Sediment Curve Uncertainty Estimation Using GLUE and Bootstrap Methods

Document Type : Research Article

Authors

• aboalhasan fathabadi
• hamed rouhani

Gonbad Kavous University

https://doi.org/10.22067/jsw.v30i2.39843

Abstract

Introduction: In order to implement watershed practices to decrease soil erosion effects it needs to estimate output sediment of watershed. Sediment rating curve is used as the most conventional tool to estimate sediment. Regarding to sampling errors and short data, there are some uncertainties in estimating sediment using sediment curve. In this research, bootstrap and the Generalized Likelihood Uncertainty Estimation (GLUE) resampling techniques were used to calculate suspended sediment loads by using sediment rating curves.
Materials and Methods: The total drainage area of the Sefidrood watershed is about 560000 km2. In this study uncertainty in suspended sediment rating curves was estimated in four stations including Motorkhane, Miyane Tonel Shomare 7, Stor and Glinak constructed on Ayghdamosh, Ghrangho, GHezelOzan and Shahrod rivers, respectively. Data were randomly divided into a training data set (80 percent) and a test set (20 percent) by Latin hypercube random sampling.Different suspended sediment rating curves equations were fitted to log-transformed values of sediment concentration and discharge and the best fit models were selected based on the lowest root mean square error (RMSE) and the highest correlation of coefficient (R2). In the GLUE methodology, different parameter sets were sampled randomly from priori probability distribution. For each station using sampled parameter sets and selected suspended sediment rating curves equation suspended sediment concentration values were estimated several times (100000 to 400000 times). With respect to likelihood function and certain subjective threshold, parameter sets were divided into behavioral and non-behavioral parameter sets. Finally using behavioral parameter sets the 95% confidence intervals for suspended sediment concentration due to parameter uncertainty were estimated. In bootstrap methodology observed suspended sediment and discharge vectors were resampled with replacement B (set to 3000) times. Sediment rating curves equation was fitted to each sampled suspended sediment and discharge data sets. Using these sediment rating curve and their residual suspended sediment concentration were calculate for test data. Finally using the 2.5 and 97.5 percentile of the B bootstrap realizations, 95% bootstrap prediction intervals were predicted.
Results and Discussion: Results showed that Motorkhane and MiyaneTonelShomare 7 stations were best fitted by a sigmoid function and Stor and Glinak stations were best fitted by second order polynomial and liner function, respectively The first 50 of the B bootstrapped curves were plotted for all stations.with respect to these plots implied that bootstrapped curves more scattered whereas observed data were less. The suspended sediment curve parameters estimated more accurately where, the suspended sediments were sampled more, as a result of reduced uncertainty in estimated suspended sediment concentration due to parameter uncertainty. In addition to sampling density bootstrapped curves, uncertainty depends on the curve shape. For GLUE methodology to assess the impact of threshold values on the uncertainty results, threshold values systematically changed from 0.1 to 0.45. Study results showed that 95% confidence intervals are sensitive to the selected threshold values and higher threshold values will result in an increasing 95% confidence interval. However, the highest 95% confidence intervals obtained by GLUE method (when threshold value was set to 0.1) was little than those values obtained by Bootstrap.
Conclusions: The uncertainty of sediment rating curves was addressed in this study by considering two different procedures based on the GLUE and bootstrap methods for four stations in Sefidrod watershed.Results showed that nonlinear equation fitted log-transformed values of sediment concentration and discharge better than linear equation. Uncertainty result using GLUE depend on chosen threshold values. As threshold values increased, 95% confidence intervals decreased. Uncertainty results showed that 95% confidence intervals estimated by bootstrap were higher than the biggest 95% confidence intervals (when threshold value set to 0.1) estimated by GLUE method. Overall, in all stations, 95% confidence intervals arising from suspended sediment curve shapes (e.g, linear, second order polynomial and sigmoid function), data sampling density and uncertainty estimation methods (here were GLUE and Bootstrap).

Keywords

• Polynomial Function
• River discharge
• sensitivity analysis
• Sigmoid Function
• Suspended Load

20.1001.1.20084757.1395.30.2.22.4

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