额尔齐斯河库威站日尺度的降雨融雪径流模拟

doi:10.13866/j.azr.2024.10.07

摘要/Abstract

摘要：

额尔齐斯河流域受地理条件的影响，流域内水文气象站点较少，基础资料匮乏，而融雪洪水在该流域的汛期及水资源管理上有着较大影响。本研究通过应用降水和气温的再分析产品及AVHRR积雪数据，利用K-means聚类法进行不同径流时期特点的划分，并在不同时期构建相应SRM+LSTM模型，并使用2009年数据及2023年实地观测的径流数据进行验证。结果表明：再分析产品CMFD能够较好地应用于额尔齐斯河流域，并能根据降水、温度、积雪及径流间的关系得到不同径流划分时期，即12月11日—次年4月10日为积雪退水期、4月11日—8月10日为融雪降水产流期、8月11日为降水产流期。SRM模型模拟效果较差，大部分径流纳什效率系数（NSE）<0；而SRM+LSTM模型能够较好地模拟该流域的不同时期的径流，决定系数R²均能达到0.5以上，纳什效率系数也能达到0.5以上，证明SRM+LSTM模型能够较好地应用于该地区，精度较高。

关键词: K-means聚类法, SRM模型, LSTM模型, 径流模拟, 额尔齐斯河

Abstract:

Due to geographical conditions, there are limited hydrometeorological stations and a lack of basic data in the Irtysh River Basin, and the snowmelt flood exerts a considerable effect on the flood season and water resources management in the basin. In this study, precipitation and temperature reanalysis products and AVHRR snow cover data were applied, the K-means clustering method was used to divide the characteristics of different runoff periods, the corresponding SRM+LSTM model in different periods was constructed, and the runoff data observed in the field in 2023 were used. Results showed that the reanalysis product CMFD can be well applied to the Irtysh River Basin according to precipitation and temperature. The relationship between snow cover and runoff was divided into different runoff periods, as follows: December 11th to April 10th of the following year was the snow retreat period, April 11th to August 10th was the snowmelt precipitation runoff period, and August 11th was the precipitation runoff period. The simulation effect of the SRM model was poor, and the Nash efficiency coefficient of most runoff was<0. The SRM+LSTM model could better simulate the runoff in different periods of the basin, the deterministic coefficient could reach>0.5, and the Nash efficiency coefficient NSE could also reach>0.5, which confirms that the SRM+LSTM model can be better applied to the area with high accuracy.

Key words: K-means clustering method, SRM model, LSTM model, runoff simulation, Irtysh River

赵文龙, 吕海深, 朱永华, 刘涵, 吴卓珺. 额尔齐斯河库威站日尺度的降雨融雪径流模拟[J]. 干旱区研究, 2024, 41(10): 1685-1698.

ZHAO Wenlong, LYU Haishen, ZHU Yonghua, LIU Han, WU Zhuojun. Simulation of rainfall and snowmelt runoff on the daily scale of the Kuwei Station in the Irtysh River[J]. Arid Zone Research, 2024, 41(10): 1685-1698.

