Showing 2 results for A. Najafinejad
M. Mardian, A. Najafinejad, J. Varvani, V. B. Sheikh1,
Volume 16, Issue 59 (spring 2012)
Abstract
Investigation in to the sediment delivery of watersheds and its variation is an important element of ecosystem management. Since sediment load depends on runoff quantity, and runoff is considered as a unique indicator of sediment load, in this research the two modified versions of the MUSLE model were evaluated for 9 torrential events in two subwatersheds of the Kamal Saleh watershed in the Markazi Province of Iran. To this end, first all factors of the model including runoff, erodibility, topographic, cover management, and support practice were estimated using routine equations of the model. Then, the power coefficient in the runoff factor was corrected, applying two methods: “m correction coefficient” and “average correction coefficient. The evaluation criteria showed that the “m correction coefficient method” (compared to the “average correction coefficient method”) reduces the difference of the observed and estimated sediment load of small and large torrential events remarkably. In fact, the application of this modified method increased the accuracy of the MUSLE by decreasing the standard deviation of prediction. Also, the validation analysis of the modified method showed that the coefficient of efficiency indexes for the Hasan-Abad station (Rudkhane Bozorg subwatershed) and Ghal'e-No station (Ashur-abad subwateshed) were 0.997 and 0.811, respectively. This result confirms the efficiency of application of “m correction coefficient method”. However, it is suggested that the performance of this method be evaluated using a sufficient number of individual hydrographs and their sedimentgraphs in other watersheds.
H. Akbari Mejdar, A. Bahremand, A. Najafinejad, V. Sheikh,
Volume 18, Issue 67 (Spring 2014)
Abstract
Over-parameterization is a well-known and often described problem in hydrological models, especially in distributed models. Therefore, using special methods to reduce the number of parameters via sensitivity analysis is important to achieve efficiency. This paper describes a sensitivity analysis strategy that graphically assigns for each parameter a relative sensitivity index and relationship of the parameter and the outputs of the model. The method is illustrated with an application of SWAT model in the Chehelchai catchment, Golestan province. In this study, total water yield, along with four major parts of water budget including surface runoff, lateral flow, groundwater and evapotranspiration was selected as objective function. SWAT is a river basin model that can be used to predict the impact of land management practices on water, sediment and agricultural chemical yield in watersheds. A relative sensitivity index was used for ranking the sensitivity of parameters. The results showed that soil evaporation compensation facto (ESCO), CN, soil available water capacity (SOL-AWC), deep aquifer percolation fraction (RCHRG-DP) and soil bulk density (SOL-BD) have the most influence on river flow. These parameters are generally stated as the most sensitive parameters of SWAT model in most of the same researches worldwide
