再分析数据在玛纳斯河径流模拟中适用性研究

doi:10.12118/j.issn.1000-6060.2023.658

Abstract

Abstract:

Meteorological data is a crucial factor in the study of hydrological processes. However, due to the complex terrain, meteorological stations in the upper reaches of the basin are scarce, limiting the study of hydrological processes in the basin due to the lack of measured data. This paper takes the Manas River Basin, Xinjiang, China as the research area and selects ERA5-Land, CMFD, and CFSR reanalysis data for analysis. The SWAT model, based on physical processes, and the AdaBoost model, based on data-driven approaches, are constructed to verify the applicability of different datasets in runoff simulation for two types of hydrological models. The Nash efficiency coefficient (NSE) and the determination coefficient (R²) are selected for quantitative analysis. The results show that: (1) The performance of the datasets in the AdaBoost model is better than that in the SWAT model. During the verification period, the NSE and R² of the ERA5-Land dataset increased by 4% and 2%; the NSE and R² of the CFSR dataset increased by 14% and 15%; the NSE and R² of the CMFD dataset changed by -10% and 8%. The NSE and R² of the meteorological station data increased by 8% and 10%. For datasets lacking data, the AdaBoost model is more applicable due to fewer restrictions on input data. (2) In the AdaBoost model, the simulation accuracy of all datasets decreased to a certain extent during the validation period, with CMFD showing the most significant decrease and ERA5-Land the least. These results indicate that the generalization ability of the AdaBoost model is weak. (3) Using ERA5-Land, CFSR, CMFD, and meteorological station data as inputs for the AdaBoost model, the simulation results show that ERA5-Land achieved good results during the verification period. The simulation accuracy of CFSR is comparable to that of meteorological stations, while CMFD performed the worst due to its inaccurate description of meteorological data in the mountainous area of the Manas River Basin. The reanalysis dataset ERA5-Land can provide a reference for runoff simulation in arid areas with insufficient measured meteorological data in northwest China.

Key words: reanalysis data sets, AdaBoost model, SWAT model, runoff simulation, Manas River Basin

LIU Bo, CHEN Fulong, TANG Hao, JIANG Long, WANG Tongxia. Applicability of reanalysis data in runoff simulation of Manas River[J].Arid Land Geography, 2024, 47(8): 1348-1357.

Figures/Tables 10

Fig. 1

Fig. 2

Tab. 1

Evaluation index calculation method"

评价指标	公式计算	最优值	注释
纳什效率系数（NSE）	$N S E = 1 - ∑ i = 1 n (Q i - Q' i) 2 ∑ i = 1 n (Q i - Q -) 2$	1	$Q i$ 为径流观测值； $Q -$ 为径流观测值均值； $Q' i$ 为径流模拟值； $Q' ¯$ 为径流模拟值均值； $P' i$ 为偏差分析中径流模拟值； $P i$ 为偏差分析中径流观测值
决定系数（R²）	$R 2 = ∑ i = 1 n (Q i - Q -) (Q' i - Q' ¯) 2 ∑ i = 1 n (Q i - Q -) 2 ∑ i = 1 n (Q' i - Q' ¯) 2$	1
相对误差（BIAS）	$B I A S = ∑ i = 1 n (P' i - P i) ∑ i = 1 n P i × 100 %$	0
平均绝对误差（MAE）	$M A E = 1 n ∑ i = 1 n P i - P' i$	0

Tab. 1

Tab. 2

Tab. 3

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Tab. 4

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数据类型	训练期		验证期
数据类型	R²	NSE	R²	NSE
ERA5-Land	0.86	0.84	0.85	0.83
CMFD	0.77	0.52	0.75	0.67
CFSR	0.79	0.76	0.64	0.61
气象站点	0.78	0.77	0.74	0.74

数据类型	训练期		验证期
数据类型	R²	NSE	R²	NSE
ERA5-Land	0.95	0.94	0.87	0.87
CMFD	0.95	0.93	0.83	0.57
CFSR	0.93	0.93	0.79	0.75
气象站点	0.93	0.92	0.84	0.82

数据类型	Q20		Q20-80		Q80
数据类型	MAE	BIAS	MAE	BIAS	MAE	BIAS
ERA5-Land	1.56	0.05	3.62	-0.01	33.99	-0.06
CFSR	1.83	0.18	6.15	0.05	40.98	-0.29
CMFD	1.63	0.03	5.58	0.13	56.20	0.26
气象站	0.92	0.03	3.42	-0.12	39.55	-0.18

Applicability of reanalysis data in runoff simulation of Manas River

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