Because of importance of sediment delivery of watersheds, sediment rating curve is used for estimation of suspended sediment concentration. But most often sediment rating curve can not represent sediment concentration well in high flows, because of bias in log-transformation and extrapolation of data. For optimization of sediment rating curve, this research carried out with data of Q-SSC in outlet of two sub watershed of the Kamal Saleh dam watershed, Markazi province, Iran. Sediment data analysis was carried out using sediment rating curve of single-line and mean of classes with different correction factors such as FAO, QMLE, Smearing, MVUE and β. The results of various evaluation indices showed that the optimum sediment rating curve in the Hasan-Abad station follows single-line sediment rating curve and in the Ghaleh-No station mean of classes sediment rating curve, both with average of MVUE correction coefficients. According to the results, β correction factor for mean of classes’ sediment rating curve could reduce the difference between observation and estimation for high flows, but because of high scatter of observed data and undesirable extrapolation the Smearing-linear and QMLE had the lowest efficiency. However, for high flows in torrential events, the mean of classes’ sediment rating curve with FAO correction factor gave more accurate results than other methods.

Estimating flood events sediment yield and its temporal variation are of the main and basic issues in watershed management strategies. On the other and there are little investigations about sediment yield behavior of the flood events and applicability of the sediment rating curves to estimate sediment load of flood events. In this study in order to investigate bias and errors of the sediment rating curves in estimating sediment load of the flood events, the estimated values of 10 types of rating curves compared by the observed values of some hourly monitored flood events in the Gharachay River of Markazi provinces. By considering accuracy and precision indexes the results shows that in all of treated sediment rating curves underestimated (40-80%) flood hydrograph sediment yield in this case the FAO’s method has relatively closer estimates to the observed data and despite of suitable applicability of the MVUE method in estimating annual sediment yield it could not prove to be applicable in the flood events cases.

Complex and variable nature of the river sediment yield caused many problems in prediction of the long-term sediment yield and input into the reservoirs .sediment rating curves generally used to estimate the suspended sediment load of the river and drainage basin. Since the regression equations of the sediment rating curves obtained by logarithmic retransformation and little independent variable include in this equation they also overestimate or underestimate the true load of the rivers. In this research in order to improve and develop sediment yield estimation methods the estimates of 10 kind of sediment rating curves were compared with actual loads of 5 stations in different climate zones of the country by using accuracy and precision index .the results show that in estimating long term mean load of the selected periods the Linear-MVUE sediment rating curve has relatively high accuracy and precision comparing other kinds of sediment rating curves. In average, the accuracy index of this method is about 1.23 that is reasonable among other methods. Since this kind of sediment, rating curve uses different data characteristics and information it could be recommend for application in sediment yield estimation within acceptable confidence limits.

Accurate estimation of the sediment amount carried by a watershed drainage system in the design of all projects in water and soil conservation is essential. Due to convert logarithmic sediment rating curves, sediment regression equations is necessary to correct too biased. The present study aims to evaluate the performance of five methods of rating curve including FAO, MVUE, QMLE, LRC, Ls and b in the case of no category situations. Root mean square of error, coefficient of efficiency indicators, General Standard Deviation, Precision, Difference relation and Relative error were used to evaluate the performance of sediment rating curves and to choose the best of them. The result showed LQMLE method with RMSE=3.663, CE=92.1, GSD=0.556, r=1.01, RE=33% and P=1.01 is the best method of calculating sediment and FAO and LS models (with root mean square error of 6.24 and efficiency coefficient 76.3), (the root mean square error of 4.87 and 85.7 efficiency coefficient) are the weakest model in estimating the suspended sediment basin. n general, different methods and different correction factors in non-categorized of sediment rating curves, can be concluded that in most models and methods LQMLE and MVUE are the best methods of estimating the suspended sediment load.

Managers always consider the precise estimation of sediments in watersheds due to various conditions, such as soil and water resources management, construction, infrastructure and economical and social issues. In this condition, an optimized determination of sediment rating equation (typical method until now for sediment yield estimation) is essential to investigate sediment yield in rivers. In this study, the best sediment rating equation was determined for four hydrometric stations of Pol-Doab watershed in Markazi province using sediment rating curves types (singlelinear, multi-linear, mean loads) together with bias correction factors (FAO, Quasi-Maximum Likelihood Estimator [QMLE], Smearing, Minimum Variance Unbiased Estimator [MVUE] and β). The results showed that the optimized equation in stations is the mean loads (MVUE), which can used for prediction of sediment yield in annual scale.Moreover, FAO factor is more accurate for the estimation of sediment yield in high variability conditions for monthly, weekly and daily scales. According to the obtained results, accurate representation of variability intensity of sediment yield is associated with the rating curves types, since the monthly rating curve is more accurate. Also, the results indicated that the watershed average slope has direct relation withb coefficient of rating equation, and when using this parameter, the rate of sediment yield can be determined for month, season and hydrological periods. Based on the obtained results, with increase in the watershed average slope, the slope of suspended sediment concentration (SSC) equation is also increased.

The aim of this study was to estimate suspended sediment by the ANN model, DT with CART algorithm and different types of SRC, in ten stations from the Lorestan Province of Iran. The results showed that the accuracy of ANN with Leven berg-Marquardt back propagation algorithm is more than the two other models, especially in high discharges. Comparison of different intervals in models showed that running models with monthly data, resulted in smaller error and better estimated results. Moreover, results showed that using Minimum Variance Unbiased Estimator (MVUE) bias correction factor modified the SRC results, especially in monthly time steps in almost all stations. Hence, it can be said that if because of advantages such as simplicity, SRC models are preferred, it is better that MSRC (modified sediment rating curve) is used in monthly period.

Sediment transport and settling leads to some outcomes such as sedimentary island establishment in river stream and consequently, decrease in transfer capacity of flood water flows, decrease in useful life of dams and depots saving capacity, corrosion in river factor foundations, damaging the water buildings and farms, settling in the canal bed and many other problems. Also it is necessary correct estimation of the rivers sediment load in all hydraulics projects is necessary. One of the methods of sediment estimating is hydrological methods. Hydrological methods are divided to several categories, such as USBR, FAO, Median of groups, MVUE, QMLE, and Smearing. In this research estimating of sediment in Arazkoose hydrometric station which is located on Gorganrood watershed was carried out by these methods. Therefore, sediment and flow discharge data during years of 1973 to 2008 accumulated and analyzed. After determination of sediment rating curves in various methods, the best method was selected by comparison of the statistic parameters such as R, RMSE, r and GSD. After that, the daily sediment discharges in all statistic period were determined. At last estimating’s quintiles during 45 days period with the statistic indicator, relative mean of error. The results of this study showed that median of groups with R=0.79, RMSE=52148, r=0.68 and GSD=0.89 is the proper method for suspended load estimating in the Arazkoose station and based on this method the average of daily sediment discharge was estimated at this station as 11324 ton/day.