Bayesian Regional Flood Frequency Analysis for Large Catchments

Thorarinsdottir, Thordis L.; Hellton, Kristoffer H.; Steinbakk, Gunnhildur H.; Schlichting, Lena; Engeland, Kolbjørn

doi:10.1029/2017wr022460

Cited by 21 publications

(24 citation statements)

References 26 publications

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“…The leave-one-out cross-validation method was used to reveal how well the Bayesian model can perform in truly out-ofsample predictions recognizing that different climate epochs may lead to different model performance (Chen et al 2014, Thorarinsdottir et al 2018. First, the first year is extracted, and the model is trained by the remaining years.…”

Section: Indices For Model Evaluationmentioning

confidence: 99%

An improved nonstationary model for flood frequency analysis and its implication for the Three Gorges Dam, China

Dong

Zhang

Lall

et al. 2019

Hydrological Sciences Journal

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This study proposes an improved nonstationary model for flood frequency analysis by investigating the relationship between flood peak and flood volume, using the Three Gorges Dam (TGD), China, for verification. First, the generalized additive model for location, scale and shape (GAMLSS) is used as the prior distribution. Then, under Bayesian theory, the prior distribution is updated using the conditional distribution, which is derived from the copula function. The results show that the improvement of the proposed model is significant compared with the GAMLSS-based prior distribution. Meanwhile, selection of a suitable prior distribution has a significant effect on the results of the improvement. For applications to the TGD, the nonstationary model can obviously increase the engineering management benefits and reduce the perceived risks of large floods. This study provides guidance for the dynamic management of hydraulic engineering under nonstationary conditions.

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Section: Indices For Model Evaluationmentioning

confidence: 99%

An improved nonstationary model for flood frequency analysis and its implication for the Three Gorges Dam, China

Dong

Zhang

Lall

et al. 2019

Hydrological Sciences Journal

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“…Flood frequency analysis (FFA) is a statistical approach aiming at determining the magnitude of floods for a predefined return period (Thorarinsdottir et al 2018). The simplest approach in FFA is to model data at a single site (at-site FFA or local modelling, Thorarinsdottir et al 2018). However, when at-site data are limited, the models' results can be very uncertain.…”

Section: Flood Frequency Analysis and Hydrological Signaturesmentioning

confidence: 99%

“…To obtain accurate results, information from adjacent or similar sites can be exploited. This approach is termed regional flood frequency analysis (RFFA, Thorarinsdottir et al 2018). Transfer of information from one catchment to the other can be achieved by purely data-based or by rainfall-runoff models.…”

Section: Flood Frequency Analysis and Hydrological Signaturesmentioning

confidence: 99%

How to Explain and Predict the Shape Parameter of the Generalized Extreme Value Distribution of Streamflow Extremes Using a Big Dataset

Tyralis

Papacharalampous

Tantanee

2018

Preprint

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The finding of important explanatory variables for the location parameter and the scale parameter of the generalized extreme value (GEV) distribution, when the latter is used for the modelling of annual streamflow maxima, is known to have reduced the uncertainties in inferences, as estimated through regional flood frequency analysis frameworks. However, important explanatory variables have not been found for the GEV shape parameter, despite its critical significance, which stems from the fact that it determines the behaviour of the upper tail of the distribution. Here we examine the nature of the shape parameter by revealing its relationships with basin attributes. We use a dataset that comprises information about daily streamflow and forcing, climatic indices, topographic, land cover, soil and geological characteristics of 591 basins with minimal human influence in the contiguous United States. We propose a framework that uses random forests and linear models to find (a) important predictor variables of the shape parameter and (b) an interpretable model with high predictive performance. The process of study comprises of assessing the predictive performance of the models, selecting a parsimonious predicting model and interpreting the results in an ad-hoc manner. The findings suggest that the shape parameter mostly depends on climatic indices, while the selected prediction model results in more than 20% higher accuracy in terms of RMSE compared to a naïve approach. The implications are important, since incorporating the regression model into regional flood frequency analysis frameworks can considerably reduce the predictive uncertainties.

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“…Unlike the FFD estimation relying on the flood samples at a single gauging site, the regional flood frequency analysis utilizes the flood samples of a group of neighboring catchments, which contain the information about the regional homogeneity of catchment characteristics controlling flood generations. The methods of regional flood frequency analysis have been shown to be able to improve the accuracy of FFD estimation and apply to ungauged regions (Dawdy et al., 2012; Hosking & Wallis, 1997; Kroll & Stedinger, 1998; Thorarinsdottir et al., 2018). Eagleson (1972) outlined a process‐based approach for FFD estimation, in which FFD is no longer directly estimated from at‐site flood samples based on an assumed theoretical probability distribution, but is derived by the probability distribution of hydrological input variables (such as rainfall intensity, rainfall duration and soil moisture) via a hydrological model (Sivapalan et al., 2005; Yu et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

A River Network‐Based Hierarchical Model for Deriving Flood Frequency Distributions and Its Application to the Upper Yangtze Basin

Jiang

Xiong

et al. 2021

Water Resources Research

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In flood frequency analysis, expanding the additional information with hydrological reasoning beyond local at‐site flood samples can be very useful for improving the accuracy of flood frequency distribution (FFD) estimation as well as reflecting a better understanding of flood characteristics. In this study, a river network‐based hierarchical model is developed to estimate the FFDs in the Upper Yangtze basin by making full use of the hydrological reasoning information of both the flood dependence within the river network and reservoir regulation. Under this hierarchical model, a covariate analysis based on the generalized additive model for location, scale and shape is performed to obtain the conditional distribution of the interested flood variable given both its upstream flood variables and the reservoir index quantifying reservoir regulation; and then, the FFD of the interested flood variable is derived by combining its conditional distribution with the probability distribution of the upstream flood variables. The application to the Upper Yangtze basin indicates that the proposed hierarchical model suggests a satisfactory performance in FFD estimation. It is also found that the reservoir regulation, especially that of the Three Gorges Reservoir, is of great significance in reducing the flood magnitude in the basin. Compared to the conventional FFD estimation method that directly fits the assumed theoretical probability distributions to the at‐site flood samples, the hierarchical model incorporating the flood dependence within the river network exhibits an advantage in capturing the effect of reservoir regulation on the floods as well as in reducing the uncertainty in flood quantile estimation.

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Bayesian Regional Flood Frequency Analysis for Large Catchments

Cited by 21 publications

References 26 publications

An improved nonstationary model for flood frequency analysis and its implication for the Three Gorges Dam, China

An improved nonstationary model for flood frequency analysis and its implication for the Three Gorges Dam, China

How to Explain and Predict the Shape Parameter of the Generalized Extreme Value Distribution of Streamflow Extremes Using a Big Dataset

A River Network‐Based Hierarchical Model for Deriving Flood Frequency Distributions and Its Application to the Upper Yangtze Basin

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