Uncertainty analysis of hydrological return period estimation, taking
the upper Yangtze River as an example

Sun, Hua; Jiang, Tong; Cheng, Jing; Su, Buda; Wang, Guojie

doi:10.5194/hess-2016-566

Cited by 6 publications

(7 citation statements)

References 49 publications

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“…These are mainly spatial interpolation and map algebra methods. The natural neighbor method (e.g., Sibson 1981; Van der Graaf 2016) is applied for modeling of water surface spatial distribution. The conditional method for raters and reclassification is applied to properly process the interpolation results.…”

Section: Tools Of Spatial Analysismentioning

confidence: 99%

Analysis of extreme flow uncertainty impact on size of flood hazard zones for the Wronki gauge station in the Warta river

et al. 2019

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In this paper, the impact of maximum flow uncertainty on flood hazard zone is analyzed. Two factors are taken into account: (1) the method for determination of maximum flows and (2) the limited length of the data series available for calculations. The importance of this problem is a consequence of the implementation of the EU Flood Directive in all EU member states. The factors mentioned seem to be among the most important elements responsible for potential uncertainty and inaccuracy of the developed flood hazard maps. Two methods are analyzed, namely the quantiles method and the maximum likelihood method. The maximum flows are estimated for the Wronki gauge station located in the reach of the Warta river. This simple river system is located in the central part of Poland. The length of the available data is 44 years. Hence, the series of the lengths 40, 30 and 20 years are tested and compared with reference calculations for 44 years. The hydrodynamic model HEC-RAS is used to calculate water surface profiles in steady state flow. The Python scripting language is applied for automation of HEC-RAS calculations and processing of final results in the form of inundation maps. The number of trials for each factor is not huge to keep the presented methodology useful in practice. The chosen measure of uncertainty is the range of variability for maximum flow values as well as inundation areas. The estimated values stressed the great importance of the factors analyzed for the uncertainty of the maximum flows as well as inundation areas. The impact of the data series length on the maximum flows is straightforward; a shorter data series gives a wider range of variability. However, the dependencies between other factors are more complex. Hence, the application of methodology based on the simulation and GIS data processing for assessment of this problem seems to be quite a good approach.

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Section: Tools Of Spatial Analysismentioning

confidence: 99%

Analysis of extreme flow uncertainty impact on size of flood hazard zones for the Wronki gauge station in the Warta river

et al. 2019

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“…The hydrological community has identified the propagation of uncertainty as one of the 23 unsolved problems in hydrology (Blöschl et al, 2019). Numerous actors in water resource and hydrologic research have attempted to identify different sources of uncertainty in water resources (Kusangaya et al, 2018;Prein, 2019;Vetter et al, 2017;Xu et al, 2010) and extreme hydrological frequencies (Her et al, 2019;Qi et al, 2016;Sun et al, 2017;Vrugt et al, 2008;Zhang et al, 2016). These research works have addressed uncertainty in water resources and extreme hydrological modeling in the isolated form.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, Sun et al (2017) identified multiple sources of uncertainty (distribution types, distribution parameters, and peak over threshold) in flood frequency. They found that distribution types could play a significant role in controlling the flood magnitude.…”

Section: Introductionmentioning

confidence: 99%

“…They found that the input data role is more significant than the role of the hydrological parameter in peak flow estimation. Others have investigated the uncertainty caused by input and hydrological parameters (e.g., Bae et al, 2018;Zhang et al, 2016), hydrological parameters and model structure (e.g., Butts et al, 2004;Jin et al, 2010), climate change, and hydrological parameters (e.g., Her et al, 2019;Meresa, 2019), climate change and flood frequency (e.g., Qi et al, 2016), frequency distribution parameters and types (e.g., Sun et al, 2017). These studies have shown that only one aspect of systematic and/or inherent uncertainty is insufficient to address the problem in estimating extreme flow.…”

Section: Introductionmentioning

confidence: 99%

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The role of input and hydrological parameters uncertainties in extreme hydrological simulations

Meresa

Tischbein

Mendela

et al. 2021

Natural Resource Modeling

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Quantifying possible sources of uncertainty in simulations of hydrological extreme events is very important for better risk management in extreme situations and water resource planning. The main objective of this research work is to identify and address the role of input data quality and hydrological parameter sets, and uncertainty propagation in hydrological extremes estimation. This includes identifying and estimating their contribution to flood and low flow magnitude using two objective functions (NSE for flood and LogNSE for low flow), 20,000 Hydrologiska Byråns Vattenbalansavdelning (HBV) hydrological parameter sets, and three frequency distribution models (Log-Normal, Pearson-III, and Generalized Extreme Value). The influence of uncertainty on the simulated flow is not uniform across all the selected three catchments due to different flow regimes and runoff generation mechanisms. The result shows that the uncertainty in high flow frequency modeling mainly comes from the input This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…There, rather simple analytical and bootstrap methods allow the exploration of the uncertainty of extreme quantiles. In a univariate framework, the effect of the choice of the marginal distribution (Merz & Thieken, 2005;Qi et al, 2016), parameter uncertainty of the marginal distribution (Qi et al, 2016), data uncertainty from threshold selection (Qi et al, 2016;Xu et al, 2010), and the effect of the choice of annual maxima sampling versus peakover-threshold sampling (e.g., Madsen et al, 1997;Martins & Stedinger, 2001a, 2001bSun et al, 2017) have been considered. In a bivariate framework that allows for the joint consideration of peak discharges and hydrograph volumes, the effect of the choice of annual maxima sampling versus peak-over-threshold sampling has, to our knowledge, not yet been analyzed.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Assessment of Synthetic Design Hydrographs for Gauged and Ungauged Catchments

Brunner

Sikorska

Furrer

et al. 2018

Water Resources Research

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Design hydrographs described by peak discharge, hydrograph volume, and hydrograph shape are essential for engineering tasks involving storage. Such design hydrographs are inherently uncertain as are classical flood estimates focusing on peak discharge only. Various sources of uncertainty contribute to the total uncertainty of synthetic design hydrographs for gauged and ungauged catchments. These comprise model uncertainties, sampling uncertainty, and uncertainty due to the choice of a regionalization method. A quantification of the uncertainties associated with flood estimates is essential for reliable decision making and allows for the identification of important uncertainty sources. We therefore propose an uncertainty assessment framework for the quantification of the uncertainty associated with synthetic design hydrographs. The framework is based on bootstrap simulations and consists of three levels of complexity. On the first level, we assess the uncertainty due to individual uncertainty sources. On the second level, we quantify the total uncertainty of design hydrographs for gauged catchments and the total uncertainty of regionalizing them to ungauged catchments but independently from the construction uncertainty. On the third level, we assess the coupled uncertainty of synthetic design hydrographs in ungauged catchments, jointly considering construction and regionalization uncertainty. We find that the most important sources of uncertainty in design hydrograph construction are the record length and the choice of the flood sampling strategy. The total uncertainty of design hydrographs in ungauged catchments depends on the catchment properties and is not negligible in our case.

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Uncertainty analysis of hydrological return period estimation, taking the upper Yangtze River as an example

Cited by 6 publications

References 49 publications

Analysis of extreme flow uncertainty impact on size of flood hazard zones for the Wronki gauge station in the Warta river

Analysis of extreme flow uncertainty impact on size of flood hazard zones for the Wronki gauge station in the Warta river

The role of input and hydrological parameters uncertainties in extreme hydrological simulations

Uncertainty Assessment of Synthetic Design Hydrographs for Gauged and Ungauged Catchments

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