雅鲁藏布江流域多源降水产品评估及其在水文模拟中的应用

doi:10.18306/dlkxjz.2020.07.006

摘要/Abstract

摘要：

论文对比分析了1980—2016年基于站点插值降水数据CMA(China Meteorological Administration)和APHRODITE(Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation)、卫星遥感降水数据PERSIANN-CDR(Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Record)和GPM(Global Precipitation Measurement)、大气再分析数据GLDAS(Global Land Data Assimilation System)以及区域气候模式输出数据HAR(High Asia Refined analysis)在雅鲁藏布江7个子流域的降水时空描述,利用国家气象站点数据对各套降水数据进行单点验证,并以这6套降水数据驱动VIC(Variable Infiltration Capacity)大尺度陆面水文模型反向评估了各套降水产品在雅鲁藏布江各子流域径流模拟中的应用潜力。结果表明：① PERSIANN-CDR和GLDAS年均降水量最高(770~790 mm),其次是HAR和GPM(650~660 mm),CMA和APHRODITE年均降水量最低(460~500 mm)。除GPM外,其他降水产品在各子流域都能表现季风流域的降水特征,约70%~90%的年降水量集中在6—9月份。② 除PERSIANN-CDR和GLDAS外,其他降水产品皆捕捉到流域降水自东南向西北递减的空间分布特征。其中,HAR数据空间分辨率最高,表现出更详细的流域内部降水空间分布特征。③ 与对应网格内的国家气象站降水数据对比显示,APHRODITE、GPM和HAR降水整体低估(低估10%~30%),且严重低估的站点主要集中在下游(低估40%~120%)。PERSIANN-CDR和GLDAS整体表现为高估上游流域站点降水(高估28%~60%),但低估下游流域站点降水(低估11%~21%)。④ 在流域径流模拟上,当前的6套降水产品在精度或时段上仍无法满足水文模型模拟的需求。⑤ 通过水文模型反向评估,6套降水产品中区域气候模式输出的HAR在流域平均降水量和季节分配上更合理。

关键词: 降水评估, 水文模拟, 雅鲁藏布江, 青藏高原

Abstract:

The gauge-based precipitation data from the National Climate Center, China Meteorological Administ-ration (CMA), Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Record (PERSIANN-CDR), Global Precipitation Measurement (GPM), Global Land Data Assimilation System (GLDAS), High Asia Refined analysis (HAR) are compared with each other and evaluated by the precipitation data from 16 national meteorological stations during 1980-2016 in the Yarlung Zangbo River and its sub-basins. The potential utilities of these multiple precipitation datasets are then systematically evaluated as inputs for the variable infiltration capacity (VIC) macroscale land surface hydrologic model. The results show that: 1) PERSIANN-CDR and GLDAS contain the largest precipitation estimates among the six datasets with mean annual precipitation of 770-790 mm, followed by the HAR and GPM (650-660 mm), while CMA and APHRODITE contain the lowest precipitation estimates with mean annual precipitation of 460-500 mm. All the products can detect the large-scale monsoon-dominated precipitation regime in the Yarlung Zangbo River and its sub-basins with 70%-90% of annual total precipitation occurring in June-September except the GPM. 2) The general spatial pattern of the annual mean precipitation fields is roughly in agreement among the six datasets, with a decreasing trend from the southeast to the northwest in the Yarlung Zangbo River Basin except the PERSIANN-CDR and GLDAS. 3) Relative to the data from the national meteorological stations, APHRODITE, GPM, and HAR generally underestimate precipitation by 10%-30%, while PERSIANN-CDR and GLDAS overestimate precipitation from stations in upstream sub-basins by 28%-60% and underestimate precipitation from stations in downstream sub-basins by 11%-21%. 4) The six precipitation datasets cannot satisfy the needs of hydrological simulation in term of accuracy or period in the basin. 5) HAR precipitation data—output of regional climate model—show more reasonable amount and seasonal pattern among the six datasets in the upper Brahmaputra according to the inverse evaluation by VIC hydrological model.

Key words: precipitation estimate, hydrological simulation, the Yarlung Zangbo River, Tibetan Plateau

孙赫, 苏凤阁. 雅鲁藏布江流域多源降水产品评估及其在水文模拟中的应用[J]. 地理科学进展, 2020, 39(7): 1126-1139.

