globalchange  > 过去全球变化的重建
DOI: 10.1016/j.pce.2019.05.002
WOS记录号: WOS:000471830500006
论文题名:
Comparison of the performance of SWAT, IHACRES and artificial neural networks models in rainfall-runoff simulation (case study: Kan watershed, Iran)
作者: Ahmadi, Mehdi1; Moeini, Abolfazl1; Ahmadi, Hassan2; Motamedvaziri, Baharak1; Zehtabiyan, Gholam Reza3
通讯作者: Moeini, Abolfazl
刊名: PHYSICS AND CHEMISTRY OF THE EARTH
ISSN: 1474-7065
EISSN: 1873-5193
出版年: 2019
卷: 111, 页码:65-77
语种: 英语
英文关键词: Arid and semiarid regions ; Kan watershed ; Hydrology models ; Runoff simulation
WOS关键词: RIVER-BASIN ; HYDROLOGICAL MODELS ; CLIMATE-CHANGE ; UNCERTAINTY ; CATCHMENT ; IMPACT ; FLOW ; PARAMETERIZATION ; OPTIMIZATION ; CALIBRATION
WOS学科分类: Geosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences ; Water Resources
WOS研究方向: Geology ; Meteorology & Atmospheric Sciences ; Water Resources
英文摘要:

The catchment area is essentially a heterogeneous dynamic, time and space hydrological system, and so the process of rainfall-runoff transmission in the catchment area is a very complex phenomenon. The temporal and spatial changes in the catchment characteristics, uncertainties in rainfall patterns, and a large number of parameters that alter the rainfall in to runoff, are the main sources of complexity in such relationships. Hydrological models are vital and exigent tools for water resources and environmental planning and management. In present study three models of SWAT, IHACRES and ANN were used on a daily, monthly and annual basis in the Kan watershed, which located in the west part of Tehran, Iran. The results showed that the performance of the three considered models are generally suitable for rainfall-runoff process simulation, however, ANN model showed a better performance for daily, monthly, and annual flow simulations compared with other two models (NSE= 0.86, R-2= 0.87, RMSE= 2.2, MBE= 0.08), and particularly for the simulation of maximum and minimum flow values. In addition, the performance of SWAT model (NSE= 0.65, R-2= 0.68, RMSE= 3.3, MBE= -0.168) was better than the IHACRES model (NSE= 0.57, R-2= 0.58, RMSE= 3.7, MBE= 0.049). However, the results of the IHACRES model were still acceptable.


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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/139456
Appears in Collections:过去全球变化的重建

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作者单位: 1.Islamic Azad Univ, Dept Forest Range & Watershed Management, Sci & Res Branch, Tehran, Iran
2.Univ Tehran, Coll Agr & Nat Resources, Karaj, Iran
3.Univ Tehran, Dept Nat Resources, Karaj, Iran

Recommended Citation:
Ahmadi, Mehdi,Moeini, Abolfazl,Ahmadi, Hassan,et al. Comparison of the performance of SWAT, IHACRES and artificial neural networks models in rainfall-runoff simulation (case study: Kan watershed, Iran)[J]. PHYSICS AND CHEMISTRY OF THE EARTH,2019-01-01,111:65-77
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