Human intervention in rivers

Berends, Koen Daniël

doi:10.3990/1.9789036548823

Cited by 2 publications

(2 citation statements)

References 182 publications

(351 reference statements)

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“…The results of parameter identification and uncertainty estimation are usually presented in the form of confidence intervals for model outputs and parameter marginal distributions. The problem was defined on the basis of Bayes estimation using the generalized likelihood uncertainty estimation (GLUE) approach (Beven and Binley, 1992;Romanowicz and Beven, 2006). Parameter distributions are obtained using the Bayes formula:…”

Section: Parameter Identification and Uncertainty Analysismentioning

confidence: 99%

Predicting discharge capacity of vegetated compound channels: uncertainty and identifiability of one-dimensional process-based models

Kiczko

Västilä

Kozioł

et al. 2020

Hydrol. Earth Syst. Sci.

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Abstract. Despite the development of advanced process-based methods for estimating the discharge capacity of vegetated river channels, most of the practical one-dimensional modeling is based on a relatively simple divided channel method (DCM) with the Manning flow resistance formula. This study is motivated by the need to improve the reliability of modeling in practical applications while acknowledging the limitations on the availability of data on vegetation properties and related parameters required by the process-based methods. We investigate whether the advanced methods can be applied to modeling of vegetated compound channels by identifying the missing characteristics as parameters through the formulation of an inverse problem. Six models of channel discharge capacity are compared in respect of their uncertainty using a probabilistic approach. The model with the lowest estimated uncertainty in explaining differences between computed and observed values is considered the most favorable. Calculations were performed for flume and field settings varying in floodplain vegetation submergence, density, and flexibility, and in hydraulic conditions. The output uncertainty, estimated on the basis of a Bayes approach, was analyzed for a varying number of observation points, demonstrating the significance of the parameter equifinality. The results showed that very reliable predictions with low uncertainties can be obtained for process-based methods with a large number of parameters. The equifinality affects the parameter identification but not the uncertainty of a model. The best performance for sparse, emergent, rigid vegetation was obtained with the Mertens method and for dense, flexible vegetation with a simplified two-layer method, while a generalized two-layer model with a description of the plant flexibility was the most universally applicable to different vegetative conditions. In many cases, the Manning-based DCM performed satisfactorily but could not be reliably extrapolated to higher flows.

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Section: Parameter Identification and Uncertainty Analysismentioning

confidence: 99%

Predicting discharge capacity of vegetated compound channels: uncertainty and identifiability of one-dimensional process-based models

Kiczko

Västilä

Kozioł

et al. 2020

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…For all three branches, water levels have visibly lowered for equal discharges due to riverbed subsidence, up to roughly 0.6 m within the entire study period. Following Berends et al (2019), who found that in the data there is no detectable effect of recent river interventions on water levels, we ignored river interventions. Also, due to time limitations we ignored hysteresis effects.…”

Section: Uncertainty Of Rating Curvesmentioning

confidence: 99%