SUN He, SU Fengge. Evaluation of multiple precipitation datasets and their potential utilities in hydrologic modeling over the Yarlung Zangbo River Basin[J]. PROGRESS IN GEOGRAPHY, 2020, 39(7): 1126-1139.

图/表 11

图1

表1

表2

图2

表3

图3

图4

表4

图5

表5

图6

参考文献 38

[1]	Duan A M, Wu G X. Role of the Tibetan Plateau thermal forcing in the summer climate patterns over subtropical Asia[J]. Climate Dynamics, 2005,24(7-8):793-807. doi: 10.1007/s00382-004-0488-8
[2]	Immerzeel W W, van Beek L P, Bierkens M F. Climate change will affect the Asian water towers[J]. Science, 2010,328:1382-1385. doi: 10.1126/science.1183188 pmid: 20538947
[3]	Yao T D, Thompson L, Yang W, et al. Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings[J]. Nature Climate Change, 2012,2(9):663-667. doi: 10.1038/NCLIMATE1580
[4]	刘天仇. 雅鲁藏布江水文特征[J]. 地理学报, 1999,54(S1):157-164.
	[ Liu Tianchou. Hydrological characteristics of Yarlung Zangbo River. Acta Geographica Sinica, 1999,54(Sl):157-164. ]
[5]	杨志刚, 卓玛, 路红亚, 等. 1961—2010年西藏雅鲁藏布江流域降水量变化特征及其对径流的影响分析[J]. 冰川冻土, 2014,36(1):166-172. doi: 10.7522/j.issn.1000-0240.2014.0021
	[ Yang Zhigang, Zhuo Ma, Lu Hongya, et al. Characteristics of precipitation variation and its effects on runoff in the Yarlung Zangbo River basin during 1961-2010. Journal of Glaciology and Geocryology, 2014,36(1):166-172. ]
[6]	陈德亮, 徐柏青, 姚檀栋, 等. 青藏高原环境变化科学评估: 过去, 现在与未来[J]. 科学通报, 2015,60(32):3025-3035.
	[ Chen Deliang, Xu Baiqing, Yao Tandong, et al. Assessment of past, present and future environmental changes on the Tibetan Plateau. Chinese Science Bulletin, 2015,60(32):3025-3035. ]
[7]	Yang W, Guo X F, Yao T D, et al. Summertime surface energy budget and ablation modeling in the ablation zone of a maritime Tibetan glacier[J]. Journal of Geophysical Research, 2011,116(D14). doi: 10.1029/2010jd015183. pmid: 24383048
[8]	Chen X N, Long D, Liang S L, et al. Developing a composite daily snow cover extent record over the Tibetan Plateau from 1981 to 2016 using multisource data[J]. Remote Sensing of Environment, 2018,215:284-299. doi: 10.1016/j.rse.2018.06.021
[9]	Li C H, Su F G, Yang D Q, et al. Spatiotemporal variation of snow cover over the Tibetan Plateau based on MODIS snow product, 2001-2014[J]. International Journal of Climatology, 2018,38(2):708-728. doi: 10.1002/joc.2018.38.issue-2
[10]	Wu Q B, Zhang T J. Changes in active layer thickness over the Qinghai-Tibetan Plateau from 1995 to 2007[J]. Journal of Geophysical Research, 2010,115(D9). doi: 10.1029/2009JD012974.
[11]	Guo D L, Wang H J. Simulation of permafrost and seasonally frozen ground conditions on the Tibetan Plateau, 1981-2010[J]. Journal of Geophysical Research: Atmospheres, 2013,118(11):5216-5230. doi: 10.1002/jgrd.50457
[12]	Zhang L L, Su F G, Yang D Q, et al. Discharge regime and simulation for the upstream of major rivers over Tibetan Plateau[J]. Journal of Geophysical Research: Atmospheres, 2013,118(15):8500-8518. doi: 10.1002/jgrd.50665
[13]	黄浠, 王中根, 桑燕芳, 等. 雅鲁藏布江流域不同源降水数据质量对比研究[J]. 地理科学进展, 2016,35(3):339-348. doi: 10.18306/dlkxjz.2016.03.008