图/表 16

图1

表1

图2

图3

图4

表2

表3

图5

图6

图7

表4

图8

图9

图10

图11

图12

参考文献 34

[1]	Wu Xuejiao, Zhang Wei, Li Hongyi, et al. Analysis of seasonal snowmelt contribution using a distributed energy balance model for a river basin in the Altai Mountains of northwestern China[J]. Hydrological Processes, 2021, 35(3): e14046.
[2]	杨金明, 李诚志, 房世峰, 等. 新疆地区季节性融雪洪水模拟与预报研究[J]. 新疆大学学报(自然科学版), 2019, 36(1): 80-88.
	[Yang Jinming, Li Chengzhi, Fang Shifeng, et al. Simulation and forecasting of seasonal snowmelt flood in Xinjiang[J]. Journal of Xinjiang University (Natural Science Edition), 2019, 36(1): 80-88.]
[3]	余其鹰, 胡彩虹, 白云岗, 等. 新疆洪水预报预警中融雪径流模型应用进展[J]. 干旱区地理, 2023, 46(12): 1951-1962. doi: 10.12118/j.issn.1000-6060.2023.153
	[Yu Qiying, Hu Caihong, Bai Yungang, et al. Progress in the application of snowmelt runoff model in flood forecasting and early warning in Xinjiang[J]. Arid Land Geography, 2023, 46(12): 1951-1962.] doi: 10.12118/j.issn.1000-6060.2023.153
[4]	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. doi: 10.13866/j.azr.2021.06.08
	[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.] doi: 10.13866/j.azr.2021.06.08
[5]	郭晨煜, 吕海深, 朱永华, 等. WRF-Hydro模型参数在河西内陆河流域的敏感性分析[J]. 水利水电科技进展, 2023, 43(6): 120-127.
	[Guo Chenyu, Lyu Haishen, Zhu Yonghua, et al. Sensitivity analysis of WRF-Hydro model parameters in hexi inland river basin[J]. Progress in Water Resources and Hydropower Science and Technology, 2023, 43(6): 120-127.]
[6]	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. doi: 10.13866/j.azr.2022.01.10
	[Sun Mingyue, Lyu Haishen, Zhu Yonghua, et al. Evaluation of the applicability of two sets of meteorological data in areas lacking data: A case study of Hutubi River Basin[J]. Arid Zone Research, 2022, 39(1): 94-103.] doi: 10.13866/j.azr.2022.01.10
[7]	张梅洁, 吕海深, 刘娣, 等. 耦合融雪的新安江模型在干旱区径流模拟研究[J]. 干旱区研究, 2022, 39(2): 379-387. doi: 10.13866/j.azr.2022.02.05
	[Zhang Meijie, Lyu Haishen, Liu Di, et al. Runoff simulation of Xin’anjiang model coupled with snow melting in arid region[J]. Arid Zone Research, 2022, 39(2): 379-387.] doi: 10.13866/j.azr.2022.02.05
[8]	李彬权, 荣伟光, 吴亚琪, 等. 大渡河流域融雪期划分方法研究[J]. 水电能源科学, 2023, 41(5): 38-42.
	[Li Binquan, Rong Weiguang, Wu Yaqi, et al. Study on the division method of snowmelt period in Dadu River Basin[J]. Hydropower Energy Science, 2023, 41(5): 38-42.]
[9]	杨春辉, 张玥, 李峰平. 寒区春汛产流预报方法研究[J]. 中国农村水利水电, 2019(5): 43-46.
	[Yang Chunhui, Zhang Yue, Li Fengping. Research on spring flood runoff forecasting method in cold regions[J]. China Rural Water Resources and Hydropower, 2019(5): 43-46.]
[10]	田琳. 中温带地区春季融雪径流研究[D]. 长春: 吉林大学, 2019.
	[Tian Lin. Study on Spring Snowmelt Runoff in the Middle Temperate Zone[D]. Changchun: Jilin University, 2019.]
[11]	邢贞相, 金超群, 纪毅, 等. 基于SWAT模型与Copula修正的融雪径流模拟[J]. 东北农业大学学报, 2020, 51(6): 79-87.
	[Xing Zhenxiang, Jin Chaoqun, Ji Yi, et al. Simulation of snowmelt runoff based on SWAT model and Copula modification[J]. Journal of Northeast Agricultural University, 2020, 51(6): 79-87.]
