates against ground observations for drought characterization in northwestern China[J]. Remote Sensing, 2022, 14(4): 828, doi: 10.3390/rs14040828.
[4]	胡彩虹, 王金星, 王艺璇, 等. 水文干旱指标研究进展综述[J]. 人民长江, 2013, 44(7): 11-15.
	[Hu Caihong, Wang Jinxing, Wang Yixuan, et al. Review on research of hydrological drought index[J]. Yangtze River, 2013, 44(7): 11-15. ]
[5]	杨银科, 盛强, 邵鹏鲲, 等. 基于树轮密度资料的黄河干流中部地区的PDSI序列重建[J]. 水电能源科学, 2022, 40(8): 1-4, 31.
	[Yang Yinke, Sheng Qiang, Shao Pengkun, et al. Reconstruction of PDSI sequence in central part of Yellow River based on tree rings density data[J]. Water Resources and Power, 2022, 40(8): 1-4, 31. ]
[6]	Wang H, Zhang Y, Shao X. A tree-ring-based drought reconstruction from 1466 to 2013 CE for the Aksu area, western China[J]. Climatic Change, 2021, 165: 1-16.
[7]	Li Y, Zhuang J, Bai P, et al. Evaluation of three long-term remotely sensed precipitation estimates for meteorological drought monitoring over China[J]. Remote Sensing, 2022, 15(1): 86, doi: 10.3390/rs15010086.
[8]	Wu J, Liu Z, Yao H, et al. Impacts of reservoir operations on multi-scale correlations between hydrological drought and meteorological drought[J]. Journal of Hydrology, 2018, 563: 726-736.
[9]	Li L, She D, Zheng H, et al. Elucidating diverse drought characteristics from two meteorological drought indices (SPI and SPEI) in China[J]. Journal of Hydrometeorology, 2020, 21(7): 1513-1530.
[10]	Tian F, Wu J, Liu L, et al. Exceptional drought across Southeastern Australia caused by extreme lack of precipitation and its impacts on NDVI and SIF in 2018[J]. Remote Sensing, 2019, 12(1): 54, doi: 10.3390/rs12010054.
[11]	Haile G G, Tang Q, Leng G, et al. Long-term spatiotemporal variation of drought patterns over the Greater Horn of Africa[J]. Science of the Total Environment, 2020, 704: 135299, doi: 10.1016/j.scitotenv.2019.135299.
[12]	薛华柱, 李阳阳, 董国涛. 基于SPEI指数分析河西走廊气象干旱时空变化特征[J]. 中国农业气象, 2022, 43(11): 923-934.
	[Xue Huazhu, Li Yangyang, Dong Guotao. Analysis of spatial-temporal variation characteristics of meteorological drought in the Hexi Corridor based on SPEI index[J]. Chinese Journal of Agrometeorology, 2022, 43(11): 923-934. ] doi: 10.3969/j.issn.1000-6362.2022.11.006
[13]	Santos J F, Pulido-Calvo I, Portela M M. Spatial and temporal variability of droughts in Portugal[J]. Water Resources Research, 2010, 46(3): W03503, doi: 10.1029/2009WR008071.
[14]	成硕, 李艳忠, 星寅聪, 等. 遥感降水产品对黄河源区水文干旱特征的模拟性能分析[J]. 干旱区地理, 2023, 46(7): 1063-1072.
	[Cheng Shuo, Li Yanzhong, Xing Yincong, et al. Simulation performance of remote sensing precipitation products on hydrologic drought characteristics in the source region of the Yellow River[J]. Arid Land Geography, 2023, 46(7): 1063-1072. ]
[15]	Agutu N O, Awange J L, Zerihun A, et al. Assessing multi-satellite remote sensing, reanalysis, and land surface models’ products in characterizing agricultural drought in East Africa[J]. Remote Sensing of Environment, 2017, 194: 287-302.
[16]	Thavorntam W, Tantemsapya N, Armstrong L. A combination of meteorological and satellite-based drought indices in a better drought assessment and forecasting in northeast Thailand[J]. Natural Hazards, 2015, 77: 1453-1474.
[17]	郭瑞芳, 刘元波. 多传感器联合反演高分辨率降水方法综述[J]. 地球科学进展, 2015, 30(8): 891-903. doi: 10.11867/j.issn.1001-8166.2015.08.0891
	[Guo Ruifang, Liu Yuanbo. Multi-satellite retrieval of high resolution precipitation: An overview[J]. Advances in Earth Science, 2015, 30(8): 891-903. ] doi: 10.11867/j.issn.1001-8166.2015.08.0891
[18]	Nguyen P, Ombadi M, Sorooshian S, et al. The PERSIANN family of global satellite precipitation data: A review and evaluation of products[J]. Hydrology and Earth System Sciences, 2018, 22(11): 5801-5816.
[19]	Gao F, Zhang Y, Ren X, et al. Evaluation of CHIRPS and its application for drought monitoring over the Haihe River Basin, China[J]. Natural Hazards, 2018, 92: 155-172.
[20]	Alijanian M, Rakhshandehroo G R, Mishra A, et al. Evaluation of remotely sensed precipitation estimates using PERSIANN-CDR and MSWEP for spatio-temporal drought assessment over Iran[J]. Journal of hydrology, 2019, 579: 124189, doi: 10.1016/j.jhydrol.2019.124189.
[21]	Fallah A, Rakhshandehroo G R, Berg P, et al. Evaluation of precipitation datasets against local observations in southwestern Iran[J]. International Journal of Climatology, 2020, 40(9): 4102-4116.
