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

干旱区地理 ›› 2024, Vol. 47 ›› Issue (8): 1348-1357.doi: 10.12118/j.issn.1000-6060.2023.658

再分析数据在玛纳斯河径流模拟中适用性研究

刘渤¹^,²(), 陈伏龙¹^,²(), 唐豪¹^,², 姜龙³, 王统霞¹^,²

1.石河子大学水利建筑工程学院，新疆石河子 832000
2.寒旱区生态水利工程兵团重点实验室，新疆石河子 832000
3.新疆石河子水文勘测局，新疆石河子 832000

收稿日期:2023-11-21 修回日期:2024-02-04 出版日期:2024-08-25 发布日期:2024-09-02
通讯作者: 陈伏龙(1978-)，男，博士，教授，主要从事水文学及水资源问题研究. E-mail: cfl103@shzu.edu.cn
作者简介:刘渤（1998-），男，硕士研究生，主要从事水文水资源问题研究. E-mail: liubo@stu.shzu.edu.cn
基金资助:
国家自然科学基金项目(52169005);南疆重点产业创新发展支撑计划(2022DB024);兵团科技创新人才计划项目(2023CB008-08);2023年自治区研究生创新计划项目资助

Applicability of reanalysis data in runoff simulation of Manas River

LIU Bo¹^,²(), CHEN Fulong¹^,²(), TANG Hao¹^,², JIANG Long³, WANG Tongxia¹^,²

1. College of Water Conservancy & Architectural Engineering, Shihezi University, Shihezi 832000, Xinjiang, China
2. Key Laboratory of Cold and Arid Regions Eco-Hydraulic Engineering of Xinjiang Production & Construction Corps, Shihezi 832000, Xinjiang, China
3. Shihezi Hydrology Bureau, Shihezi 832000, Xinjiang, China

Received:2023-11-21 Revised:2024-02-04 Published:2024-08-25 Online:2024-09-02

摘要/Abstract

摘要：

气象数据是水文过程研究的关键因素，但是因为流域地形复杂，上游没有足够实测数据的气象站点，流域水文过程研究在较大程度上受到了限制。以玛纳斯河流域为研究区，研究对象选取CMFD、ERA5-Land和CFSR 3种再分析数据，构建基于物理过程的SWAT模型和基于数据驱动的AdaBoost模型，验证不同数据集在2种类型水文模型中径流模拟的适用性，选用纳什效率系数（NSE）和决定系数（R²）进行定量分析。结果表明:（1）数据集在AdaBoost模型中的表现要好于SWAT模型，各数据集和气象站NSE与R²均有所提升，AdaBoost模型由于对输入数据的限制更少其更适用于资料缺乏地区。（2）验证期ERA5-Land在2种模型中拟合效果均为最好（ERA5-Land+SWAT：NSE=0.83，R²=0.85；ERA5-Land+AdaBoost：NSE=0.87，R²=0.87），再分析数据集ERA5-Land可为西北实测气象资料不足的干旱区径流模拟提供参考。

关键词: 再分析数据集, AdaBoost模型, SWAT模型, 径流模拟, 玛纳斯河流域

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

刘渤, 陈伏龙, 唐豪, 姜龙, 王统霞. 再分析数据在玛纳斯河径流模拟中适用性研究[J]. 干旱区地理, 2024, 47(8): 1348-1357.

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.

图/表 10

图1

图2

表1

评价指标计算方法"

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

表1

表2

表3

图3

图4

图5

图6

表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

再分析数据在玛纳斯河径流模拟中适用性研究

Applicability of reanalysis data in runoff simulation of Manas River

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