两种河道洪水演进方法在新疆山区的应用比较

doi:10.13866/j.azr.2023.08.04

Abstract

Abstract:

We applied the two built-in channel routing methods of the WRF-Hydro model (i.e., the diffusive wave and the Muskingum-Cunge methods) to the upstream area of the Shimen Hydrological Station in the Hutubi River Basin and used GFS precipitation data for model input. The difference between the simulated and measured runoff was compared and analyzed using daily flow observations from 2015 to 2019 and annual average flow data from 1978-1983 and 2008-2019 at the Shimen Hydrological Station. We also discussed the simulation effect of the flood evolution method based on the WRF-Hydro model in the Xinjiang inland arid mountainous area. The results show that the simulation effect of the diffusive wave method is generally better than that of the Muskingum-Cunge method. This paper analyzes some characteristics of the two river routing models of WRF-Hydro to simulate floods. The diffusive wave method had better simulation results, but took longer, whereas the Muskingum-Cunge method had less ideal simulation results, but went faster.

Key words: Xinjiang, inland arid area, diffusive wave, Muskingum-Cunge, flood routing

WANG Xiang, LYU Haishen, ZHU Yonghua, GUO Chenyu. Application and comparison of two channel flood routing methods in Xinjiang mountainous areas[J].Arid Zone Research, 2023, 40(8): 1240-1247.

