DATA ASSIMILATION FOR CALIBRATION-PREDICTION USING SWAT MODEL

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BAYAT M.; ALIZADEH H.; MOJARADI B.

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Journal: IRAN-WATER RESOURCES RESEARCH Year:2018 | Volume:14 | Issue:1 Page(s): 1-12

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Title

DATA ASSIMILATION FOR CALIBRATION-PREDICTION USING SWAT MODEL

Author(s)

BAYAT M. | ALIZADEH H. | MOJARADI B. | Issue Writer Certificate

Keywords

CALIBRATIONQ2

PREDICTIONQ2

SWATQ1

DATA ASSIMILATIONQ1

ENSEMBLE KALMAN FILTERQ1

Abstract

This paper deals with parameter estimation of SWAT model by means of streamflow DATA ASSIMILATION and application of calibrated model for hydrological simulation of Mahabad River leading to Urmia Lake. DATA ASSIMILATION algorithem is compared with SUFI2 algorithem. SUFI2 is an uncertaintybased optimization method first developed for auto-CALIBRATION of environmental and water resource models. Due to availablity in SWAT-CUP package this is usually used for CALIBRATION of SWAT. To illustrate capabilities of DATA ASSIMILATION for CALIBRATION of the model and PREDICTION of the river discharge, ENSEMBLE KALMAN FILTER (EnKF) is utilized in a joint state-parameter estimation framework. Both coding EnKF and calling SWAT is done in MATLAB environment. Results showed that the joint state-parameter estimation using EnKF for SWAT, lead to improvement of accuracy of simulation and PREDICTION of Mahabad River’s monthly discharge at Bitass hydrometery gauge compared to parameter estimation of the model using SUFI2.

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APA: Copy

BAYAT, M., ALIZADEH, H., & MOJARADI, B.. (2018). DATA ASSIMILATION FOR CALIBRATION-PREDICTION USING SWAT MODEL. IRAN-WATER RESOURCES RESEARCH, 14(1 ), 1-12. SID. https://sid.ir/paper/100292/en

Vancouver: Copy

BAYAT M., ALIZADEH H., MOJARADI B.. DATA ASSIMILATION FOR CALIBRATION-PREDICTION USING SWAT MODEL. IRAN-WATER RESOURCES RESEARCH[Internet]. 2018;14(1 ):1-12. Available from: https://sid.ir/paper/100292/en

IEEE: Copy

M. BAYAT, H. ALIZADEH, and B. MOJARADI, “DATA ASSIMILATION FOR CALIBRATION-PREDICTION USING SWAT MODEL,” IRAN-WATER RESOURCES RESEARCH, vol. 14, no. 1 , pp. 1–12, 2018, [Online]. Available: https://sid.ir/paper/100292/en

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