Modeling input errors to improve uncertainty estimates for one-dimensional sediment transport models

Jung, Jeffrey Y.; Niemann, Jeffrey D.; Greimann, Blair P.

doi:10.1007/s00477-017-1495-8

Cited by 4 publications

(1 citation statement)

References 41 publications

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“…How to effectively predict the sedimentation process has been of critical significance to hydraulic engineers. As such, a non‐conventional approach to estimate uncertainty of sediment loading and to quantify the risk of reservoir sedimentation under non‐stationary flow conditions may serve as a viable alternative for water resources planning and management (Phatarfod, Unny, & Ponnambalam, 1990; Xu, Ito, Liao, & Wang, 1997; Jung, Niemann, & Greimann, 2018; Tsai, Hung, & Oh, 2018).…”

Section: Introductionmentioning

confidence: 99%

Risk analysis of reservoir sedimentation under non‐stationary flows

Tsai

Yeh

Hsu

et al. 2021

J Flood Risk Management

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Reservoir sustainability is strongly impacted by the reservoir sedimentation processes. Most of the substantial sedimentation processes occur in non‐stationary flows such as in the case of flash floods, surges and tidal waves. However, a stationary probability assumption is normally adopted to reduce mathematical model complexity. This work develops a non‐stationary Gambler's ruin model using the Monte‐Carlo simulation method. Daily water‐level data for the Xia‐Yun station are used to predict the effective risk that the maximum capacity of the water treatment plant in the Shihmen Reservoir is reached. This non‐stationary model yields fairly accurate probabilities of sedimentation by the transitional probability of a reservoir reaching different levels of turbidity, and the average time to reach a designated reservoir maximum handling turbidity. The extended capacity of the proposed model demonstrates the major particle processes during non‐stationary flows. Such analytical results offer water resources agency to scientifically evaluate the dredging/remediation strategies with the existing reservoirs. Transport capacities of rivers and streams, and the potential consequences of flood risks in response to reservoir sedimentation can then be comprehensively estimated in order to allow effective contingency planning for public safety.

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Section: Introductionmentioning

confidence: 99%

Risk analysis of reservoir sedimentation under non‐stationary flows

Tsai

Yeh

Hsu

et al. 2021

J Flood Risk Management

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Uncertainty analysis in the detection of trends, cycles, and shifts in water resources time series

Coelho

Fernandes

Detzel

2019

Water Resour Manage

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Scientists and water resources managers are increasingly worried about issues related to uncertainty. Although uncertainty analysis has been required in the decision-making processes, there is still a lack of knowledge on how to do it. The application of statistical methods for water resources research requires time series to be compliant with the hypotheses of randomness, homogeneity, independence, and stationarity (RHIS). Noncompliance may occur when trends, cycles, and/or shifts are present. However, the uncertainties and associated subjectivity in assessment and expression may make it difficult to detect these patterns of variability. In this research, synthetic time series (STS) were generated from the uncertainties in flow rates and a water quality parameter, and tested for RHIS by the Single-Sample Runs, Mann-Whitney, Wald and Wolfowitz, and Mann-Kendall tests, respectively. The tests were applied with an increasing number of elements (N). Three uncertainty scenarios were defined, the low level (LL: 10-30%), mid level (ML: 30-50%) and high level (HL: 50-70%). The Monte Carlo Method (MCM) was applied with uniform, normal and lognormal probability distributions. In each scenario, averages and standard deviations (std) were calculated as a measure of the p-values uncertainty. Although the complete series were compliant with the hypotheses, the p-values and std ranges varied from one extreme to the other (0.0-1.0) as N increased. The std ranges had an impact on the decisions when the pvalue was close to the rejection limit. The randomness p-value for the flows in the uniform case was 0.04, while the STS p-values were ≈ 0.05 ± 0.04, 0.09 ± 0.10, and 0.18 ± 0.20 in the LL, ML and HL scenario respectively. However, the decision on significance should not be based only on p-values, since the actual existence and continuity of patterns of variability depends more on representativity-related issues. The results lead to the conclusion that efforts to reduce uncertainties should be directed towards the development of appropriate monitoring and data analysis/interpretation strategies.

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Partition of one-dimensional river flood routing uncertainty due to boundary conditions and riverbed roughness

Wang

Zhao

et al. 2022

Journal of Hydrology

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Modeling input errors to improve uncertainty estimates for one-dimensional sediment transport models

Cited by 4 publications

References 41 publications

Risk analysis of reservoir sedimentation under non‐stationary flows

Risk analysis of reservoir sedimentation under non‐stationary flows

Uncertainty analysis in the detection of trends, cycles, and shifts in water resources time series

Partition of one-dimensional river flood routing uncertainty due to boundary conditions and riverbed roughness

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