[12]	汪伟, 方秀琴, 杜晓彤, 等. 基于VIC模型的柳江流域分布式水文模拟与应用[J]. 水土保持研究, 2020, 27(3): 328-335.
	[Wang Wei, Fang Xiuqin, Du Xiaotong, et al. Distributed hydrological simulation and application of Liujiang River Basin based on VIC model[J]. Soil and Water Conservation Research, 2020, 27(3): 328-335.]
[13]	陈心池, 张利平, 闪丽洁, 等. 新疆山区中小河流洪水预报模型及其应用[J]. 干旱区研究, 2017, 34(6): 1426-1435.
	[Chen Xinchi, Zhang Liping, Shan Lijie, et al. Flood forecasting model for small and medium-sized rivers in mountainous areas of Xinjiang and its application[J]. Arid Zone Research, 2017, 34(6): 1426-1435.]
[14]	Martinec J. Snowmelt-runoff model for stream flow forecasts[J]. Hydrology Research, 1975, 6(3): 145-154.
[15]	Siemens K, Dibike Y, Shrestha R R, et al. Runoff projection from an alpine watershed in western Canada: Application of a snowmelt runoff model[J]. Water, 2021, 13(9): 1199.
[16]	谢顺平, 都金康, 冯学智, 等. 融雪径流模型参数渐进式优化率定方法[J]. 南京大学学报(自然科学), 2015, 51(5): 1005-1013.
	[Xie Shunping, Du Jinkang, Feng Xuezhi, et al. Parameter progressive optimization calibration method for snowmelt runoff model[J]. Journal of Nanjing University (Natural Science), 2015, 51(5): 1005-1013.]
[17]	Sharma T P P, Zhang Jiahua, Khanal N R, et al. Assimilation of snowmelt runoff model (SRM) using satellite remote sensing data in Budhi Gandaki River Basin, Nepal[J]. Remote Sensing, 2020, 12(12): 1951.
[18]	刘攀, 郑雅莲, 谢康, 等. 水文水资源领域深度学习研究进展综述[J]. 人民长江, 2021, 52(10): 76-83.
	[Liu Pan, Zheng Yalian, Xie Kang, et al. Review of research progress on deep learning in the field of hydrology and water resources[J]. People’s Yangtze River, 2021, 52(10): 76-83.]
[19]	Xiang Zhongrun, Yan Jun, Demir I. A rainfall-runoff model with LSTM-based sequence-to-sequence learning[J]. Water resources research, 2020, 56(1): e2019WR025326.
[20]	Garg N, Negi S, Nagar R, et al. Multivariate multi-step LSTM model for flood runoff prediction: A case study on the Godavari River Basin in India[J]. Journal of Water and Climate Change, 2023, 14(10): 3635-3647.
[21]	殷兆凯, 廖卫红, 王若佳, 等. 基于长短时记忆神经网络(LSTM)的降雨径流模拟及预报[J]. 南水北调与水利科技, 2019, 17(6): 1-9.
	[Yin Zhaokai, Liao Weihong, Wang Ruojia, et al. Rainfall runoff simulation and forecast based on long short-term memory neural network (LSTM)[J]. South-to-North Water Diversion and Water Science and Technology, 2019, 17(6): 1-9.]
[22]	殷仕明, 徐炜, 熊一橙, 等. 基于迁移学习的长短时记忆神经网络水文模型[J]. 水力发电学报, 2022, 41(6): 53-64.
	[Yin Shiming, Xu Wei, Xiong Yicheng, et al. Hydrological model of long short-term memory neural network based on transfer learning[J]. Journal of Hydroelectric Engineering, 2022, 41(6): 53-64.]
[23]	Cui Zhen, Zhou Yanlai, Guo Shenglian, et al. A novel hybrid XAJ-LSTM model for multi-step-ahead flood forecasting[J]. Hydrology Research, 2021, 52(6): 1436-1454.
[24]	Chen Shengyue, Huang Jinliang, Huang Jr-Chuang. Improving daily streamflow simulations for data-scarce watersheds using the coupled SWAT-LSTM approach[J]. Journal of Hydrology, 2023, 622: 129734.
