Modeling probabilistic-based 1D riverbed elevation estimation model due to uncertainties in runoff and sediment-related factors

Abstract

This study aims to develop a probabilistic model to quantify the reliability of estimating riverbed elevations due to the uncertainties in the runoff and sediment-related factors (named PM_MBEE_1D); the above uncertainties are quantified by reproducing a considerable number of runoff-related and sediment-related factors via the multivariate Monte Carlo simulation approach. Using a sizeable number of simulated uncertainty factors, the proposed PM_MBEE_1D model is developed by coupling the rainfall–runoff model (SAC-SMA) and 1D sediment transport simulation model (CCHE1D) with the uncertainty/risk analysis advanced first-order second-moment (AFOSM) method as well as the logistic regression analysis. Validated by the historical data in the Jhuosdhuei River watershed, the proposed PM_MBEE_1D model could efficiently and successfully capture the spatial and temporal changes in the estimated riverbed elevations (i.e., scouring and siltation) due to the uncertainties in the river runoff and sediment with a high accuracy (nearly 0.983). Also, using the proposed PM_MBEE_1D model with given runoff and sediment factors under a desired reliability, the probabilistic-based riverbed elevations could accordingly be estimated as a reference to watershed treatment and management plan. HIGHLIGHTS The proposed PM_MBEE_1D could quantify the reliability of the estimated riverbed elevations at various cross-sections due to the uncertainties in the river runoff and sediment discharges.; The proposed PM_MBEE_1D could provide the riverbed elevation estimates under a desired likelihood.; The resulting big data of the simulations of the rainfall-induced movable-bed elevations could be applied in AI model training.;

Keywords

• cche1d
• logistic regression analysis
• multivariate monte carlo simulation method
• uncertainty analysis