Regional flood frequency analysis in eastern Australia: Bayesian GLS regression-based methods within fixed region and ROI framework – Quantile Regression vs. Parameter Regression Technique

Haddad, Khaled; Rahman, Ataur

doi:10.1016/j.jhydrol.2012.02.012

Cited by 136 publications

(102 citation statements)

References 43 publications

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“…Table 1 also provides a comparison of the performance of the index-flood and the regressionbased models. In terms of RRMSE and NHS criterion, the two approaches will lead to very similar results, which is consistent with what it is reported in other studies (GRE-HYS, 1996a, b;Haddad and Rahman, 2012). Therefore, similar conclusions can be drawn from the two approaches.…”

Section: Results Of the Regression-based Modelsupporting

confidence: 91%

“…It is hence necessary to develop procedures to transfer, or to regionalize, the available information at existing gauged sites to the ungauged ones. regional flood frequency analysis (RFFA) represents a large class of techniques commonly used in water sciences to evaluate the risk of occurrence of extreme hydrological phenomena of rare magnitudes at ungauged locations (Haddad and Rahman, 2012;Hosking and Wallis, 1997;Laio et al, 2011;Pandey, 1998;Reis et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Delineation of homogenous regions using hydrological variables predicted by projection pursuit regression

Durocher

Chebana

Ouarda

2016

Hydrol. Earth Syst. Sci.

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Abstract. This study investigates the utilization of hydrological information in regional flood frequency analysis (RFFA) to enforce desired properties for a group of gauged stations. Neighbourhoods are particular types of regions that are centred on target locations. A challenge for using neighbourhoods in RFFA is that hydrological information is not available at target locations and cannot be completely replaced by the available physiographical information. Instead of using the available physiographic characteristics to define the centre of a target location, this study proposes to introduce estimates of reference hydrological variables to ensure a better homogeneity. These reference variables represent nonlinear relations with the site characteristics obtained by projection pursuit regression, a nonparametric regression method. The resulting neighbourhoods are investigated in combination with commonly used regional models: the index-flood model and regression-based models. The complete approach is illustrated in a real-world case study with gauged sites from the southern part of the province of Québec, Canada, and is compared with the traditional approaches such as region of influence and canonical correlation analysis. The evaluation focuses on the neighbourhood properties as well as prediction performances, with special attention devoted to problematic stations. Results show clear improvements in neighbourhood definitions and quantile estimates.

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Section: Results Of the Regression-based Modelsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Delineation of homogenous regions using hydrological variables predicted by projection pursuit regression

Durocher

Chebana

Ouarda

2016

Hydrol. Earth Syst. Sci.

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“…A first possibility to achieve this improvement may be to include other descriptors. Alternative strategies include moving from fixed regions to a region-ofinfluence approach (e.g., Burn, 1990;Haddad and Rahman, 2012), or using specialized geostatistical methods to transfer information along the hydrologic network (e.g., Gottschalk, 1993;Sauquet, 2006;Skøien et al, 2006;Laaha et al, 2014). In addition, combining several implementations may also yield further improvement.…”

Section: Discussionmentioning

confidence: 99%

A data-based comparison of flood frequency analysis methods used in France

Kochanek

Renard²,

Arnaud³

et al. 2014

Nat. Hazards Earth Syst. Sci.

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Abstract. Flood frequency analysis (FFA) aims at estimating quantiles with large return periods for an extreme discharge variable. Many FFA implementations are used in operational practice in France. These implementations range from the estimation of a pre-specified distribution to continuous simulation approaches using a rainfall simulator coupled with a rainfall-runoff model. This diversity of approaches raises questions regarding the limits of each implementation and calls for a nation-wide comparison of their predictive performances.This paper presents the results of a national comparison of the main FFA implementations used in France. More accurately, eight implementations are considered, corresponding to the local, regional and local-regional estimation of Gumbel and Generalized Extreme Value (GEV) distributions, as well as the local and regional versions of a continuous simulation approach. A data-based comparison framework is applied to these eight competitors to evaluate their predictive performances in terms of reliability and stability, using daily flow data from more than 1000 gauging stations in France.Results from this comparative exercise suggest that two implementations dominate their competitors in terms of predictive performances, namely the local version of the continuous simulation approach and the local-regional estimation of a GEV distribution. More specific conclusions include the following: (i) the Gumbel distribution is not suitable for Mediterranean catchments, since this distribution demonstrably leads to an underestimation of flood quantiles; (ii) the local estimation of a GEV distribution is not recommended, because the difficulty in estimating the shape parameter results in frequent predictive failures; (iii) all the purely regional implementations evaluated in this study displayed a quite poor reliability, suggesting that prediction in completely ungauged catchments remains a challenge.

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“…Reis et al [21] introduced a Bayesian analysis of the Generalized Least Squares (GLS) model, which provides measures of the model error variance that are more accurate. Many studies with regression-based techniques [22][23][24][25][26][27][28][29][30][31] have been used to describe quantitative relationships between independent and dependent variables.…”

Section: Introductionmentioning

confidence: 99%

The estimation of flood quantiles in ungauged sites using teaching-learning based optimization and artificial bee colony algorithms

et al. 2017

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Abstract. In this study, a Regional Flood Frequency Analysis (RFFA) was applied to 33 stream gauging stations in the Eastern Black Sea Basin, Turkey. Homogeneity of the region was determined by discordancy (D i ) and heterogeneity measures (H i ) based on L-moments. Generalized extreme-value, lognormal, Pearson type III, and generalized logistic distributions were tted to the ood data of the homogeneous region. Based on the appropriate distribution for the region, ood quantiles were estimated for return periods of T = 5; 10; 25; 50; 100, and 500 years. A non-linear regression model was then developed to determine the relationship between ood discharges and meteorological and hydrological characteristics of the catchment. In order to compare regression analysis with other models, Arti cial Bee Colony algorithm (ABC) and Teaching-Learning Based Optimization (TLBO) models were developed. The equations were obtained using the ABC and TLBO algorithms to estimate ood discharges for di erent return periods. The analysis showed that the TLBO and ABC results were superior to the regression analysis. Error values indicated that the TLBO method yielded better results for the estimation of ood quantiles for di erent independent variables.

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Regional flood frequency analysis in eastern Australia: Bayesian GLS regression-based methods within fixed region and ROI framework – Quantile Regression vs. Parameter Regression Technique

Cited by 136 publications

References 43 publications

Delineation of homogenous regions using hydrological variables predicted by projection pursuit regression

Delineation of homogenous regions using hydrological variables predicted by projection pursuit regression

A data-based comparison of flood frequency analysis methods used in France

The estimation of flood quantiles in ungauged sites using teaching-learning based optimization and artificial bee colony algorithms

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