青藏高原被称为“亚洲水塔”和“第三极”,青藏高原水资源变化对我国乃至周边众多国家的水资源安全及人民生活均产生深远的影响。利用青藏高原周边103个气象站点及国内外8种卫星遥感与再分析数据,采用线性倾向估计、Mann-Kendall趋势检验法和相关系数法对1980—2019年青藏高原降水量变化趋势及各数据集适用性进行了研究,利用相关系数(R)、相对误差(BIAS)及均方根误差(RMSE)对这8种卫星遥感与再分析数据适用性进行了评估分析。研究结果表明:①8种降水数据集均能够反映青藏高原降水的空间格局,但精度存在明显差异,其中阳坤的CFMD数据集质量最高,相对误差为13.64%。②多种气象数据均表明近40 a青藏高原整体降水量显著上升的面积为12.8%~69.82%,各降水数据集均在地形较为复杂或降水量较低的干旱地区有着更高的相对误差,此外稀疏的实测站点也对数据集质量影响较大。③近40 a青藏高原66%地区降水量呈上升趋势,中部与北部地区上升趋势显著,而青藏高原东南部雅鲁藏布江、怒江、澜沧江及长江源区下游的降水量则呈显著的下降趋势。④青藏高原各流域降水中黄河源区降水量上升最为快速,上升速率在5 mm/a左右,怒江流域、雅鲁藏布江流域下游地区降水量下降速度最快,达到9 mm/a以上。
Abstract
The Qinghai-Tibet Plateau, renowned as the “water tower of Asia” and the “third pole,” exerts a profound influence on water security and the livelihoods of people in China and neighboring countries. This study examines the changes in water resources on the Qinghai-Tibet Plateau and their influence. To carry out this analysis, remote sensing and reanalysis data from 103 meteorological stations surrounding the plateau, as well as eight types of satellite remote sensing data from domestic and international sources, are utilized. Linear trend estimation, Mann-Kendall trend tests, and correlation coefficient methods are employed to investigate the trend of precipitation change on the Qinghai-Tibet Plateau from 1980 to 2019, as well as the suitability of each dataset. The applicability of these eight satellite remote sensing and reanalysis data is assessed by using correlation coefficients (R), relative errors (BIAS), and root mean square errors (RMSE). The findings are as follows: 1) the eight precipitation datasets can generally depict the spatial distribution of precipitation on the Qinghai-Tibet Plateau, although significant differences in accuracy exist. Among them, Yang Kun’s CFMD dataset exhibits the highest quality, with a relative error of 13.64%. 2) Various meteorological data reveal a significant increase in overall precipitation over the past four decades on the Qinghai Tibet Plateau, covering an area of 12.8%-69.82%. However, arid regions with complex topography or low precipitation display higher relative errors. Additionally, the scarcity of observation stations greatly affects dataset quality. 3) During the past four decades, approximately 66% of the Qinghai-Tibetan Plateau witnessed an upward trend in precipitation, particularly in the central and northern regions. Conversely, the Yarlung Zangbo River, Nujiang River, Lancang River, and the lower reaches in the source region of Yangtze River in the southeastern part of the plateau experienced a significant downward trend. 4) Among the plateau’s watersheds, the source area of the Yellow River observed the most rapid increase in precipitation, at a rate of approximately 5 mm/year. Conversely, the Nujiang River Basin and the lower reaches of the Yarlung Zangbo River Basin experienced the fastest decrease, exceeding 9 mm/year.
关键词
降水量 /
时空变化 /
线性倾向估计 /
Mann-Kendall趋势检验法 /
相关系数法 /
数据适用性 /
精度评估 /
青藏高原
Key words
precipitation /
temporal and spatial variation /
linear trend estimation /
Mann-Kendall trend tests method /
correlation coefficient methods /
data applicability /
accuracy evaluation /
Qinghai-Tibet Plateau
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] IMMERZEEL W W, VAN BEEK L P H, BIERKENS M F P. Climate Change Will Affect the Asian Water Towers[J]. Science, 2010, 328(5984): 1382-1385.
[2] QIU J. China: The Third Pole[J]. Nature, 2008, 454(7203): 393-396.
