收稿日期: 2023-11-21
修回日期: 2024-02-04
网络出版日期: 2024-09-02
基金资助
国家自然科学基金项目(52169005);南疆重点产业创新发展支撑计划(2022DB024);兵团科技创新人才计划项目(2023CB008-08);2023年自治区研究生创新计划项目资助
Applicability of reanalysis data in runoff simulation of Manas River
Received date: 2023-11-21
Revised date: 2024-02-04
Online published: 2024-09-02
气象数据是水文过程研究的关键因素,但是因为流域地形复杂,上游没有足够实测数据的气象站点,流域水文过程研究在较大程度上受到了限制。以玛纳斯河流域为研究区,研究对象选取CMFD、ERA5-Land和CFSR 3种再分析数据,构建基于物理过程的SWAT模型和基于数据驱动的AdaBoost模型,验证不同数据集在2种类型水文模型中径流模拟的适用性,选用纳什效率系数(NSE)和决定系数(R2)进行定量分析。结果表明:(1) 数据集在AdaBoost模型中的表现要好于SWAT模型,各数据集和气象站NSE与R2均有所提升,AdaBoost模型由于对输入数据的限制更少其更适用于资料缺乏地区。(2) 验证期ERA5-Land在2种模型中拟合效果均为最好(ERA5-Land+SWAT:NSE=0.83,R2=0.85;ERA5-Land+AdaBoost:NSE=0.87,R2=0.87),再分析数据集ERA5-Land可为西北实测气象资料不足的干旱区径流模拟提供参考。
关键词: 再分析数据集; AdaBoost模型; SWAT模型; 径流模拟; 玛纳斯河流域
刘渤 , 陈伏龙 , 唐豪 , 姜龙 , 王统霞 . 再分析数据在玛纳斯河径流模拟中适用性研究[J]. 干旱区地理, 2024 , 47(8) : 1348 -1357 . DOI: 10.12118/j.issn.1000-6060.2023.658
Meteorological data is a crucial factor in the study of hydrological processes. However, due to the complex terrain, meteorological stations in the upper reaches of the basin are scarce, limiting the study of hydrological processes in the basin due to the lack of measured data. This paper takes the Manas River Basin, Xinjiang, China as the research area and selects ERA5-Land, CMFD, and CFSR reanalysis data for analysis. The SWAT model, based on physical processes, and the AdaBoost model, based on data-driven approaches, are constructed to verify the applicability of different datasets in runoff simulation for two types of hydrological models. The Nash efficiency coefficient (NSE) and the determination coefficient (R2) are selected for quantitative analysis. The results show that: (1) The performance of the datasets in the AdaBoost model is better than that in the SWAT model. During the verification period, the NSE and R² of the ERA5-Land dataset increased by 4% and 2%; the NSE and R² of the CFSR dataset increased by 14% and 15%; the NSE and R2 of the CMFD dataset changed by -10% and 8%. The NSE and R2 of the meteorological station data increased by 8% and 10%. For datasets lacking data, the AdaBoost model is more applicable due to fewer restrictions on input data. (2) In the AdaBoost model, the simulation accuracy of all datasets decreased to a certain extent during the validation period, with CMFD showing the most significant decrease and ERA5-Land the least. These results indicate that the generalization ability of the AdaBoost model is weak. (3) Using ERA5-Land, CFSR, CMFD, and meteorological station data as inputs for the AdaBoost model, the simulation results show that ERA5-Land achieved good results during the verification period. The simulation accuracy of CFSR is comparable to that of meteorological stations, while CMFD performed the worst due to its inaccurate description of meteorological data in the mountainous area of the Manas River Basin. The reanalysis dataset ERA5-Land can provide a reference for runoff simulation in arid areas with insufficient measured meteorological data in northwest China.
