An integrated framework for simultaneously modeling primary and secondary salinity at a watershed scale

•Secondary salinity resulting from human activities was added to the SWAT-S model.•The modified SWAT-S was linked with the SWAP to improve the salinity simulation.•Critical salinity processes were assessed for management in a large watershed. Salinity is a major environmental phenomenon that affects...

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Veröffentlicht in:Journal of hydrology (Amsterdam) 2022-09, Vol.612, p.128171, Article 128171
Hauptverfasser: Maleki Tirabadi, Mohammad Sadegh, Banihabib, Mohammad Ebrahim, Randhir, Timothy O.
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Sprache:eng
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Zusammenfassung:•Secondary salinity resulting from human activities was added to the SWAT-S model.•The modified SWAT-S was linked with the SWAP to improve the salinity simulation.•Critical salinity processes were assessed for management in a large watershed. Salinity is a major environmental phenomenon that affects watersheds and irrigated areas worldwide. The watershed-scale salinity modeling requires a watershed-scale model like the Soil and Water Assessment Tool (SWAT) that simulates hydrologic processes throughout a watershed. The Richards equation can assist in addressing very complex processes that affect water flow in the unsaturated root zone influenced by gravity and capillarity at a field scale. The SWAT model does not use the Richards equation for modeling flows in the root zone, resulting in less accuracy in simulating hydrologic processes related to agriculture. This study presents an integrated framework for the watershed-scale salinity modeling by modifying the water-salt balance (SWAT-S) model linked to the Soil Water Atmosphere Plant (SWAP) model. The integrated framework was applied to the Mond River Basin of Iran. The model was calibrated and validated with historical records of the river discharge and salt concentration using the SUFI-2 optimization algorithm to evaluate the model's strength and to verify the accuracy of simulations. The model performance was satisfactory in calibration and validation periods with the coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), Percent bias (|PBIAS|), and RMSE-observations standard deviation ratio (RSR) ranging from 0.56 to 0.87, 0.52 to 0.87, 0.2 to 22.2, and 0.37 to 0.69, respectively. Also, P‐factor values were more than 0.65, while R‐factor values were
ISSN:0022-1694
DOI:10.1016/j.jhydrol.2022.128171