Extended abstract
1- Introduction
The phenomena of erosion, sediment transport, and sedimentations have tremendously destructive effects on the environment and hydraulics structures. In general, the sediment transportation depends on river discharges, but the proposed equations inherited serious errors. The estimation of suspended sediment load (SSL) is one of the most important factors in river hydraulics, morphology and sediment hydraulic studies. An accurate estimation of suspended sediment loads (SSL) is crucial for the management and construction of the water resources projects. This factor also is the fundamental basis for the proper management planning of the soil and water resource in the watershed. The estimation of the total suspended load of the river with measured data in the hydrometric stations using the relation sediment rating curves are possible through the conventional methods. Accurate and reliable suspended sediment estimates are required in a variety of experimental and operational hydrological situations for scientific and/or river management purposes. Sediment ratings may, for example, be used to estimate the long-term rates of landscape denudation, to reflect the river morphological changes, to gauge the sensitivity of catchments for varying land-use practices, or to accomplish specific project applications, such as the estimation of the reservoir lifetimes, or the identification of the tolerable effluent discharge, and/or the water quality inputs, around the hydroelectric turbines. Suspended sediment loads (SSL) are often estimated through an empirical relation between suspended sediment load (L) and streamflow (S). This relation is usually defined as a power function, L = aSb, and is referred to as a suspended sediment rating curve. This function can be formulated as either a linear or non-linear model to find the solution of the rating curve parameters (a and b). Formulation of the power function as a linear model requires a logarithmic transformation to linearize the function and a subsequent correction for the transformation bias. Rating-curve parameter estimates for both the bias-corrected, transformed-linear or non-linear models can be obtained through the method of least squares.
2- Methodology
In this paper, the rate of the sediment load in Zaremrood river was estimated using USBR, Polyline USBR, moderate categories curve, seasonal rating curve and FAO models. In addition, during 30 days (60 samples), the suspended load sampling was conducted, then, the selected model was evaluated using a direct sampling of the suspended load. To evaluate the suspended sediment loads and an optimized model on them, in this research, the data were collected from the Iranian water resources management company and Mazandaran regional water authority. The data analysis first included the control and adequacy testing, detection of outliers and normality test. Also, to ensure the homogeneity of the data, the homogeneity test was performed using Kolmogorov-Smirnov test in the SPSS software. Finally, the five models were derived using the corresponding data of flow (Qw) and sediment discharge (Qs).
3- Results
The results of this study showed that the moderate categories curve model had the lowest RME rate (173/65) and the highest coefficients of determination (96%) (table 1), and as a result, this model was selected as the best model to estimate the suspended load of Zaremrood river (Figure 1).
Table 1. RME values for the models
Estimation Models | Single Linear Rating Curve Model | Multiline Rating Curve Model | Moderate Categories Curve Model | Seasonal Rating Curve Model | FAO Model |
RME | 1984.42 | 745.50 | 173.65 | 227.12 | 426.23 |
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