	[ Huang Xi, Wang Zhonggen, Sang Yanfang, et al. Precision of data in three precipitation datasets of the Yarlung Zangbo River Basin. Progress in Geography, 2016,35(3):339-348. ]
[14]	吕洋, 杨胜天, 蔡明勇, 等. TRMM卫星降水数据在雅鲁藏布江流域的适用性分析[J]. 自然资源学报, 2013,28(8):1414-1425. doi: 10.11849/zrzyxb.2013.08.014
	[ Lv Yang, Yang Shengtian, Cai Mingyong, et al. The applicability analysis of TRMM precipitation data in the Yarlung Zangbo River Basin. Journal of Natural Resources, 2013,28(8):1414-1425. ]
[15]	郭禹含, 王中根, 伍玉良. 多源再分析降水数据在拉萨河流域应用对比研究[J]. 地理科学进展, 2017,36(8):1033-1039. doi: 10.18306/dlkxjz.2017.08.012
	[ Guo Yuhan, Wang Zhonggen, Wu Yuliang. Comparison of applications of different reanalyzed precipitation data in the Lhasa River Basin based on HIMS model. Progress in Geography, 2017,36(8):1033-1039. ]
[16]	Liu J T, Xu Z X, Bai J R, et al. Assessment and correction of the PERSIANN-CDR product in the Yarlung Zangbo River Basin, China[J]. Remote Sensing, 2018,10(12):2031. doi: 10.3390/rs10122031. doi: 10.3390/rs10122031
[17]	Tong K, Su F G, Yang D Q, et al. Tibetan Plateau precipitation as depicted by gauge observations, reanalyses and satellite retrievals[J]. International Journal of Climatology, 2014,34(2):265-285. doi: 10.1002/joc.3682
[18]	Qi W, Liu J G, Chen D L. Evaluations and improvements of GLDAS2.0 and GLDAS2.1 forcing data's applicability for basin scale hydrological simulations in the Tibetan Plateau[J]. Journal of Geophysical Research: Atmospheres, 2018,123(23):13128-13148.
[19]	Maussion F, Scherer D, Mölg T, et al. Precipitation seasonality and variability over the Tibetan Plateau as resolved by the high asia reanalysis[J]. Journal of Climate, 2014,27(5):1910-1927. doi: 10.1175/JCLI-D-13-00282.1
[20]	Curio J, Maussion F, Scherer D. A 12-year high-resolution climatology of atmospheric water transport over the Tibetan Plateau[J]. Earth System Dynamics, 2015,6(1):109-124.
[21]	Yatagai A, Kamiguchi K, Arakawa O, et al. APHRODITE: Constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges[J]. Bulletin of the American Meteorological Society, 2012,93(9):1401-1415. doi: 10.1175/BAMS-D-11-00122.1
[22]	Ashouri H, Hsu K, 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. doi: 10.1175/BAMS-D-13-00068.1
[23]	Huffman G J, Bolvin D T, Braithwaite D, et al. NASA global precipitation measurement (GPM) integrated multi-satellite retrievals for GPM (IMERG) [R]. Algorithm theoretical basis document, Version 4.5. Greenbelt, USA: NASA, 2015.
[24]	Rodell M, Houser P R, Jambor U, et al. The global land data assimilation system[J]. Bulletin of the American Meteorological Society, 2004,85(3):381-394. doi: 10.1175/BAMS-85-3-381
[25]	Caraway N M, McCreight J L, Rajagopalan B. Multisite stochastic weather generation using cluster analysis and k-nearest neighbor time series resampling[J]. Journal of Hydrology, 2014,508:197-213. doi: 10.1016/j.jhydrol.2013.10.054
[26]	Nasseri M, Tavakol-Davani H, Zahraie B. Performance assessment of different data mining methods in statistical downscaling of daily precipitation[J]. Journal of Hydrology, 2013,492:1-14. doi: 10.1016/j.jhydrol.2013.04.017
