Presentation of proper suspended sediment load estimation model using integrated regression functions and temporal classification of water discharge in Balikhli chay watershed- Ardebil province- Iran

This study was done for evaluating and providing an suitable model to estimate sediment load in the Almas Bridge water gage established on Balikhly river regarding harmful economic and environmental effects caused by river sediment load. In this context, we evaluate and compare synthetic methods into regression analyses based on classification of discharge and corresponding sediment load data over 31 years in order to increase accuracy and efficiency of sediment rating curve. Various models were tested based on significance level of 0/05 and standard error of estimate (SEE) in SPSS software to select the suitable sediment load estimation model. Results indicated good performance of the exponential model among regression models and the monthly model among temporal models. Thus, the monthly power regression model with the lowest SEE (0/81) was selected as the proper model for estimating suspended sediment load. In contrary, the temporal model used for fitting regression functions to discharge and sediment load data based on all data without classification, was recognized as the most non-efficient model. Finally, it is concluded that the monthly model can be useful to know of sediment yield regime and complexity of sediment load transport in the watershed.