Abstract Regionalization is an essential technique for improving flood estimation accuracy by identifying homogeneous regions based on basin features such as morphology, land use, and hydrological factors. In this study, flood frequencies at the regional level were analysed using data from seven monitoring stations within the Upper Narmada Basin, India. The Index Flood method, which assumes similarity in frequency distributions across sites, simplifies the analysis process. To estimate the distribution parameters – location, scale, and shape – L-moments were applied due to their ability to produce nearly unbiased estimates. The annual maximum series (AMS) underwent quality checks, homogeneity tests, heterogeneity analysis, trend evaluation, and regional consistency assessments. Following these evaluations, suitable datasets were selected for regional flood frequency analysis, and parameters were estimated using both L-moment and Index Flood methods. Flood frequency patterns were determined for both gauged and ungauged catchments. The discordancy measure (di) test confirmed the appropriateness of the datasets from all seven stations for analysis. The use of the generalised extreme value (GEV) distribution was crucial for accurately predicting flood events across various return periods. The L-moment method was preferred over the Index Flood approach due to its simplicity, reliability, and avoidance of complex calculations. Research Highlights <UnorderedList Mark="Bullet"> <ItemContent> Regionalization enhances the accuracy of flood estimation by grouping hydrologically similar basins based on morphological, land-use, and climatic characteristics. </ItemContent> <ItemContent> Flood frequency analysis was conducted using data from seven gauging stations in the Upper Narmada Basin, India. </ItemContent> <ItemContent> The Index Flood approach was employed, assuming similar distribution shapes across all sites for simplification and regional coherence. </ItemContent> <ItemContent> L-moments were used for parameter estimation (location, scale, and shape), offering nearly unbiased and robust results. </ItemContent> <ItemContent> The Annual Maximum Series (AMS) underwent thorough quality checks including discordancy, homogeneity, heterogeneity, and trend tests. </ItemContent> <ItemContent> Both L-moment and Index Flood methods were used to estimate regional flood quantiles for gauged and ungauged catchments. </ItemContent> <ItemContent> The Generalized Extreme Value (GEV) distribution was identified as the best-fit model based on statistical goodness-of-fit criteria. </ItemContent> <ItemContent> L-moment techniques proved superior due to their simplicity, accuracy, and ease of application without complex computational needs. </ItemContent> </UnorderedList>

Analysing regional flood frequency in the Upper Narmada River Basin of central India through Index Flood and L-moment methodologies

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发表日期：

2025年11月8日

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Namrata Chandel,
P G Agnihotri,
J N Patel

摘要

Abstract

Regionalization is an essential technique for improving flood estimation accuracy by identifying homogeneous regions based on basin features such as morphology, land use, and hydrological factors. In this study, flood frequencies at the regional level were analysed using data from seven monitoring stations within the Upper Narmada Basin, India. The Index Flood method, which assumes similarity in frequency distributions across sites, simplifies the analysis process. To estimate the distribution parameters – location, scale, and shape – L-moments were applied due to their ability to produce nearly unbiased estimates. The annual maximum series (AMS) underwent quality checks, homogeneity tests, heterogeneity analysis, trend evaluation, and regional consistency assessments. Following these evaluations, suitable datasets were selected for regional flood frequency analysis, and parameters were estimated using both L-moment and Index Flood methods. Flood frequency patterns were determined for both gauged and ungauged catchments. The discordancy measure (d_i) test confirmed the appropriateness of the datasets from all seven stations for analysis. The use of the generalised extreme value (GEV) distribution was crucial for accurately predicting flood events across various return periods. The L-moment method was preferred over the Index Flood approach due to its simplicity, reliability, and avoidance of complex calculations.

Research Highlights

Regionalization enhances the accuracy of flood estimation by grouping hydrologically similar basins based on morphological, land-use, and climatic characteristics.

Flood frequency analysis was conducted using data from seven gauging stations in the Upper Narmada Basin, India.

The Index Flood approach was employed, assuming similar distribution shapes across all sites for simplification and regional coherence.

L-moments were used for parameter estimation (location, scale, and shape), offering nearly unbiased and robust results.

The Annual Maximum Series (AMS) underwent thorough quality checks including discordancy, homogeneity, heterogeneity, and trend tests.

Both L-moment and Index Flood methods were used to estimate regional flood quantiles for gauged and ungauged catchments.

The Generalized Extreme Value (GEV) distribution was identified as the best-fit model based on statistical goodness-of-fit criteria.

L-moment techniques proved superior due to their simplicity, accuracy, and ease of application without complex computational needs.