%0 Journal Article
%T Ability of Loadest Regression Methods to Estimate Annual Suspended Sediment
%J Journal of Range and Watershed Managment
%I دانشکده منابع طبیعی دانشگاه تهران
%Z 5044-2008
%A Nazari Samani, Aliakbar
%A Salvati, Aryan
%D 2021
%\ 11/22/2021
%V 74
%N 3
%P 597-609
%! Ability of Loadest Regression Methods to Estimate Annual Suspended Sediment
%K Taylor Diagram
%K Gorganrood
%K correlation coefficient
%K Suspended Sediment
%R 10.22059/jrwm.2021.301593.1492
%X Having knowledge on the quantitative amount of watershed sediment yield is one of the most basic information to deal with soil erosion and conservation as well as design of dams. In Iran, the estimation of suspended sediment load is often based on measurement curve methods. Since sediment discharge data are random and discontinuous, in practice, their internalization and extrapolation is associated with many errors. This review is to evaluate the number of data available to estimate daily sediment load with Loadest regression models. Therefore, daily discharge data of Ghazaghli station in Gorganrood forest watershed were used. So that different percentages of available data were accidentally deleted and the amount of sediment load was estimated by 11 methods. According to the evaluation results (Taylor diagram), model number 2 has the best accuracy and in the absence of up to 50% of the daily sediment data, the correlation coefficient of more than 0/5 in the annual sediment estimation and only for the first year And in the rest of the years under study the correlation coefficient is unacceptable. Therefore, the use of sediment measurement curve methods with the data available at the level of Iranian stations, if the number of data available to construct the measurement curve is less than 185 will be associated with very little accuracy. Also, the higher the amount of available data belonging to the periods of low sediment transport (autumn and dry years), the lower the efficiency of the Loadest method will be.
%U https://jrwm.ut.ac.ir/article_85286_f177a6ce669af5ceacd2147df6a8ff22.pdf