[1] | 夏军, 左其亭. 国际水文科学研究的新进展[J]. 地球科学进展, 2006, 21(3): 256-261. |
[Xia Jun, Zuo Qiting. Advances in international hydrological science research[J]. Advances in Earth Science, 2006, 21(3): 256-261.] | |
[2] | 金倩芳. 无资料地区短期水文预报方法研究与应用[D]. 武汉: 华中科技大学, 2020. |
[Jin Qianfang. Research and application on the short-term hydrological forecasting methods in the ungauged basins[D]. Wuhan: Huazhong University of Science & Technology, 2020.] | |
[3] | Santra M S, Santra A, Kumar A. Catchment specific evaluation of Aphrodite’s and TRMM derived gridded precipitation data products for predicting runoff in a semi gauged watershed of tropical India[J]. Geocarto International, 2021, 36(11): 1292-1308. |
[4] | Himanshu S K, Pandey A, Patil A. Hydrologic evaluation of the TMPA-3B42V7 precipitation data set over an agricultural watershed using the SWAT model[J]. Journal of Hydrologic Engineering, 2018, 23(4): doi: 10.1061/(ASCE)HE.1943-5584.0001629. |
[5] | Zhang Y, Hanati G, Danierhan S, et al. Application and assessment of a downscaled GPM dataset in the simulation of snowmelt runoff in alpine mountainous areas[J]. Journal of Hydrology: Regional Studies, 2022, 41: 101107, doi: 10.1016/j.ejrh.2022.101107. |
[6] | Behrangi A, Khakbaz B, Jaw T C, et al. Hydrologic evaluation of satellite precipitation products over a mid-size basin[J]. Journal of Hydrology, 2011, 397(3-4): 225-237. |
[7] | 蒋慧敏, 刘春云, 贾健, 等. 新疆夏季对流性降水时空分布特征及成因分析[J]. 高原气象, 2019, 38(2): 340-348. |
[Jiang Huimin, Liu Chunyun, Jia jian, et al. The temporal and spatial characteristics of convective precipitation in Xinjiang among the summer and causes analysis[J]. Plateau Meteorology, 2019, 38(2): 340-348.] | |
[8] | 牛怡莹, 李春兰, 王军, 等. 内蒙古ERA5再分析降水数据性能评估与极端降水时空特征分析[J]. 干旱区地理, 2023, 46(9): 1418-1431. |
[Niu Yiying, Li Chunlan, Wang Jun, et al. Performance evaluation of ERA5 reanalysis precipitation data and spatiotemporal characteristics of extreme precipitation in Inner Mongolia[J]. Arid Land Geography, 2023, 46(9): 1418-1431.] | |
[9] | 成硕, 李艳忠, 星寅聪, 等. 遥感降水产品对黄河源区水文干旱特征的模拟性能分析[J]. 干旱区地理, 2023, 46(7): 1063-1072. |
[Cheng Shuo, Li Yanzhong, Xing Yincong, et al. Simulation performance of remote sensing precipitation products on hydrological drought characteristics in the source region of the Yellow River[J]. Arid Land Geography, 2023, 46(7): 1063-1072.] | |
[10] | Hou C Z, Huang D Q, Xu H, et al. Evaluation of ERA5 reanalysis over the deserts in northern China[J]. Theoretical and Applied Climatology, 2022, 151(1-2): 801-816. |
[11] | 谭秋阳, 徐宗学, 赵彦军, 等. CMFD数据集在雅江年楚河流域的适用性分析[J]. 北京师范大学学报(自然科学版), 2021, 57(3): 372-379. |
[Tan Qiuyang, Xu Zongxue, Zhao Yanjun, et al. Applicability of China meteorological forcing dataset to the Nianchu River Basin[J]. Journal of Beijing Normal University (Natural Science Edition), 2021, 57(3): 372-379.] | |
[12] | 黄艳伟, 李颖, 朱红雷, 等. CFSR数据集在辉发河流域水文模拟中的应用[J]. 水土保持研究, 2021, 28(1): 300-306. |
[Huang Yanwei, Li Ying, Zhu Honglei, et al. Application of CFSR dataset to hydrological simulation of Huifa River Basin[J]. Research of Soil and Water Conservation, 2021, 28(1): 300-306.] | |
[13] | 张淑芬, 董燕灵, 徐精诚, 等. 基于目标扰动的AdaBoost算法[J]. 通信学报, 2023, 44(2): 198-209. |
[Zhang Shufen, Dong Yanling, Xu Jingcheng, et al. AdaBoost algorithm based on target perturbation[J]. Journal on Communications, 2023, 44(2): 198-209.] | |