[3] YAO T, THOMPSON L G, MOSBRUGGER V, et al. Third Pole Environment (TPE)[J]. Environmental Development, 2012, 3: 52-64.
[4] PRITCHARD H D. Asia’s Shrinking Glaciers Protect Large Populations from Drought Stress[J]. Nature, 2019,569(7758):649-654.
[5] 李潮流, 康世昌. 青藏高原不同时段气候变化的研究综述[J]. 地理学报, 2006, 61(3): 327-335.
[6] 潘保田, 李吉均, 陈发虎. 青藏高原:全球气候变化的驱动机与放大器: Ⅰ 新生代气候变化的基本特征[J]. 兰州大学学报, 1995, 31(3): 120-128.
[7] 刘晓琼, 吴泽洲, 刘彦随, 等. 1960—2015年青海三江源地区降水时空特征[J]. 地理学报, 2019, 74(9): 1803-1820.
[8] SHEN M, PIAO S, CONG N, et al. Precipitation Impacts on Vegetation Spring Phenology on the Tibetan Plateau[J]. Global Change Biology, 2015, 21(10): 3647-3656.
[9] GAO Y H,LAN C,ZHANG Y X, et al. Changes in Moisture Flux over the Tibetan Plateau During 1979-2011 and Possible Mechanisms[J]. Journal of Climate, 2014, 27(5):1876-1893.
[10] 刘 田, 阳 坤, 秦 军, 等. 青藏高原中、东部气象站降水资料时间序列的构建与应用[J]. 高原气象, 2018, 37(6): 1449-1457.
[11] 曾琪铖,金 鑫.GPM与TRMM降水数据在柴达木盆地的精度评估[J].人民黄河,2022,44(3):16-20.
[12] 张磊磊, 康 颖, 岳青华, 等. 四种卫星降水数据在黄河源区的适用性分析[J]. 人民黄河, 2021, 43(3): 29-33.
[13] 黄 琦,王瑞敏,向俊燕,等.三种降水产品在雅砻江流域的时空适用性研究[J].水文,2020,40(4):14-21.
[14] 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.
[15] 朱艳欣, 桑燕芳. 青藏高原降水季节分配的空间变化特征[J]. 地理科学进展, 2018, 37(11): 1533-1544.
[16] 计晓龙, 吴昊旻, 黄安宁, 等. 青藏高原夏季降水日变化特征分析[J]. 高原气象, 2017, 36(5): 1188-1200.
[17] 黄 伟, 赵 虹.长序列高时空分辨率月尺度温度和降水数据集(1951—2011)[R]. 北京:国家青藏高原科学数据中心, 2019.
[18] 彭守璋. 中国1 km分辨率逐月降水量数据集(1901—2022)[R]. 时空三极环境大数据平台,2020.
[19] 何永利. 基于GPCC降水数据集的泛第三极月平均降水数据集(1891—2016)[R]. 国家青藏高原科学数据中心, 2019.
[20] 阳 坤, 何 杰, 唐文君,等. 中国区域地面气象要素驱动数据集(1979—2018)[R]. 时空三极环境大数据平台,2019.
[21] XU Jian-hua , CHEN Yan-ing , LI Wei-hong, et al. The Nonlinear Hydro-climatic Process in the Yarkand River, northwestern China[J]. Stochastic Environmental Research & Risk Assessment, 2013, 27(2):389-399.
[22] BANASIK K, HEJDUK L, HEJDUK A, et al. Long-term Variability of Runoff from a Small Catchment in the Region of the Kozienice Forest[J]. Sylwan, 2013, 157(8):578-586.
[23] 李 燕,周游游,胡宝清,等.基于TRMM数据的广西西江流域降水时空分布特征[J].亚热带资源与环境学报, 2017,12(1):75-82,88.
[24] 玉院和,王金亮.TRMM 3B43降水数据在云南地区的降尺度适用性评价[J].中国农业气象,2020, 41(9):575-586.
[25] 刘江涛,徐宗学,赵焕,等.不同降水卫星数据反演降水量精度评价:以雅鲁藏布江流域为例[J].高原气象,2019,38(2):386-396.
[26] 汪梓彤,李石宝,张志友.GPM近实时降水产品在青藏高原的多尺度精度评价[J].人民黄河,2021,43(4):43-49,116.