[22]	Bai L, Wen Y, Shi C, et al. Which precipitation product works best in the Qinghai-Tibet Plateau, multi-source blended data, global/regional reanalysis data, or satellite retrieved precipitation data?[J]. Remote Sensing, 2020, 12(4): 683, doi: 10.3390/rs12040683.
[23]	Guo H, Bao A, Liu T, et al. Meteorological drought analysis in the lower Mekong Basin using satellite-based long-term CHIRPS product[J]. Sustainability, 2017, 9(6): 901, doi: 10.3390/su9060901.
[24]	Arshad M, Ma X, Yin J, et al. Evaluation of GPM-IMERG and TRMM-3B42 precipitation products over Pakistan[J]. Atmospheric Research, 2021, 249: 105341, doi: 10.1016/j.atmosres.2020.105341.
[25]	Liu J, Shangguan D, Liu S, et al. Evaluation and comparison of CHIRPS and MSWEP daily-precipitation products in the Qinghai-Tibet Plateau during the period of 1981—2015[J]. Atmospheric Research, 2019, 230: 104634, doi: 10.1016/j.atmosres.2019.104634.
[26]	Ma J Z, Wang X S, Edmunds W M. The characteristics of ground-water resources and their changes under the impacts of human activity in the arid northwest China: A case study of the Shiyang River Basin[J]. Journal of Arid Environments, 2005, 61(2): 277-295.
[27]	Al-Kilani M R, Rahbeh M, Al-Bakri J, et al. Evaluation of remotely sensed precipitation estimates from the NASA POWER project for drought detection over Jordan[J]. Earth Systems and Environment, 2021, 5(3): 561-573.
[28]	刘志红, McVicar Tim R, Van Niel T G, 等. 专用气候数据空间插值软件ANUSPLIN及其应用[J]. 气象, 2008, 34(2): 92-100.
	[Liu Zhihong, McVicar Tim R, Van Niel T G, et al. Introduction of the professional interpolation software for meteorology data: ANUSPLINN[J]. Meteorological Monthly, 2008, 34(2): 92-100. ]
[29]	钱永兰, 吕厚荃. 基于ANUSPLIN软件的逐日气象要素插值方法应用与评估[J]. 气象与环境学报, 2010, 26(2): 7-15.
	[Qian Yonglan, Lü Houquan. Application and assessment of spatial interpolation method on daily meteorological elements based on ANUSPLIN software[J]. Journal of Meteorology and Environmental, 2010, 26(2): 7-15. ]
[30]	Ashouri H, Hsu K L, Sorooshian S, et al. PERSIANN-CDR: Daily precipitation climate data record from multisatellite observations for hydrological and climate studies[J]. Bulletin of the American Meteorological Society, 2015, 96(1): 69-83.
[31]	Funk C, Peterson P, Landsfeld M, et al. The climate hazards infrared precipitation with stations: A new environmental record for monitoring extremes[J]. Scientific Data, 2015, 2(1): 1-21.
[32]	Joyce R J, Janowiak J E, Arkin P A, et al. CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution[J]. Journal of Hydrometeorology, 2004, 5(3): 487-503.
[33]	Huffman G J, Adler R F, Bolvin D T, et al. The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales[J]. Journal of Hydrometeorology, 2007, 8(3): 38-55.
[34]	Hu Q F, Yang D W, Wang Y T, et al. Accuracy and spatio-temporal variation of high resolution satellite rainfall estimate over the Ganjiang River Basin[J]. Science China: Technological Science, 2013, 56(4): 853-865.
[35]	Sun R, Yuan H, Liu X, et al. Evaluation of the latest satellite-gauge precipitation products and their hydrologic applications over the Huaihe River Basin[J]. Journal of Hydrology, 2016, 536: 302-319.
[36]	Tong K, Su F, Yang D, et al. Evaluation of satellite precipitation retrievals and their potential utilities in hydrologic modeling over the Tibetan Plateau[J]. Journal of Hydrology, 2014, 519(A): 423-437.
[37]	Zeng H, Li L, Li J. The evaluation of TRMM multisatellite precipitation analysis (TMPA) in drought monitoring in the Lancang River Basin[J]. Journal of Geographical Sciences, 2012, 22: 273-282. doi: 10.1007/s11442-012-0926-1
[38]	Gao L, Zhang Y. Spatio-temporal variation of hydrological drought under climate change during the period 1960—2013 in the Hexi Corridor, China[J]. Journal of Arid Land, 2016, 8: 157-171.
[39]	Wu J, Yao H, Chen X, et al. A framework for assessing compound drought events from a drought propagation perspective[J]. Journal of Hydrology, 2022, 604: 127228, doi: 10.1016/j.jhydrol.2021.127228.

SPEI值	干旱类型
SPEI>-0.5	无旱
-1.0<SPEI≤-0.5	轻旱
-1.5<SPEI≤-1.0	中旱
-2.0<SPEI≤-1.5	重旱
SPEI≤-2.0	特旱

多源遥感降水产品在西北干旱区的气象干旱性能评估

Evaluation of meteorological drought performance of multisource remote-sensing precipitation products in arid northwest China

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