[14] | Tyralis H, Papacharalampous G. Boosting algorithms in energy research: A systematic review[J]. Neural Computing and Applications, 2021, 33(21): 1-17. |
[15] | 张圆圆, 侯艳, 李康. 多分类研究中的boosting算法[J]. 中国卫生统计, 2018, 35(1): 142-145. |
[Zhang Yuanyuan, Hou Yan, Li Kang. Boosting algorithm in multi-classification research[J]. Chinese Journal of Health Statistics, 2018, 35(1): 142-145.] | |
[16] | 孟宪贵, 郭俊建, 韩永清. ERA5再分析数据适用性初步评估[J]. 海洋气象学报, 2018, 38(1): 91-99. |
[Meng Xiangui, Guo Junjian, Han Yongqing. Preliminarily assessment of ERA5 reanalysis data[J]. Journal of Marine Meteorology, 2018, 38(1): 91-99.] | |
[17] | Zhao B, Zhang B, Shi C, et al. Comparison of the global energy cycle between Chinese reanalysis interim and ECMWF reanalysis[J]. Journal of Meteorological Research, 2019, 33(3): 563-575. |
[18] | Kang M, Chun H, Song B. Contributions of convective and orographic gravity waves to the Brewer-Bobson circulation estimated from NCEP CFSR[J]. Journal of the Atmospheric Sciences, 2020, 77(3): 981-1000. |
[19] | 刘自牧, 李国平. 高原切变线的客观识别与时空分布的统计分析[J]. 大气科学, 2019, 43(1): 13-26. |
[Liu Zimu, Li Guoping. Objective identification of the Tibetan Plateau shear line and statistical analysis of its spatiotemporal evolution features[J]. Chinese Journal of Atmospheric Sciences, 2019, 43(1): 13-26.] | |
[20] | 温婷婷, 郭英香, 董少睿, 等. 1979—2017年CRU、ERA5、CMFD格点降水数据在青藏高原适用性评估[J]. 干旱区研究, 2022, 39(3): 684-697. |
[Wen Tingting, Guo Yingxiang, Dong Shaorui, et al. Assessment of CRU, ERA5, CMFD grid precipitation data for the Tibetan Plateau from 1979 to 2017[J]. Arid Zone Research, 2022, 39(3): 684-697.] | |
[21] | 张银辉. SWAT模型及其应用研究进展[J]. 地理科学进展, 2005, 24(5): 123-132. |
[Zhang Yinhui. Development of study on model-SWAT and its application[J]. Progress in Geography, 2005, 24(5): 123-132.] | |
[22] | Fan J Z, Fan Z Y. A time series regression model via improved PCA and bagging algorithms[J]. Academic Journal of Engineering and Technology Science, 2023, 6(5): 23-29. |
[23] | 尹儒, 门昌骞, 王文剑, 等. 模型决策树: 一种决策树加速算法[J]. 模式识别与人工智能, 2018, 31(7): 643-652. |
[Yin Ru, Men Changqian, Wang Wenjian, et al. Model decision tree: An accelerated algorithm of decision tree[J]. Pattern Recognition and Artificial Intelligence, 2018, 31(7): 643-652.] | |
[24] | 唐豪, 王晓云, 陈伏龙, 等. 基于ERA5-Land数据集的玛纳斯河径流模拟研究[J]. 地学前缘, 2022, 29(3): 271-283. |
[Tang Hao, Wang Xiaoyun, Chen Fulong, et al. Simulation of Manas River runoff based on ERA5-Land dataset[J]. Earth Science Frontiers, 2022, 29(3): 271-283.] | |
[25] | 谷新晨, 肖森元, 杨广, 等. 基于CMADS和SWAT模型的玛纳斯河流域水文过程模拟[J]. 水资源与水工程学报, 2021, 32(2): 116-123. |
[Gu Xinchen, Xiao Senyuan, Yang Guang, et al. Hydrological process simulation of Manas River Basin based on CMADS and SWAT model[J]. Journal of Water Resources & Water Engineering, 2021, 32(2): 116-123.] | |
[26] | 陈伏龙, 王怡璇, 吴泽斌, 等. 气候变化和人类活动对干旱区内陆河径流量的影响——以新疆玛纳斯河流域肯斯瓦特水文站为例[J]. 干旱区研究, 2015, 32(4): 692-697. |
[Chen Fulong, Wang Yixuan, Wu Zebin, et al. Impacts of climate change and human activities on runoff of continental river in arid areas: Taking Kensiwate hydrological station in Xinjiang Manas River Basin as an example[J]. Arid Zone Research, 2015, 32(4): 692-697.] | |
[27] | 肖森元, 杨广, 何新林, 等. 玛纳斯河流域MIKE SHE水文模型率定[J]. 山地学报, 2021, 39(1): 1-9. |
[Xiao Senyuan, Yang Guang, He Xinlin, et al. Calibration of hydrological modelling by MIKE SHE for the Manas River Basin, Xinjiang, China[J]. Mountain Research, 2021, 39(1): 1-9.] |
/
〈 | 〉 |