CMADS在玉龙喀什河径流模拟中的适用性研究

doi:10.13866/j.azr.2022.04.10

Abstract

Abstract:

In this paper, the Yulong Kashi River Basin was selected as the research area, and the runoff simulation of SWAT hydrological model driven by The China Meteorological Assimilation Driving Datasets (CMADS) for the SWAT model was carried out to assess the accuracy of CMADS in the watershed and its applicability to the SWAT model. The results showed that (1) the correlation coefficients of precipitation, maximum temperature, and minimum temperature of CMADS with the measured hydrological stations were 0.650, 0.998, and 0.995, respectively, and their simulation of temperature was significantly better than that of precipitation. The spatial-temporal distribution characteristics of CMADS to precipitation and maximum and minimum temperatures were studied by using collaborative Kriging interpolation, which shows that the data set can well capture the topographic characteristics of the underlying surface in the study area, and its meteorological data has high quality. (2) With regard to runoff simulation of the SWAT model, except for poor simulation, the Nash-Sutcliffe efficiency coefficient of the periodic rate and validation period reached 0.845 and 0.836, respectively, and satisfactory simulation results were obtained. This paper showed that the CMADS+SWAT model had good applicability for hydrological simulation in the study area and provided an alternative to construct a hydrological model in areas with scarce ground stations, which plays a certain role in promoting hydrological simulation in cold and dry areas.

Key words: Yulong Kashi River, cold and arid area, reanalysis data, temporal and spatial distribution characteristics, SWAT model

LUO Chengyan,CHEN Fulong,HE Chaofei,LONG Aihua,QIAO Changlu. Applicability of CMADS in runoff simulation of Yulong Kashi River[J].Arid Zone Research, 2022, 39(4): 1090-1101.

Figures/Tables 13

Fig. 1

Tab. 1

Fig. 2

Fig. 3

Tab. 2

Accuracy evaluation formula"

名称	公式	范围	最优值
相关系数（Correlation Coefficient, CC）	$C C = ∑ i = 1 n [V o b s (i) - V - o b s (i)] [V s i m (i) - V - s i m (i)] ∑ i = 1 n [V o b s (i) - V - o b s (i)] 2 ∑ i = 1 n [V s i m (i) - V - s i m (i)] 2$	[-1,1]	1
均方根误差（Root Mean Square Error,RMSE）	$R M S E = 1 n ∑ i = 1 n [V s i m (i) - V o b s (i)] 2$	[0,∞]	0
相对误差（Relative Error, RE）	$R E = ∑ i = 1 n [V s i m (i) - V o b s (i)] ∑ i = 1 n V o b s (i) × 100 %$	[-∞,∞]	0
探测率（Probability of Detection, POD）	$P O D = H H + M$	[0,1]	1
误报率（False Alarm Ratio, FAR）	$F A R = F H + F$	[0,1]	0
临界成功指数（Critical Success Index, CSI）	$C S I = H H + M + F$	[0,1]	1
纳什效率系数（Nash-Sutcliffe Efficiency Coefficient,NSE）	$N S E = 1 - ∑ i = 1 n [V o b s (i) - V - s i m (i)] 2 ∑ i = 1 n [V o b s (i) - V - s i m (i)] 2$	[0,1]	1
模型决定系数（Coefficient of Determination, R²）	$R 2 = ∑ i = 1 n [V o b s (i) - V - o b s] [V s i m (i) - V - s i m] 2 ∑ i = 1 n [V o b s (i) - V - o b s] 2 ∑ i = 1 n [V s i m (i) - V - s i m] 2$	[0,1]	1

Tab. 2

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Tab. 3

Tab. 4

Fig. 9

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数据名称	数据描述	数据来源
气象数据	空间分辨率0.25°,时间为2008—2016年逐日	国家青藏高原科学数据中心,数据版本CMADS V1.1
水文数据	1969—2016年逐月径流	和田地区水利局,玉龙喀什河出山口同古孜洛克水文站
DEM数字高程数据	空间分辨率30 m,地理坐标为WGS_1984,投影坐标为WGS_1984_World_Mercator	地理空间数据云ASTER GDEM 30 m分辨率数字高程数据
土地利用数据	空间分辨率30 m,时间为2010年,投影坐标为WGS_1984_World_Mercator	中国科学院资源环境科学数据中心
土壤数据	空间分辨率1 km,时间为2008年,投影坐标为WGS_1984_World_Mercator	世界粮农组织提供世界土壤数据库（HWSD）中的中国土壤数据集（V1.2）

序号	调参方式__参数	名称	调参区间	最优值	t-Stat	P-Value
1	V__SMFMX	最大融雪因子/（mm·℃^-1·d^-1）	[1.5,6.9]	5.1345	54.7774	0.0000
2	R__ALPHA_BF	基流α因子/d	[0,1]	0.1100	-25.2418	0.0000
3	V__SMTMP	融雪基温/℃	[-5,10]	0.5000	8.4776	0.0000
4	V__GW_DELAY	地下水延迟时间/d	[0,500]	329.000	4.8323	0.0000
5	A__TLAPS	气温直减率/（℃·km^-1）	[-1.5,0]	-1.5320	2.4267	0.0154
6	R__OV_N	坡面漫流的曼宁系数	[-0.2,0.2]	-0.0090	2.1911	0.0286
7	R__CN2	初始SCS径流曲线数	[-0.2,0.2]	-0.1000	-1.5313	0.1259
8	R__SOL_BD	土壤湿容重/（g·cm^-3）	[-0.2,0.2]	-0.0278	-1.4564	0.1455
9	R__SOL_K	饱和渗透系数/（mm·h^-1）	[-0.5,0.5]	0.3000	-1.2161	0.2241
10	A__PLAPS	降水直减率/（mm·km^-1）	[0,2]	1.6500	-1.1239	0.2612
11	V__RCHRG_DP	深层含水层的渗透系数	[0,1]	0.2480	0.7908	0.4292
12	V__SFTMP	降雪气温/℃	[-5,5]	-0.0216	0.6371	0.5242
13	R__HRU_SLP	平均比降	[-0.2,0.2]	0.0587	-0.5362	0.5919
14	R__SLSUBBSN	平均坡长/m	[-0.1,0.1]	0.0097	0.4710	0.6377
15	V__GWQMN	发生回归流所需的浅层含水量的水位阀值/mm	[0,5000]	4750.0	0.4302	0.6671
16	V__SMFMN	最小融雪因子/（mm·℃^-1·d^-1）	[1.5,6.9]	4.4167	-0.2597	0.7951
17	V__ESCO	土壤蒸发补偿因子	[0,1]	0.4100	0.1570	0.8753

时期	平均值/（m³·s^-1）		纳什系数	模型决定系数	相对误差
时期	实测	模拟	纳什系数	模型决定系数	相对误差
率定期	93.678	86.214	0.845	0.8562	-10.93%
验证期	89.446	78.471	0.836	0.8153	-12.47%

Applicability of CMADS in runoff simulation of Yulong Kashi River

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