Monthly Stream-Flow Forecasting Using the ECMWF Model, Case Study: Sefidrud BasinIran

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Dehban H.; EBRAHIMI K.; ARAGHINEJAD SH.; BAZRAFSHAN J.; MODARESI F.

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Journal: IRAN-WATER RESOURCES RESEARCH Year:2020 | Volume:16 | Issue:3 Page(s): 272-281

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Title

Monthly Stream-Flow Forecasting Using the ECMWF Model, Case Study: Sefidrud BasinIran

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Climate ModelsQ1

SVRQ1

Rainfall-Runoff ModellingQ1

Abstract

Stream flow forecasting on a monthly time scale is essential for optimal water resources management and planning. In this paper using the predictions obtained from the ECMWF climate model, monthly stream flow forecast was made in Shahroud river Subbasin, part of Sefidrood basin northwest of Iran. To this end, using monthly precipitation forecasts from ECMWF climate model in tandem with SVR data-driven modelling, as a rainfall-runoff model, the stream flow was predicted based on the predicted precipitations. First, the results of precipitation forecast for the desired historical period and up to a 3-month forecast horizon for the study area were obtained from the Climate Data Store. Then, by using the SVR driven model, a linked Climate-Data-driven model was developed to predict the flow up to a 3-month forecast horizon. The results showed that flow forecasting based on climate forecasting models for the forecast horizon of the next month is more accurate than that of two and three months. The forecast horizon of the next month had the highest Nash-Sutcliffe coefficients of 0. 77 and 0. 48 in calibration and validation, respectively. It alo had the highest correlation coefficients in calibration (0. 87) and validation (0. 69), the lowest root mean square error in calibration (6. 8 million cubic meters) and validation (6. 3 million cubic meters) and moreover had the best relative bias value for calibration (0. 96) and validation (1. 1). Furthermore, based on the POD and FAR probabilistic indices, the results showed that the developed predictive model has a high ability to detect different states of stream flow events, especially for extreme flows event.

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APA: Copy

Dehban, H., EBRAHIMI, K., ARAGHINEJAD, SH., BAZRAFSHAN, J., & MODARESI, F.. (2020). Monthly Stream-Flow Forecasting Using the ECMWF Model, Case Study: Sefidrud BasinIran. IRAN-WATER RESOURCES RESEARCH, 16(3 ), 272-281. SID. https://sid.ir/paper/376482/en

Vancouver: Copy

Dehban H., EBRAHIMI K., ARAGHINEJAD SH., BAZRAFSHAN J., MODARESI F.. Monthly Stream-Flow Forecasting Using the ECMWF Model, Case Study: Sefidrud BasinIran. IRAN-WATER RESOURCES RESEARCH[Internet]. 2020;16(3 ):272-281. Available from: https://sid.ir/paper/376482/en

IEEE: Copy

H. Dehban, K. EBRAHIMI, SH. ARAGHINEJAD, J. BAZRAFSHAN, and F. MODARESI, “Monthly Stream-Flow Forecasting Using the ECMWF Model, Case Study: Sefidrud BasinIran,” IRAN-WATER RESOURCES RESEARCH, vol. 16, no. 3 , pp. 272–281, 2020, [Online]. Available: https://sid.ir/paper/376482/en

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