Figures/Tables 8

Fig. 1

Fig. 2

Tab. 1

Fig. 3

Tab. 2

Fig. 4

Tab. 3

Fig. 5

References 20

[1]	夏军. 水文学科发展与思考[J]. 中国科学基金, 2000(5): 39-43.
	[ Xia Jun. Perspective and ponderaction in hydrological science[J]. Bulletin of National Natural Science Foundation of China, 2000(5): 39-43. ]
[2]	Nikoo M, Ramezani F, Hadzima-Nyarko M, et al. Flood-routing modeling with neural network optimized by social-based algorithm[J]. Natural Hazards, 2016, 82(1): 1-24.
[3]	Usda. National Engineering Handbook, Section 4: Hydrology Chapter 17 Flood Routing[M]. Washington, DC. 1972: 2-4.
[4]	陈亚宁, 杨青, 罗毅, 等. 西北干旱区水资源问题研究思考[J]. 干旱区地理, 2012, 35(1): 1-9.
	[ Chen Yaning, Yang Qing, Luo Yi, et al. Ponder on the issues of water resources in the arid region of northwest China[J]. Arid Land Geography, 2012, 35(1): 1-9. ]
[5]	穆振侠. 天山西部山区分布式水文模型的研究[D]. 乌鲁木齐: 新疆农业大学, 2007.
	[ Mu Zhenxia. Research on Distributed Hydrological Model in Western Tianshan Moutains[D]. Urumqi: Xinjiang Agricultural University, 2007. ]
[6]	孟伟, 张远, 郑丙辉. 辽河流域水生态分区研究[J]. 环境科学学报, 2007, 27(6): 911-918.
	[ Meng Wei, Zhang Yuan, Zheng Binghui. Study of aquatic eco region in Liao River Basin[J]. Acta Scientiae Circumstantiae, 2007, 27(6): 911-918. ]
[7]	陈庭兴, 吕海深, 朱永华. 基于GEV分布的西营河流域洪水特性分析[J]. 干旱区研究, 2021, 38(6): 1563-1569.
	[ Chen Tingxing, Lyu Haishen, Zhu Yonghua. Analysis of flood characteristics in Xiying River Basin based on GEV distribution[J]. Arid Zone Research, 2021, 38(6): 1563-1569. ]
[8]	刘玉环, 李致家, 刘志雨, 等. 半湿润半干旱流域空间组合模型研究[J]. 湖泊科学, 2020, 32(3): 826-839. doi: 10.18307/2020.0322
	[ Liu Yuhuan, Li Zhijia, Liu Zhiyu, et al. Spatial combination model for semi-humid and semi-arid watersheds[J]. Journal of Lake Sciences, 2020, 32(3): 826-839. ] doi: 10.18307/2020.0322
[9]	刘昱辰, 刘佳, 李传哲, 等. WRF-Hydro模式在水文模拟与预报应用中的研究进展[J]. 水电能源科学, 2019, 37(11): 1-5.
	[ Liu Yuchen, Liu Jia, Li Chuanzhe, et al. Advances of WRF-Hydro and its application in hydrological simulation and forecasting[J]. Water Resources and Power, 2019, 37(11): 1-5. ]
[10]	刘洪波, 菅浩然, 孙明坤. WRF-Hydro模型在典型中小流域的日径流模拟研究[J]. 水文, 2021, 41(4): 48-55.
	[ Liu Hongbo, Jian Haoran, Sun Mingkun. Daily discharge simulation of small and medium-sized humid and semi-humid basins using WRF-Hydro model[J]. Journal of China Hydrology, 2021, 41(4): 48-55. ]
[11]	Sun M, Li Z, Yao C, et al. Evaluation of flood prediction capability of the WRF-Hydro model based on multiple forcing scenarios[J]. Water, 2020, 12(3): 874. doi: 10.3390/w12030874
[12]	Davy Q G M L, Joël A, Browne K N A, et al. Potential of the coupled WRF/WRF-Hydro modeling system for flood forecasting in the Ouémé River (West Africa)[J]. Water, 2022, 14(8): 1192. doi: 10.3390/w14081192
[13]	王岚, 刘志辉, 姚俊强, 等. 1978—2011年呼图壁河径流的变化趋势[J]. 水土保持通报, 2015, 35(3): 62-67.
	[ Wang Lan, Liu Zhihui, Yao Junqiang, et al. Runoff variation trend of Hutubi River during 1978-2011[J]. Bulletin of Soil and Water Conservation, 2015, 35(3): 62-67. ]
[14]	耿峻岭, 高玲, 陈建江, 等. 新疆呼图壁河流域水文特征分析[J]. 干旱区研究, 2005, 22(3): 371-376.
	[ Geng Junling, Gao Ling, Chen Jianjiang, et al. Analysis on the hydrological characteristics in the Hutubi River Basin, Xinjiang[J]. Arid Zone Research, 2005, 22(3): 371-376. ]
[15]	Shen Y, Xiong A. Validation and comparison of a new gauge-based precipitation analysis over mainland China[J]. International Journal of Climatology, 2016, 36(1): 252-265. doi: 10.1002/joc.4341
[16]	孙铭悦, 吕海深, 朱永华, 等. 2套气象数据在资料缺乏地区的适用性评估——以呼图壁河流域为例[J]. 干旱区研究, 2022, 39(1): 94-103.
	[ Sun Mingyue, Lyu Haishen, Zhu Yonghua, et al. Applicability assessment of two meteorological datasets in areas lacking data with the Hutubi River Basin as an example[J]. Arid Zone Research, 2022, 39(1): 94-103. ]
[17]	朱仟. 气候变化下降水输入和水文模型参数对水文模拟的影响[D]. 杭州: 浙江大学, 2017.
	[ Zhu Qian. Effects of Precipitation Products and Parameters in Hydrological Model on Hydrological Simulation under Climate Change[D]: Hangzhou: Zhejiang University, 2017. ]
[18]	Ryu Y, Lim Y J, Ji H S, et al. Applying a coupled hydrometeorological simulation system to flash flood forecasting over the Korean Peninsula[J]. Asia-Pacific Journal of Atmospheric Sciences, 2017, 53(4): 421-430. doi: 10.1007/s13143-017-0045-0
[19]	Naabil E, Lamptey B, Arnault J, et al. Water resources management using the WRF-Hydro modelling system: Case-study of the Tono dam in West Africa[J]. Journal of Hydrology: Regional Studies, 2017, 12: 196-209. doi: 10.1016/j.ejrh.2017.05.010
[20]	Silver M, Karnieli A, Giant H, et al. An innovative method for determining hydrological calibration parameters for the WRF-Hydro model in arid regions[J]. Environmental Modelling & software, 2017, 91: 47-69.

参数	参数含义	参数范围^[9]	率定结果
REFKDT	径流入渗参数	0.6~6.0	2.5
SLOPE	深层排水系数	0.0~1.0	0.2
MANNFAC	河道曼宁糙率乘子	0.3~2.0	0.9

模式	年份	NSE	MRE/%	R²	用时
马斯京根-康吉	2016年	0.667	3.89	0.688	112 min
	2017年	0.631	9.81	0.695
	2018年	0.638	7.41	0.678
	2019年	0.591	5.99	0.685
扩散波	2016年	0.652	3.51	0.696	296 min
	2017年	0.663	9.88	0.722
	2018年	0.665	3.49	0.678
	2019年	0.658	5.80	0.747

演进方法	洪水场次	实测洪峰 /（m³·s^-1）	预测洪峰 /（m³·s^-1）	洪峰相对误差/%	精度评价
扩散波法	1	103	105.62	2.54	√
	2	151	152.64	1.09	√
	3	89.47	96.29	7.62	-
	4	129	123.92	3.94	√
马斯京根-康吉法	1	103	103.31	0.30	√
	2	151	145.76	3.47	√
	3	89.47	97.22	8.66	-
	4	129	121.04	6.17	-

