Surface water quality modeling in Jiroft dam watershed

Document Type : Research Paper

Authors

1 Master of Civil Engineering, Water Resources Management, Department of Water Engineering, Faculty of Civil and Surveying Engineering, Graduate University of Advanced Technology, Kerman, Iran.

2 Assisstant Professor, Department of Energy, Institute of Science and High Technology and Environmental Science, Graduate University of Advanced Technology, Kerman, Iran.

3 Assisstant Professor, Department of Ecology, Institute of Science and High Technology and Environmental Science, Graduate University of Advanced Technology, Kerman, Iran.

Abstract

Poor management of agricultural waste is one of the sources of water pollution. In this study, the surface water quality of Jiroft Dam watershed was modeled using the QSWAT model. The study area is located at the upstream of Jiroft Dam watershed with the total area of 783446.81 hectares in Kerman province. The rainfall-runoff model was simulated over the 21 years from 2000 to 2020 for a monthly time step. Data from Konaroye hydrometric station was used as observed flow data. The meteorological data was collected from Baft synoptic station. The model was calibrated and validated using the SUFI-2 automated algorithm in SWAT-CUP software for periods (2011-2019) and (2008-2010), respectively. The final results of determination and Nash-Sutcliffe coefficients from calibration and validation processes obtained 0.79, 0.77, 0.81 and 0.82, respectively. Sensitivity analysis was performed for 12 calibration parameters. The results show that base-flow alpha factor is the most sensitive parameter. After modeling of the flow rates in watershed, in next step surface water quality was modeled in QSWAT by considering Urea as fertilizer which is mostly used on the area under cultivation of Jiroft Dam watershed. The results for Nitrate load show that model prediction is in good agreement with the experimental data. The results of this study show that QSWAT model can be used as an effective and efficient method in order to predict surface water quality and managing of water resources.

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Water and Irrigation Management
Volume 12, Issue 1
April 2022
Pages 157-170

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