Abstract
One of the difficulties in hydrological models is the collection of meteorological data especially in large catchments. This issue is more obvious in the case of semidistributed hydrological models, which need long periods of meteorological data such as precipitation and temperature to perform watershed practices such as hydrological cycle and sustainable development. Moreover, this issue is significant for arid regions that generally have sparse data and lack of weather stations, especially in develo** countries. In this study, Climate Forecast System Reanalysis model had been applied for Soil and Water Assessment Tool hydrologic simulator generate daily flow prediction for a catchment including dry climate. Required data for development of hydrologic simulator have been prepared in Geographic Information System database. Then, model has been calibrated via semiautomatic method namely Sequential Uncertainty Fitting 2. Results of study show that application of renewed meteorological data is promising for flow prediction. Also, accuracy of model according to Nash and Sutcliffe obtained efficiency of 0.54 for calibration and 0.45 for validation, respectively. In summary, it can be concluded that results’ quality classified as good for calibration and fair for validation according to Nash and Sutcliffe efficiency.
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Acknowledgment
We deeply appreciate the Research Management Center (RMC) of Universiti Teknologi Malaysia for funding this research under postdoctoral fellowship scheme. We are thankful to all members of consultant engineers of Ab Rah Saz Shargh Corporation in Iran and the Regional Water Organization, Agricultural Organization, and Natural Resources Organization of Hormozgan province, Iran. We also wish to acknowledge Dr. Philip W. Gassman at the Center for Agricultural and Rural development, Iowa State University for introduction of SWAT model and related new contributions.
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Jajarmizadeh, M., Harun, S., Kuok, K.K., Sabari, N.S. (2015). Contribution of Climate Forecast System Meteorological Data for Flow Prediction. In: Abu Bakar, S., Tahir, W., Wahid, M., Mohd Nasir, S., Hassan, R. (eds) ISFRAM 2014. Springer, Singapore. https://doi.org/10.1007/978-981-287-365-1_8
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DOI: https://doi.org/10.1007/978-981-287-365-1_8
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