[25]	Xiao Qintai, Zhou Li, Xiang Xin, et al. Integration of hydrological model and time series model for improving the runoff simulation: A case study on BTOP model in Zhou River Basin, China[J]. Applied Sciences, 2022, 12(14): 6883.
[26]	高黎明, 张乐乐, 沈永平, 等. ERA-Interim和CMFD气象驱动数据在新疆额尔齐斯河流域的适用性评价[J]. 冰川冻土, 2022, 44(1): 179-187. doi: 10.7522/j.issn.1000-0240.2022.0029
	[Gao Liming, Zhang Lele, Shen Yongping, et al. Applicability of ERA-Interim and CMFD weather-driven data in the Irtysh River Basin of Xinjiang[J]. Glaciology and Geocryology, 2022, 44(1): 179-187.]
[27]	郝晓华, 赵琴, 纪文政, 等. 1980—2020年AVHRR中国积雪物候数据集[J]. 中国科学数据(中英文网络版), 2022, 7(3): 50-59.
	[Hao Xiaohua, Zhao Qin, Ji Wenzheng, et al. AVHRR snow phenology dataset in China from 1980 to 2020[J]. Chinese Scientific Data Online, 2022, 7(3): 50-59.]
[28]	He Jie, Yang Kun, Tang Wenjun, et al. The first high-resolution meteorological forcing dataset for land process studies over China[J]. Scientific Data, 2020, 7(1): 25.
[29]	Chen Hongju, Yang Jianping, Ding Yongjian, et al. Simulation of daily snow depth data in China based on the NEX-GDDP[J]. Water, 2021, 13(24): 3599.
[30]	Hochreiter Sepp, Schmidhuber Jurgen. Long short-term memory[J]. Neural computation, 1997, 9(8): 1735-1780. doi: 10.1162/neco.1997.9.8.1735 pmid: 9377276
[31]	Jain A K, Murty M N, Flynn P J. Data clustering: A review[J]. ACM Computing Surveys (CSUR), 1999, 31(3): 264-323.
[32]	金浩宇, 鞠琴. SRM模型在尼洋河流域的应用研究[J]. 水文, 2019, 39(5): 19-24.
	[Jin Haoyu, Ju Qin. Application of SRM model in Niyang River Basin[J]. Journal of China Hydrology, 2019, 39(5): 19-24.]
[33]	张伟, 沈永平, 贺建桥, 等. 额尔齐斯河源区森林对春季融雪过程的影响评估[J]. 冰川冻土, 2014, 36(5): 1260-1270. doi: 10.7522/j.issn.1000-0240.2014.0151
	[Zhang Wei, Shen Yongping, He Jianqiao, et al. Impact assessment of forests on spring snowmelt process in the headwaters of the Irtysh River[J]. Glaciology and Geocryology, 2014, 36(5): 1260-1270.]
[34]	李巧媛, 谢自楚. 高原区气温垂直递减率的分布及其特点分析——以青藏高原及其周边地区为例[J]. 石河子大学学报(自然科学版), 2006, 24(6): 719-723.
	[Li Qiaoyuan, Xie Zichu. Distribution and characteristics of vertical temperature decline rate in plateau region: A case study of the Tibetan Plateau and its surrounding areas[J]. Journal of Shihezi University (Natural Science Edition), 2006, 24(6): 719-723.]

分带	高程范围/m	平均高程/m	面积占比累计/%	分带面积/km²
1	1017~1517	1267	2.4	59.86
2	1517~2017	1767	24.0	538.71
3	2017~2517	2267	60.0	897.84
4	2517~3017	2767	93.6	837.98
5	3017~3854	3436	100.0	159.62

时期		年份	SRM		SRM+LSTM
时期		年份	R²	NSE	R²	NSE
积雪退水期	率定期	2007年	0.278	0.266	0.922	0.878
积雪退水期	率定期	2008年	0.815	<0	0.925	0.902
融雪降水产流期	验证期	2009年	0.689	<0	0.817	0.555
	率定期	2007年	0.160	<0	0.441	0.422
	率定期	2008年	0.496	<0	0.834	0.814
降水产流期	验证期	2009年	0.334	0.082	0.314	0.277
	率定期	2007年	0.073	<0	0.815	0.646
	率定期	2008年	0.186	<0	0.513	0.468
融雪降水产流期	验证期	2009年	0.420	<0	0.753	0.749
融雪降水产流期	验证期	2023年	0.494	0.060	0.834	0.628