[27]	Ver Hoef J M, Temesgen H. A comparison of the spatial linear model to Nearest Neighbor (k-NN) methods for forestry applications[J]. PLoS One, 2013,8(3):e59129. doi: 10.1371/journal.pone.0059129. pmid: 23527110
[28]	Vicente-Serrano S M, Beguería S, López-Moreno J I, et al. A complete daily precipitation database for northeast Spain: Reconstruction, quality control, and homogeneity[J]. International Journal of Climatology, 2009,30(8):1146-1163. doi: 10.1002/joc.v30:8
[29]	Liang X, Lettenmaie D P, Wood E F, et al. A simple hydrologically based model of land-surface water and energy fluxes[J]. Journal of Geophysical Research: Atmospheres, 1994,99:14415-14428. doi: 10.1029/94JD00483
[30]	Liang X, Lettenmaie D P, Wood E F. One-dimensional statistical dynamic representation of subgrid spatial variability of precipitation in the two-layer variable infiltration capacity model[J]. Journal of Geophysical Research: Atmospheres, 1996,101(D16):21403-21422. doi: 10.1029/96JD01448
[31]	Cherkauer K A, Lettenmaier D P. Hydrologic effects of frozen soils in the upper Mississippi River basin[J]. Journal of Geophysical Research: Atmospheres, 1999,104(D16):19599-19610. doi: 10.1029/1999JD900337
[32]	Kan B Y, Su F G, Xu B Q, et al. Generation of high mountain precipitation and temperature data for a quantitative assessment of flow regime in the upper Yarkant Basin in the Karakoram[J]. Journal of Geophysical Research: Atmospheres, 2018,123(16):8462-8486. doi: 10.1029/2017JD028055
[33]	Meng F C, Su F G, Li Y, et al. Changes in terrestrial water storage during 2003-2014 and possible causes in Tibetan Plateau[J]. Journal of Geophysical Research: Atmospheres, 2019,124(6):2909-2931. doi: 10.1029/2018JD029552
[34]	Su F G, Zhang L L, Ou T H, et al. Hydrological response to future climate changes for the major upstream river basins in the Tibetan Plateau[J]. Global and Planetary Change, 2016,136:82-95. doi: 10.1016/j.gloplacha.2015.10.012
[35]	Tong K, Su F G, Yang D Q, et al. Evaluation of satellite precipitation retrievals and their potential utilities in hydrologic modeling over the Tibetan Plateau[J]. Journal of Hydrology, 2014,519:423-437. doi: 10.1016/j.jhydrol.2014.07.044
[36]	Zhao Q D, Ding Y J, Wang J, et al. Projecting climate change impacts on hydrological processes on the Tibetan Plateau with model calibration against the glacier inventory data and observed streamflow[J]. Journal of Hydrology, 2019,573:60-81. doi: 10.1016/j.jhydrol.2019.03.043
[37]	Immerzeel W W, Wanders N, Lutz A F, et al. Reconciling high-altitude precipitation in the upper Indus basin with glacier mass balances and runoff[J]. Hydrology and Earth System Sciences, 2015,19(11):4673-4687. doi: 10.5194/hess-19-4673-2015
[38]	Wortmann M, Bolch T, Menz C, et al. Comparison and correction of high-mountain precipitation data based on Glacio-hydrological modeling in the Tarim River headwaters (high Asia)[J]. Journal of Hydrometeorology, 2018,19(5):777-801.

