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چکیده
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In the current study, Sediment load and related hazards were studied in the Shaharchay River basin, a sub-basin of Lake Urmia, northwestern Iran. The various statistical and modeling methodologies were used to analyze discharge and sediment load data from 2002 to 2017. In this regard, a power-law sediment rating curve was developed, and extreme value analyses were conducted with Annual Maximum Series and Peaks Over Threshold methods. These data were fitted using the Generalized Extreme Value and the Generalized Pareto distributions. Trends and seasonality in the time series of sediment load were investigated, and higher loads were observed in spring months. The dependence between event duration and average sediment load showed a positive dependence. The specific sediment yield was calculated and ranged between 12.9 and 386.0 tons/km²/year. The frequency-magnitude analysis was performed, and the sediment load was estimated for various return periods. Uncertainty and sensitivity analyses were performed, which showed that the sediment rating curve coefficients contributed to ~60% of the total variance in load estimates. Climate change impacts were investigated as well, which showed a gain of 15-25% of sediment yield at the end of the 21st century under the RCP 4.5 scenario. Copula-based analysis was used to study joint behavior of discharge and sediment load. Artificial Neural Network models were then developed to predict the sediment load, and these model outputs gave an R² value of 0.89, hence outperforming the traditional rating curves. A risk matrix was developed that took into consideration the combined impacts of sediment load magnitude and frequency. The results of this holistic study can be used for sustainable water management strategies and assessment of sediment-related risks within the Shaharchay River basin.
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