Heterogeneity measures in hydrological frequency analysis: review and new developments

Requena, Ana I.; Chebana, Fateh; Ouarda, Taha B. M. J.

doi:10.5194/hess-21-1651-2017

Cited by 15 publications

(11 citation statements)

References 53 publications

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“…The performance of several pooling group approaches may be assessed in many ways (e.g., Requena, Chebana, & Ouarda, ). In the present study, the assessment of the approaches for quantile estimation is done by a procedure through which both quantile estimation and uncertainty are evaluated to identify an accurate and robust approach.…”

Section: Methodsologymentioning

confidence: 99%

Pooled frequency analysis for intensity–duration–frequency curve estimation

Requena

Burn

Coulibaly

2019

Hydrological Processes

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Rainfall intensity–duration–frequency (IDF) curves are used in the design of urban infrastructure. Their estimation is based on rainfall frequency analysis, usually performed on rainfall records from a single gauged station. However, available at‐site record length is often too short to provide accurate estimates for long return periods. In the present study, a general framework for pooled rainfall frequency analysis based on the index‐event model is proposed for IDF estimation at gauged stations. Pooling group formation is defined by the region of influence approach on the basis of the geographical distance similarity measure. Several pooled approaches are defined and evaluated by a procedure through which quantile estimation and uncertainty are assessed. Alternate approaches for the definition of a pooling group are based on different criteria regarding initial pooling group size (and the relationship between size and return period), approaches for assessing pooling group homogeneity, and the use of macroregions in pooling group formation. The proposed framework is applied to identify the preferred approach for pooled rainfall intensity frequency analysis in Canada. Pooled approaches are found to provide more precise estimates than the at‐site approach, especially for long return periods. Pooled parent distribution selection supported the use of the generalized extreme value distribution across the country. Recommendations for pooling group formation include increasing the pooling group size with increases in return period and identifying an appropriate trade‐off between pooling group homogeneity and size for long return periods.

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Section: Methodsologymentioning

confidence: 99%

Pooled frequency analysis for intensity–duration–frequency curve estimation

Requena

Burn

Coulibaly

2019

Hydrological Processes

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“…G ranges from 0 to 1: the former is obtained when all t (i)' s are equal and the latter when all but one value are equal to zero. Requena et al (2017) demonstrated that, in the context of a simulation framework, the G performed as the best heterogeneity measure among the evaluated measures.…”

Section: Heterogeneity Measures For Pooling-groupsmentioning

confidence: 94%

“…1127 Water Resources Research PUBLICATIONS Viglione et al (2007) and Castellarin et al (2008). Recently, Requena et al (2017) tested the performance of different heterogeneity measures in the context of a simulation-based general framework, concluding that the assumption-free Gini index (Ceriani & Verme, 2012;Gini, 1912) applied to the at-site L-variation coefficient (L-CV) over a region provided the best results.…”

Section: Introductionmentioning

confidence: 99%

Use of Flood Seasonality in Pooling‐Group Formation and Quantile Estimation: An Application in Great Britain

Formetta

Bell

Stewart

2018

Water Resources Research

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Regional flood frequency analysis is one of the most commonly applied methods for estimating extreme flood events at ungauged sites or locations with short measurement records. It is based on: (i) the definition of a homogeneous group (pooling‐group) of catchments, and on (ii) the use of the pooling‐group data to estimate flood quantiles. Although many methods to define a pooling‐group (pooling schemes, PS) are based on catchment physiographic similarity measures, in the last decade methods based on flood seasonality similarity have been contemplated. In this paper, two seasonality‐based PS are proposed and tested both in terms of the homogeneity of the pooling‐groups they generate and in terms of the accuracy in estimating extreme flood events. The method has been applied in 420 catchments in Great Britain (considered as both gauged and ungauged) and compared against the current Flood Estimation Handbook (FEH) PS. Results for gauged sites show that, compared to the current PS, the seasonality‐based PS performs better both in terms of homogeneity of the pooling‐group and in terms of the accuracy of flood quantile estimates. For ungauged locations, a national‐scale hydrological model has been used for the first time to quantify flood seasonality. Results show that in 75% of the tested locations the seasonality‐based PS provides an improvement in the accuracy of the flood quantile estimates. The remaining 25% were located in highly urbanized, groundwater‐dependent catchments. The promising results support the aspiration that large‐scale hydrological models complement traditional methods for estimating design floods.

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“…Numerous time and frequency analysis methods are usually adopted to investigate the variations of flood event metrics at station scale, including temporal trend analysis methods (e.g., Mann-Kendall test, segmented regression model) (Shao and Campbell., 2002;Chen and Li, 2011a;Zhang et al, 2011b;Wang et al, 2015), change point tests (e.g., Pettit test, Mann-Kendall test, moving t-test) (Zheng et al, 2007;Li et al, 2008;She et al, 2017), periodicity analysis (e.g., wavelet analysis) (Anctil, 2010;Yang et al, 2018) and frequency analysis (e.g., copula analysis, entropy theory) (Zhang et al, 2011a;Singh, 2015). Most of existing studies mainly focus on flood magnitude metrics (e.g., flood volume, peak flood) and their frequencies at station scale (Milly et al, 2002a;Rogger et al, 2012;Xia et al, 2012;Ma et al, 2014;Requena et al, 2017;Merz et al, 2018;Sun et al, 2018). However, all of these findings are still difficult to reveal comprehensive variation characteristics at basin scale.…”

Section: Introductionmentioning

confidence: 99%

Investigation on flood event variations at space and time scales in the Huaihe River Basin of China using flood behavior classification

2020

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Flood is one of the severest natural disasters in the world and has caused enormous causalities and property losses. Previous studies usually focus on flood magnitude and occurrence time at event scale, which are insufficient to contain entire behavior characteristics of flood events. In our study, nine behavior metrics in five categories (e.g., magnitude, duration, timing, rates of changes and variability) are adopted to fully describe a flood event. Regional and interannual variations of representative flood classes are investigated based on behavior similarity classification of numerous events. Contributions of geography, land use, hydrometeorology and human regulation on these variations are explored by rank analysis method. Results show that: five representative classes are identified, namely, conventional events (Class 1, 61.7% of the total), low discharge events with multiple peaks (Class 2, 5.3%), low discharge events with low rates of changes (Class 3, 18.1%), low discharge events with high rates of changes (Class 4, 10.8%) and high discharge events with long durations (Class 5, 4.1%). Classes 1 and 3 are the major flood events and distributed across the whole region. Class 4 is mainly distributed in river sources, while Classes 2 and 5 are in the middle and down streams. Moreover, the flood class is most diverse in normal precipitation years (

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Heterogeneity measures in hydrological frequency analysis: review and new developments

Cited by 15 publications

References 53 publications

Pooled frequency analysis for intensity–duration–frequency curve estimation

Pooled frequency analysis for intensity–duration–frequency curve estimation

Use of Flood Seasonality in Pooling‐Group Formation and Quantile Estimation: An Application in Great Britain

Investigation on flood event variations at space and time scales in the Huaihe River Basin of China using flood behavior classification

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