Classifications of runoff and sediment data to improve the rating curve method

Abstract

In this study, data classification method was evaluated to increase accuracy of estimating suspended sediment load. To achieve this objective, suspended sediment in Chehelgazi and Khalifeh-Tarkhan rivers in Kurdistan, Iran, were estimated using Sediment Rating Curve (SRC) method in three different approaches of data classification. At first, measured data were modeled without classification. Then, data based on flow statues were divided into two series as high and low flow. Eventually, based on sediment concentration, the data were divided into low and high sediment concentration. Long-term runoff and sediment data were used to calibrate rating curve model. The estimated values were compared with recorded data and the performances of these models were evaluated using statistical criteria. The results indicated an effective role of data classification to improve estimating sediment transportation by rating curve method. In one of the stations, it was observed that due to classification based on river flow and sediment concentration, model efficiency was increased about 45% and 28%, respectively. Furthermore, in case of improving efficiency of SRC method, classifying data based on flow statues was found to be more effective than sediment concentration. The results of this study can be used to improve the management of the basin by more accurately estimating the amount of suspended sediments transporting in the rivers draining to reservoirs.