干旱区生态环境知识资源中心(Arid): Comparing the Performance of An Autoregressive State-Space Approach to the Linear Regression and Artificial Neural Network for Streamflow Estimation

Arid

DOI	10.3808/jei.202000440
	Comparing the Performance of An Autoregressive State-Space Approach to the Linear Regression and Artificial Neural Network for Streamflow Estimation
	Yang, Y.; Huang, T. T.; Shi, Y. Z.; Wendroth, O.; Liu, B. Y.
通讯作者	Yang, Y (corresponding author), Beijing Normal Univ, Fac Geog Sci, Sch Geog, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China.
来源期刊	JOURNAL OF ENVIRONMENTAL INFORMATICS
ISSN	1726-2135
EISSN	1684-8799
出版年	2021
卷号	37 期号:1 页码:36-48
英文摘要	Accurate streamflow estimation remains a great challenge although diverse modeling techniques have been developed during recent decades. In contrast to the process-based models, the empirical data-driven methods are easy to operate, require low computing capacity and yield fairly accurate outcomes, among which the state-space (STATE) approach takes use of the temporal structures inherent in streamflow series and serves as a feasible solution for streamflow estimation. Yet this method has rarely been applied, neither its comparison with other methods. The objective was to compare the performance of an autoregressive STATE approach to the traditional multiple linear regression and artificial neural network in simulating annual streamflow series of 15 catchments located in the Loess Plateau of China. Annual data of streamflow (Q), precipitation (P) and potential evapotranspiration (PET) during 1961 similar to 2013 were collected. The results show that STATE was generally the most accurate method for Q estimation, explaining almost 90% of the total variance averaged over all the 15 catchments. The estimation of streamflow relied on its own of the previous year for most catchments. Besides, the impacts of P and PET on the temporal distribution of streamflow were almost equal. Missing data were estimated using the STATE method, which allowed inter-annual trend analysis of the streamflow. Significant downward trends were manifested at all the 15 catchments during the study period and the corresponding slopes ranged from -0.24 to -1.71 mm y(-1). These findings hold important implications for hydrological modelling and management in China's Loess Plateau and other arid and semi-arid regions.
英文关键词	artificial neural network climatic factors Loess Plateau state-space model streamflow time series analysis
类型	Article
语种	英语
开放获取类型	Bronze
收录类别	SCI-E
WOS记录号	WOS:000642943000004
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
来源机构	北京师范大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/350786
作者单位	[Yang, Y.; Huang, T. T.; Shi, Y. Z.] Beijing Normal Univ, Fac Geog Sci, Sch Geog, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China; [Wendroth, O.] Univ Kentucky, Dept Plant & Soil Sci, Lexington, KY 40546 USA; [Liu, B. Y.] Beijing Normal Univ, Fac Geog Sci, Sch Geog, Beijing 100875, Peoples R China
推荐引用方式 GB/T 7714	Yang, Y.,Huang, T. T.,Shi, Y. Z.,et al. Comparing the Performance of An Autoregressive State-Space Approach to the Linear Regression and Artificial Neural Network for Streamflow Estimation[J]. 北京师范大学,2021,37(1):36-48.
APA	Yang, Y.,Huang, T. T.,Shi, Y. Z.,Wendroth, O.,&Liu, B. Y..(2021).Comparing the Performance of An Autoregressive State-Space Approach to the Linear Regression and Artificial Neural Network for Streamflow Estimation.JOURNAL OF ENVIRONMENTAL INFORMATICS,37(1),36-48.
MLA	Yang, Y.,et al."Comparing the Performance of An Autoregressive State-Space Approach to the Linear Regression and Artificial Neural Network for Streamflow Estimation".JOURNAL OF ENVIRONMENTAL INFORMATICS 37.1(2021):36-48.