Simulation of Manas River runoff based on ERA5-Land dataset

doi:10.13745/j.esf.sf.2022.1.50

Abstract

Abstract:

Meteorological data is a key factor in hydrological process research, and the development of reanalysis data provides a new solution for runoff simulation in data-scarce regions. In order to study the applicability of ERA5-Land reanalysis dataset in local runoff simulation, several evaluation indexes were selected to evaluate the accuracy of ERA5-Land precipitation and temperature in the Manas River basin above the Kanswat hydrological station, and the distribution characteristics of the two ERA5-Land components in the study area were analyzed by empirical orthogonal function (EOF). In terms of accuracy, ERA5-Land has a good correlation with measurement data. The precipitation detection rate was 0.96, which can reflect most precipitation events, but was 21.81% higher than the measured rate. The accuracy of temperature was better comparably, with better overall fitting accuracy, yielding an optimal range of -5-20 ℃, with increasing uncertainties near the extremum. The EOF deterministic mode showed that the precipitation and temperature flutuations in the study area followed basically the same trend, that is, they were susceptible to the influence of large-scale weather system. In the validation period, the Nash coefficients (NSE) were 0.88 and 0.82 for the final SWAT runoff models at daily and monthly scales, respectively. The ERA5-Land reanalysis dataset can provide a reference for runoff simulation in northwest China where measured meteorological data are lacking.

Key words: ERA-Land, SWAT model, runoff, EOF, Manas River, data-scarce regions

CLC Number:

P334.92
X87

TANG Hao, WANG Xiaoyun, CHEN Fulong, JIANG Long, HE Chaofei, LONG Aihua. Simulation of Manas River runoff based on ERA5-Land dataset[J]. Earth Science Frontiers, 2022, 29(3): 271-283.

Figures/Tables 19

Fig.1 Schematic diagram of the study area

Table 1 Basic data list

数据类型	数据描述	数据来源
数字高程(DEM)	ASTER GDEM 30 m×30 m	地理空间数据云
土壤数据	世界土壤数据库(HWSD)	联合国粮农组织(FAO)
土地利用数据	中国土地利用遥感数据	中科院地理科学研究所
再分析数据	ERA5-Land	哥白尼气候变化服务数据平台
实测水文气象数据	2000—2012年日径流、日降水、日平均气温数据	石河子水文勘测局

Table 2 Evaluation index calculation formula

指标	计算公式	最优值
相关系数(CC)	CC= $∑ i = 1 n (P i - P ¯) (P' i - P ¯') ∑ i = 1 n (P i - P ¯) 2 ∑ i = 1 n (P' i - P ¯') 2$	1
均方根误差(RMSE)	RMSE= $1 n ∑ 1 n (P' i - P i) 2$	0
相对误差(BIAS)	BIAS= $∑ i = 1 n (P' i - P i) ∑ i = 1 n P' i$ ×100%	0
探测率(POD)	POD= $H H + M$	1
空报率(FAR)	FAR= $F H + F$	0
临界成功指数(CSI)	CSI= $H H + M + F$	1

Table 2 Evaluation index calculation formula

指标	计算公式	最优值
相关系数(CC)	CC= $∑ i = 1 n (P i - P ¯) (P' i - P ¯') ∑ i = 1 n (P i - P ¯) 2 ∑ i = 1 n (P' i - P ¯') 2$	1
均方根误差(RMSE)	RMSE= $1 n ∑ 1 n (P' i - P i) 2$	0
相对误差(BIAS)	BIAS= $∑ i = 1 n (P' i - P i) ∑ i = 1 n P' i$ ×100%	0
探测率(POD)	POD= $H H + M$	1
空报率(FAR)	FAR= $F H + F$	0
临界成功指数(CSI)	CSI= $H H + M + F$	1

Fig.2 Division of subbasins

Fig.3 Distribution of soil types

Fig.4 Land use map

Table 3 ERA5-Land evaluation results

参数	CC	RMSE	BIAS	POD	FAR	CSI
日降水	0.7	2.66	21.81	0.96	0.46	0.56
月降水	0.73	17.96	21.81
日平均气温	0.98	3.94	-7.28
月平均气温	0.99	3.44	-7.09

Fig.5 Precipitation comparison between measurement and ERA5-Land data

Fig.6 Scatter plots of daily precipitation during 2000-2012

Fig.7 Frequency distribution of precipitation intensities and precipitation contribution rates from measurements and ERA5-Land data

Fig.8 Scatter plots of temperature

Fig.9 Spatial interpolation of local precipitation in spring (a), summer (b), autumn (c) and winter (d)

Fig.10 Spatial interpolation of local temperature in spring (a), summer (b), autumn (c) and winter (d)

Fig.11 EOF analysis results of annual precipitation

Fig.12 EOF analysis results of annual mean temperature

Table 4 Sensitivity analysis results

敏感性排序	参数名	t-Stat	p-Value	最优值
1	V__SMFMX.bsn	-22.60	0.00	1.12
2	V__SFTMP.bsn	18.14	0.00	3.43
3	V__GWQMN.gw	5.50	0.00	3684.32
4	V__SMTMP.bsn	5.48	0.00	4.13
5	R__SOL_AWC.sol	-3.04	0.00	-0.11
6	V__SMFMN.bsn	-3.02	0.00	0.92
7	V__RCHRG_DP.gw	-1.88	0.06	0.46
8	V__ALPHA_BF.gw	1.82	0.07	0.25
9	R__SOL_BD.sol	-1.81	0.07	0.18
10	V__CN2.mgt	-0.97	0.33	75.09

Fig.13 Simulation results of monthly average flow from 2002 to 2012 by SWAT model

Fig.14 Simulation results of daily mean flow from 2002 to 2012 by SWAT model

Table 5 Simulation evaluation table

评价指标	率定期		验证期
评价指标	日尺度	月尺度	日尺度	月尺度
NSE	0.84	0.91	0.82	0.88
R²	0.87	0.93	0.86	0.91

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