	拉孜	拉孜—奴各沙	日喀则	拉萨	奴各沙—羊村	羊村—奴下	奴下—巴昔卡
水文观测站	拉孜站	奴各沙站	日喀则站	拉萨站	羊村站	奴下站	—
经度位置/(°E)	29.05	29.32	29.25	29.63	29.28	29.47	28.10
纬度位置/(°N)	87.38	89.71	88.88	91.15	91.88	94.57	95.53
流域面积/km²	50553	45327	11064	26235	26599	41770	51507
流域平均海拔/m	5370	4983	5353	5272	4767	4937	3711
研究时段	1980—2000年	1980—2000年	1980—2000年	1980—2000年	1980—2000年	1980—2000年	—
实测径流/(m³/s)	172.18	202.15	55.16	300.02	163.87	920.31	—
对总径流贡献/%	9.17	11.09	3.76	15.98	8.99	51.01	—
冰川面积/km²	835.40	145.90	129.50	266.50	458.14	1104.36	5351.76
冰川占比/%	1.60	0.41	1.21	0.98	1.71	2.81	10.21
积雪覆盖比例/%	15.58	7.12	6.98	23.08	10.21	24.25	31.97

降水数据	研究时段	时空分辨率	数据类别	来源
CMA	1980—2016年	1/12°、逐日	站点插值数据	国家气象局
APHRODITE	1979—2007年	0.25°、逐日	基于站点数据	Yatagai等^[21]
PERSIANN-CDR	1983—2016年	0.25°、6 h	遥感卫星数据	Ashouri等^{[22 ]}
GPM	2015—2016年	0.10°、逐日	遥感卫星数据	Huffman等^[23]
GLDAS	1980—2016年	0.25°、3 h	再分析数据	Rodell等^[24]
HAR	2001—2013年	10 km、逐日	区域气候模式输出	Maussion等^[19]

子流域	站点	APHRODITE		GPM		PERSIANN-CDR		GLDAS		HAR
子流域	站点	相关系数	相对误差/%	相关系数	相对误差/%	相关系数	相对误差/%	相关系数	相对误差/%	相关系数	相对误差/%
拉孜	拉孜	0.57	9.00	0.88	-2.03	0.57	59.51	0.38	59.01	0.60	-42.27
拉孜—奴各沙	南木林	0.99	-6.60	0.93	-89.04	0.46	28.61	0.32	39.96	0.65	-8.74
日喀则	日喀则	0.99	-0.11	0.87	-24.50	0.57	34.47	0.54	48.66	0.26	-54.61
日喀则	江孜	0.98	4.65	0.98	9.99	0.68	58.07	0.46	69.62	0.63	-4.40
拉萨	拉萨	0.99	-7.71	-0.14	-34.29	0.17	37.23	0.47	34.43	0.21	-139.09
	当雄	0.93	-0.77	0.89	-20.13	0.42	23.28	0.62	15.83	0.25	35.88
	墨竹工卡	0.99	-26.08	0.85	-70.72	0.45	23.54	0.36	20.12	0.12	-52.48
奴各沙—羊村	泽当	0.99	3.44	0.88	-10.57	0.39	57.02	0.54	46.46	0.19	-13.82
	尼木	0.94	-0.39	0.89	-51.80	0.87	45.24	0.65	55.20	0.20	-32.65
	浪卡子	0.99	1.08	0.93	6.61	0.67	47.97	0.45	59.91	0.20	39.57
	贡嘎	0.88	-13.44	0.89	-33.30	0.45	42.21	0.47	50.70	0.15	18.15
羊村—奴下	加查	0.99	7.74	0.91	-42.40	0.49	52.73	0.15	31.54	-0.20	-55.70
	米林	0.99	-6.60	0.70	11.38	0.79	28.07	0.38	27.32	0.35	-120.23
	林芝	0.98	-3.58	0.98	-9.63	0.64	29.81	0.41	8.91	0.09	-49.92
奴下—巴昔卡	嘉黎	0.99	6.26	0.08	-34.49	0.38	29.59	0.26	-19.39	0.03	18.09
奴下—巴昔卡	波密	0.99	-8.63	0.94	-50.42	0.67	-11.15	0.43	-20.95	0.09	-11.07

子流域	CMA		APHRODITE		GLDAS		PERSIANN-CDR
子流域	纳什系数	相对误差/%	纳什系数	相对误差/%	纳什系数	相对误差/%	纳什系数	相对误差/%
拉孜	0.40	-29.9	0.60	-28.6	-19.90	301.9	-51.60	383.6
拉孜—奴各沙	0.80	-29.5	0.35	-49.2	-0.70	170.7	-1.40	130.8
日喀则	0.50	-41.7	0.20	-56.3	-12.70	264.8	-3.30	159.2
拉萨	0.60	-41.6	0.50	-48.2	0.70	-35.8	0.50	34.7
奴各沙—羊村	0.20	-54.9	-0.10	-70.3	-0.20	89.1	0.60	81.4
羊村—奴下	-0.20	-69.6	-0.40	-76.4	0.10	-53.9	0.80	5.8