多源降水产品在高寒内陆河流域的适用性和误差组分

doi:10.13866/j.azr.2025.01.05

Abstract

Abstract:

The quality of precipitation data are critical factor influencing the accuracy of runoff simulation in high-cold mountainous districts as it plays an important role in the ecological environmental protection and water resource management. The spatiotemporal characteristics of precipitation are analyzed in the headwater catchment of the Yarkant River Basin on the basis of GPM (Global Precipitation Measurement), AIMERG (the Asian precipitation dataset by calibrating the GPM-era IMERG), CMFD (China Meteorological Forcing Dataset) and ERA5 (The fifth-generation atmospheric reanalysis of the European Center for Medium-Range Weather Forecasts). Subsequently, the accuracy of the multisource precipitation data are evaluated against the observed precipitation. The error characteristics of various precipitation products was analyzed by means of the error decomposition model. The main findings were as follows: (1) The spatial pattern for CMFD and AIMERG was characterized by the increase from the north to south, which was consistent with the spatial pattern for the grid observation data set CN05.1 (the National Climate Center of China Meteorological Administration precipitation dataset). An opposite pattern was detected for ERA5 and GPM. Additionally, AIMERG and CMFD displayed higher precipitation in the glacier area. (2)The inter-annual variation characteristics of various precipitation products were significantly different, and the ratio of summer and autumn precipitation to annual precipitation for most precipitation products was more than 60%. Among all the precipitation products, only AIMERG reproduced the seasonal patterns, such as the time when the maximum monthly precipitation occurred and the peak shape for the monthly precipitation at all stations. AIMERG had the greatest ability to reproduce gauged monthly precipitation, with a higher correlation coefficient (>0.6) and lower root mean square error (8.45-11.57 mm), whereas ERA5 show the poorest ability. (3) All precipitation products showed a higher performance in reproducing daily precipitation during the wet period (from May to October) than during the dry period (from November to April). AIMERG had a greater critical success index in both wet period and dry period than for other precipitation products. (4) The dominant error of the various precipitation products in summer was the hit error, whereas the dominant error in winter varied with the precipitation product. These findings provide some reference for the runoff simulation and algorithm improvement of precipitation products in the high-cold region, where meteorological data are limited.

Key words: reanalysis data, satellite precipitation, AIMERG, error decomposition model, upper Yarkant River

XU Liuxin, WANG Wenyu, WANG Xiaoyan, WANG Xueying, GU Huanghe. Evaluation and Error decomposition of multisource precipitation data in an alpine and endorheic river watershed[J].Arid Zone Research, 2025, 42(1): 51-62.

Figures/Tables 11

Fig. 1

Tab. 1

The definition for different types of precipitation events"

	$降水产品 ≥ 阈值$	$降水产品 ≤ 阈值$
$地面降水 ≥ 阈值$	命中事件（H）	漏报事件（M）
$地面降水 ≤ 阈值$	误报事件（F）	未降雨事件（N）

Tab. 1

Fig. 2

Fig. 3

Fig. 4

Tab. 2

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Tab. 3

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站点	数据集	CC	BIAS/%	RMSE/mm
卡群站	ERA5	0.60	565.62	74.59
	GPM	0.55	-51.89	12.96
	CMFD	0.57	68.85	17.86
	AIMERG	0.75	22.54	11.57
库鲁克栏杆站	ERA5	0.83	593.57	88.39
	GPM	0.65	-16.84	9.90
	CMFD	0.51	36.09	14.06
	AIMERG	0.68	10.37	11.10
塔什库尔干站	ERA5	0.64	355.85	36.40
	GPM	0.45	65.75	14.98
	CMFD	0.96	22.67	3.88
	AIMERG	0.85	15.09	8.45

序号	涉及的降水产品	研究区	文献来源
1	TRMM3B42、GPM-IMERG、MSWEP V2.2	天山地区	[26]
2	GPM、IMERG-V06、TRMM	天山地区	[27]
3	GPM、TRMM、CMORPH	天山地区	[28]
4	GPM、TRMM、CMORPH	天山山区	[8]
5	GPM、TRMM	黑河流域	[29]
6	TRMM 3B42、CMORPH-RAW、CMORPH-CRT、APHRO、CN05.1、ITPCAS	黑河流域	[30]
7	IMERG、APHRODITE、CMPA	亚洲（含天山子区域）	[10]
8	GPM、ERA5、CHIRPS	伊犁河上游	[31]
9	TRMM、GPM、PERSIANN、CHIRPS、ERA5	开都河上游	[32]

Evaluation and Error decomposition of multisource precipitation data in an alpine and endorheic river watershed

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References 34

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