低空遥感结合卫星影像的河道流量反演

doi:10.12118/j.issn.1000-6060.2022.357

Abstract

Abstract:

Accurate monitoring of runoff from small and medium-sized rivers is of great significance for ecological stability in arid areas. However, it is difficult to accurately retrieve the flow of small and medium-sized rivers by remote sensing. Taking the Zhongfengchang river section of Kashi River in Nilka County, Xinjiang, China, as an example, this study constructed a power function relationship model between river width, water depth, and discharge based on the relationship fitting method and measured hydrological data, unmanned aerial vehicle data, and satellite data. Using the time series of satellite data, the runoff volume of the monitored river section was inferred 24 times in different periods. The results show that when the runoff rate is 0-50 m³·s^-1 and 50-100 m³·s^-1, the inversion of the runoff rate based on the hydraulic geometry of the river width is optimal, with root mean square errors (RMSEs) of 7.15 m³·s^-1and 2.81 m³·s^-1, respectively; when the runoff rate is 100-200 m³·s^-1 and >200 m³·s^-1, the inversion of the hydraulic geometry based on water depth and river width is the best, with RMSEs of 13.37 m³·s^-1 and 1.06 m³·s^-1, respectively. These findings provide a new method for the fine monitoring and management of runoff of small and medium-sized rivers in areas lacking hydrologic data and have high reference value for flood disaster prediction, hydropower resource development, and water ecosystem restoration.

Key words: unmanned remote sensing, Sentinel-2, river discharge, relationship fitting method, estimation

JIANG Leipeng,DING Jianli,BAO Qingling,GE Xiangyu,LIU Jingming,WANG Jinjie. Runoff estimation with low altitude remote sensing and satellite images[J].Arid Land Geography, 2023, 46(3): 385-396.

Figures/Tables 12

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References 31

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函数	H=F(W)	W=G(H)
方程	y=B₀+B₁x+B₂x²+B₃x³+B₄x⁴	y=B₀+B₁x+B₂x²+B₃x³+B₄x⁴
参数值	B₀：37.5882；B₁：30.8797 B₂：-3.65321；B₃：0.36687 B₄：-0.0143	B₀：88.38032；B₁：3.59985 B₂：-0.05631；B₃：0.00429 B₄：-0.000002
相关系数	0.98	0.98
均方根误差	0.56	0.50
残差平方和	4.77	3.51

日期（年-月-日）	概化水深/m	概化河宽/m	面积/m²	日期（年-月-日）	概化水深/m	概化河宽/m	面积/m²
2016-09-11	0.91	51.32	10264	2017-10-03	0.81	47.27	9454
2016-10-09	0.80	47.08	9416	2018-03-08	0.71	43.34	8668
2016-10-15	0.78	46.06	9212	2018-03-18	0.73	44.03	8806
2017-03-21	0.70	43.56	8712	2018-04-17	0.79	46.80	9360
2017-04-10	0.78	46.12	9224	2018-04-25	0.84	48.80	9760
2017-05-20	0.92	51.99	10398	2018-07-19	1.78	79.83	15966
2017-06-09	1.70	77.78	15556	2018-08-18	1.83	81.24	16248
2017-07-06	1.90	83.45	16690	2018-08-28	1.74	78.75	15750
2017-08-08	2.09	88.41	17682	2018-09-04	1.75	78.78	15756
2017-08-26	1.71	77.89	15578	2018-10-07	0.75	45.30	9060
2017-09-10	1.55	73.11	14622	2019-03-23	0.73	44.16	8832
2017-09-17	1.08	57.34	11468	2019-04-05	0.72	43.83	8766

方法	径流量0~50 m³·s^-1			径流量50~100 m³·s^-1
方法	RA/%	RMSE/m³·s^-1	MPE	RA/%	RMSE/m³·s^-1	MPE
式（1）	4.35	7.15	-0.15	4.38	2.81	-0.01
式（2）	42.34	19.75	0.31	16.32	10.85	-0.01
式（3）	19.42	8.86	-0.14	8.89	5.96	-0.20
方法	径流量100~200 m³·s^-1			径流量>200 m³·s^-1
方法	RA/%	RMSE/m³·s^-1	MPE	RA/%	RMSE/m³·s^-1	MPE
式（1）	13.60	22.29	-0.05	8.53	28.21	-0.01
式（2）	8.71	13.60	-0.10	3.54	11.20	0.00
式（3）	8.99	13.37	-0.05	0.38	1.06	-0.01

Runoff estimation with low altitude remote sensing and satellite images

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