[27] YAN J, BRDOSSY A. Short-time Precipitation Estimation Using Weather Radar and Surface Observations: With Rainfall Displacement Information Integrated in a Stochastic Manner[J]. Journal of Hydrology, 2019, 574(15): 672-682.
[28] 刘小婵,张洪岩,赵建军,等.东北地区TRMM数据降尺度的GWR模型分析[J].地球信息科学学报,2015,17(9):1055-1062.
[29] 申双和,褚荣浩,吕厚荃,等.气候变化情景下黄淮海冬麦区降水量及其适宜度变化分析[J].中国农业气象,2015, 36(4):454-464.
[30] 冯川玉,李陈彧,周志浩,等.青藏高原降水变化特征及趋势分析[J].水文,2022,42(1):75-79.
[31] 许建伟,高艳红,彭保发,等.1979—2016年青藏高原降水的变化特征及成因分析[J].高原气象,2020,39(2):234-244.
[32] 吕春艳,李 旭,刘明歆,等.柴达木盆地1981—2017年降水及大气环流特征分析[J].沙漠与绿洲气象,2020,14(3):78-87.
[33] 李文广,彭 辉,韩 凯,等.考虑水库影响的长江上游流域降水时空变化分析[J].中国农村水利水电,2019(10):75-80.
[34] 刘新有,李自顺,刘永兴,等.怒江流域云南区段降雨时空变化分析[J].人民长江,2017,48(18):39-44.
[35] 张仪辉,刘昌明,梁 康,等.雅鲁藏布江流域降水时空变化特征[J].地理学报,2022,77(3):603-618.
[36] 王 丹,王爱慧.1901—2013年GPCC和CRU降水资料在中国大陆的适用性评估[J].气候与环境研究,2017,22(4):446-462.
[37] 王 文,汪小菊,王 鹏.GLDAS月降水数据在中国区的适用性评估[J].水科学进展, 2014, 25(6):769-778.
[38] 王忠静,石羽佳,张 腾.TRMM遥感降水低估还是高估中国大陆地区的降水[J].地球科学进展,2021,36(6):604-615.
[39] 齐文文,张百平,庞 宇,等.基于TRMM数据的青藏高原降水的空间和季节分布特征[J].地理科学,2013, 33(8):999-1005.
[40] 刘婷婷,朱秀芳,郭 锐,等.ERA5再分析降水数据在中国的适用性分析[J].干旱区地理,2022,45(1):66-79.
[41] 张仁平,张云玲,郭 靖,等.新疆地区降水分布的空间插值方法比较[J].草业科学,2018,35(3):521-529.
[42] 吴 娴, 黄 伟, 陈发虎. 1951—2012年中国大陆0.025°×0.025°高分辨率月气温和降水量格点数据集的建立及其初步应用[J]. 兰州大学学报(自然科学版), 2014, 50(2): 213-220.
[43] MOSIER T M,HILL D F,SHARP K V.30-Arcsecond Monthly Climate Surfaces with Global Land Coverage[J].International Journal of Climatology,2014,34(7):2175-2188.
[44] TIMM O E, GIAMBELLUCA T W, DIAZ H F. Statistical Downscaling of Rainfall Changes in Hawai’I Based on the CMIP5 Global Model Projections[J]. Journal of Geophysical Research: Atmospheres, 2015, 120(1): 92-112.
[45] 姜贵祥, 孙旭光. 格点降水资料在中国东部夏季降水变率研究中的适用性[J]. 气象科学, 2016, 36(4): 448-456.
[46] SCHNEIDER U, FUCHS T, MEYER-CHRISTOFFER A, et al. Global Precipitation Analysis Products of the GPCC[R]. Offenbach: Global Precipitation Climatology Centre (GPCC), DWD, 2008.
[47] 胡庆芳, 杨大文, 王银堂, 等. 赣江流域高分辨率卫星降水数据的精度特征与时空变化规律[J]. 中国科学: 技术科学, 2013, 43(4): 447-459.
[48] 邹 磊,夏 军,陈心池,等.多套降水产品精度评估与可替代性研究[J].水力发电学报,2017,36(5):36-46.
基金
“第二次青藏高原综合考察研究”(2019QZKK0608)
PDF(14685 KB)