Application and comparison of two channel flood routing methods in Xinjiang mountainous areas

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 20

Related Articles 15

Metrics

Comments

Recommended 0

[1]	LI Xiaofeng, HUI Tingting, LI Yaoming, MAO Jiefei, WANG Guangyu, FAN Lianlian. Effects of different grazing management strategies on plant diversity in the mountain grassland of Xinjiang, China [J]. Arid Zone Research, 2024, 41(1): 124-134.
[2]	WANG Chao, MA Zhancang, PAN Chengnan, WU Xingyue, SONG Wendan, YAN Ping. New records of Amaranthus in Xinjiang [J]. Arid Zone Research, 2023, 40(8): 1280-1288.
[3]	Gulistan ANWAR, Turgun NURDIN, Dilhumar ABDUKERIM, Mamtimin SULAYMAN. New records of mosses of Leskeaceae to Xinjiang [J]. Arid Zone Research, 2023, 40(8): 1289-1293.
[4]	LI Hong, LI Zhongqin, CHEN Puchen, PENG Jiajia. Spatio-temporal variation of snow cover in Altai Mountains of Xinjiang in recent 20 years and its influencing factors [J]. Arid Zone Research, 2023, 40(7): 1040-1051.
[5]	XU Junli, HAN Haidong, WANG Jian. Recharge sources and potential source areas of atmospheric PM_2.5 in Xinjiang [J]. Arid Zone Research, 2023, 40(6): 874-884.
[6]	XUE Yibo, HUANG Shuangyan, ZHANG Xiaoxiao, LEI Jiaqiang, LI Shengyu. Study on the strong winter airborne dustfall mixed rain and snow events in Xinjiang, China in 2018 [J]. Arid Zone Research, 2023, 40(5): 681-690.
[7]	ZHAO Keming, SUN Mingjing, LI Xia, SHI Junjie, AN Dawei, XU Tingting. Comparison of the distribution and applicability of two typical atmospheric diffusion indices in Xinjiang [J]. Arid Zone Research, 2023, 40(5): 691-702.
[8]	ZHAO Yuzhi,YANG Jianjun. Spatio-temporal pattern of water resource carrying capacity, coupling and coordination of subsystems in southern Xinjiang [J]. Arid Zone Research, 2023, 40(2): 213-223.
[9]	DONG Hanlin, WANG Wenting, XIE Yun, Aydana YESINALI, JIANG Yuantian, XU Jiaqi. Climate dry-wet conditions, changes, and their driving factors in Xinjiang [J]. Arid Zone Research, 2023, 40(12): 1875-1884.
[10]	WU Xiaodan,LUO Min,MENG Fanhao,SA Chula,YIN Chaohua,BAO Yuhai. New characteristics of spatio-temporal evolution of extreme climate events in Xinjiang under the background of warm and humid climate [J]. Arid Zone Research, 2022, 39(6): 1695-1705.
[11]	JIANG Lei,ZHAO Yi,ZHANG Pengwei,HE Liang,BAI Xiang. Study on influence degree of phreatic evaporation based on hydrogen and oxygen isotope characteristics [J]. Arid Zone Research, 2022, 39(6): 1793-1800.
[12]	Mamtimin SULAYMAN,Alanur KAHAR,LIANG Lingwei,Mamurbieke MAKAN,WANG Pengjun. Discovery of a moss family Schistostegaceae in Xinjiang, China [J]. Arid Zone Research, 2022, 39(6): 1852-1855.
[13]	Gulistan ANWAR,WANG Pengjun,Alanur KAHAR,Mamtimin SULAYMAN. Buxbaumia viridis, a newly recorded species in Xinjiang, China and its historical correction in China’s distribution [J]. Arid Zone Research, 2022, 39(6): 1856-1861.
[14]	LU Yayan,XU Xiaoliang,LI Jicai,FENG Xiaohua,LIU Luyuan. Research on the spatio-temporal variation of carbon storage in the Xinjiang Tianshan Mountains based on the InVEST model [J]. Arid Zone Research, 2022, 39(6): 1896-1906.
[15]	WANG Jiaoyan. Study on spatiotemporal characteristics of drought in Xinjiang based on Multi-Source Weighted-Ensemble Precipitation multi-source merged precipitation product [J]. Arid Zone Research, 2022, 39(5): 1398-1409.