多源再分析降水数据在拉萨河流域应用对比研究

doi:10.18306/dlkxjz.2017.08.012

摘要/Abstract

摘要：

针对资料稀缺地区水文模拟计算难题,开展多源再分析降水数据在拉萨河流域应用对比研究,本文基于HIMS系统构建了拉萨河流域分布式水文模型,以气象站实测数据为参照,对比分析了中国区域地面降水格点日值数据集和中国区域高时空分辨率地面气象要素驱动数据集两套遥感再分析数据集的气象数据在拉萨河流域的径流模拟效果。结果表明：在日和月时间尺度上,气象站实测降水数据的径流模拟精度最好,驱动集降水数据径流模拟结果要好于网格点降水数据。总体上,基于气象站实测降水数据的径流模拟纳西效率系数为0.86(日过程)和0.93(月过程),相关系数均在0.9以上。基于两类再分析数据的降水径流模拟纳西效率系数均在0.7(日过程)和0.8(月过程)以上,相关系数均在0.9左右。对于资料稀缺地区,多源再分析降水数据是重要的可用数据来源。借助于降水—径流模型,探讨多源再分析降水数据对径流模拟精度的影响,是完善多源再分析降水数据产品质量的一个重要环节。

关键词: 分布式水文模型, 多源再分析降水数据, 模拟径流, 对比分析, 拉萨河流域

Abstract:

Hydrological simulation in ungauged basins is a challenging topic in hydrology and water resource fields internationally. With the fast development of remote sensing technology, it is possible to utilize remote sensing derived precipitation data in hydrological fields to accelerate the progress of research in the PUB (predictions in ungauged basins) plan. This study compared the applications of different reanalyzed precipitation data—the grid and forcing precipitation data—in hydrological simulation in the Lhasa River Basin. The study built a distributed hydrological model using the HIMS model for the basin. The process started with inputting the daily precipitation data from the National Meteorological Center, then based on the measured flow in the Lhasa hydrological control station to calibrate and verify the hydrological model. After this, two types of remote sensing reanalyzed precipitation data in HIMS model were imported for runoff simulation, and the results were compared with simulation results of the measured weather station daily precipitation data. Subsequently, the applicability of the two types of remote sensing reanalyzed precipitation data in the Lhasa River Basin was analyzed. On the whole, the Nash-Sutcliffe efficiency coefficient of the runoff simulation based on daily precipitation data is 0.86 (daily process) and 0.93 (monthly process), and the correlation coefficient is above 0.9. The Nash-Sutcliffe efficiency coefficient of the rainfall-runoff simulations based on the two reanalyzed precipitation datasets are both above 0.7 in the daily scale process and over 0.8 in the monthly scale process, and the correlation coefficient are both around 0.9. The results show that the measured weather station daily precipitation data resulted in the best simulation outcomes and both the grid precipitation data and forcing precipitation data generate satisfactory runoff simulation results in the Lhasa River Basin. This indicates that daily precipitation data are useful although the number of rainfall stations in this area is limited. The remote sensing reanalyzed precipitation datasets can be well used in ungauged areas such as the Lhasa River Basin and it may become a reliable source when analyzing the relationship between rainfall and runoff in ungauged basins. Using rainfall-runoff model to examine the impact of multi-sources reanalyzed precipitation datasets on the accuracy of runoff simulation is essential for evaluating the quality of such datasets.

Key words: distributed hydrological model, multi-source reanalyzed precipitation data, runoff simulation, comparitive analysis, Lhasa River Basin

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.

Yuhan GUO, Zhonggen WANG, Yuliang WU. Comparison of applications of different reanalyzed precipitation data in the Lhasa River Basin based on HIMS model[J]. PROGRESS IN GEOGRAPHY, 2017, 36(8): 1033-1039.

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流域编号	土壤蓄水容量W_mm	产流参数		壤中流系数L_a	地下水补给系数R_c	实际蒸散发系数ε	基流系数K_b	汇流参数
流域编号	土壤蓄水容量W_mm	R		壤中流系数L_a	地下水补给系数R_c	实际蒸散发系数ε	r	C1	C2
1	356.0	9.00	0.10	0.23	0.01	0.10	0.01	0.06	0.05
2	50.0	0.48	0.19	0.97	0.03	1.56	0.05	0.90	0.90
3	50.0	0.22	0.99	0.57	0.09	0.15	0.02	0.05	0.06
4	50.0	5.31	0.10	0.14	0.01	0.16	0.03	0.05	0.25
5	73.0	8.65	0.16	0.20	0.01	0.86	0.02	0.05	0.05
6	50.0	3.00	0.99	0.10	0.01	0.10	0.01	0.05	0.05
7	50.0	2.88	0.10	0.19	0.02	0.96	0.01	0.06	0.05
8	1000.0	3.60	0.63	0.15	0.09	0.10	0.02	0.09	0.28
9	50.0	2.62	0.36	0.51	0.01	0.15	0.01	0.07	0.16
10	96.0	11.25	0.10	0.10	0.13	0.10	0.01	0.05	0.05
11	93.5	0.30	0.10	0.33	0.02	0.38	0.01	0.07	0.17
12	50.0	0.22	0.10	0.10	0.01	0.14	0.01	0.13	0.07
13	50.0	4.83	0.70	0.10	0.03	0.35	0.06	0.06	0.05
14	50.0	0.68	0.10	0.10	0.14	0.10	0.01	0.09	0.09
15	50.0	4.34	0.99	0.15	1.00	0.13	0.01	0.05	0.05
16	113.0	0.37	0.19	0.42	0.01	0.10	0.01	0.05	0.90

计算时期	NSE			RE			WE/%
计算时期	日尺度		月尺度		日尺度	月尺度	日尺度	月尺度
率定期(1973-2002)	0.86	0.93		0.93	0.97		-6.86	-6.91
验证期(2003-2008)	0.83	0.90		0.92	0.95		-11.57	-7.85

日尺度	率定期(1973-2002)			验证期(2003-2008)			月尺度	率定期(1973-2002)				验证期(2003-2008)
日尺度	NSE		RE	WE/%		NSE	RE	WE/%	NSE	RE	WE/%	NSE	RE	WE/%
气象站降水数据	0.86	0.93	-6.86	0.83	0.92	-11.57	气象站降水数据	0.93	0.97	-6.91		0.90	0.95	-7.85
网格点降水数据	0.74	0.89	16.33	0.80	0.89	8.23	网格点降水数据	0.81	0.93	16.32		0.88	0.94	8.16
驱动集降水数据	0.79	0.90	6.36	0.78	0.88	-2.66	驱动集降水数据	0.87	0.94	6.38		0.86	0.93	-2.66

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