Identification of homogeneous rainfall regimes in parts of Western Ghats region of Karnataka

Venkatesh, B.; Jose, Mathew K.

doi:10.1007/s12040-007-0029-z

Cited by 48 publications

(23 citation statements)

References 7 publications

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“…Baeriswyl and Rebetez, 1996;Singh and Singh, 1996); cluster analysis (e.g. Easterling, 1989;Venkatesh and Jose, 2007); combination of PCA and cluster analysis (e.g. Dinpashoh et al, 2004); L-moments associated with cluster analysis (e.g.…”

Section: Introductionmentioning

confidence: 99%

Regional frequency analysis of daily rainfall extremes using L-moments approach

Malekinezhad¹,

Zare-Garizi²

2014

Atmósfera

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RESUMENLos valores extremos de precipitación diaria se encuentran entre los sucesos ambientales con consecuencias más desastrosas para la sociedad. La información sobre las magnitudes y frecuencias de las precipitaciones extremas es vital para el manejo sostenible de los recursos hídricos, la planeación de emergencias vinculadas con el clima y el diseño de estructuras hidráulicas. En este trabajo se analiza la frecuencia de precipitaciones diarias máximas en la provincia de Golestán, localizada en el noreste de Irán. Se trataron de encontrar distribuciones de frecuencias regionales adecuadas para precipitaciones máximas diarias y de predecir los valores de retorno de episodios extremos de precipitación (diseño de la profundidad de la precipitación). Se aplicó la regionalización de procedimientos de momentos-L, en conjunto con un método de indización de las precipitaciones, a los registros máximos de precipitación de 47 estaciones en el área de estudio. Debido investigación fue la desagregación del área en subregiones coherentes y homogéneas. Así, se dividió el área de estudio en cinco regiones homogéneas con base en análisis de conglomerados de las características locales y en pruebas de homogeneidad regional. Los resultados de la precisión del ajuste indicaron que la mejor distribución es diferente para cada región homogénea. La diferencia puede deberse a las condiciones precisión, obtenidas mediante simulaciones de Monte Carlo, demuestran que la estimación de incertidumbre mediante valores de la raíz cuadrada del error cuadrático medio (RMSE, por sus siglas en inglés) y límites del error estadístico de 90%, es relativamente baja cuando los periodos de retorno son menores de 100 años. Sin embargo, para periodos más largos, las estimaciones de precipitación deben tomarse con cautela. Más años por estación, ya sea por registros más largos o por más estaciones en las regiones, se requerirían para estimaciones de precipitación mayores a T = 100 años. El análisis encontró que el índice de precipitación (promedio in situ de la máxima precipitación) puede estimarse razonablemente bien como una función de la precipitación media anual en la provincia de Golestán. Pueden utilizarse índices de precipitación combinados con curvas de crecimiento regional para calcular precipitaciones de diseños en sitios carentes de sistemas de medición. En general se encontró que el análisis de conglomerados, en conjunto con la técnica de análisis regional de frecuencias basada en momentos-L, puede aplicarse de manera exitosa para obtener estimados de precipitaciones de diseño en el noreste de Irán. El enfoque de este trabajo y sus resultados tienen gran clima y el diseño de estructuras hidráulicas. ABSTRACTDaily extreme precipitation values are among environmental events with the most disastrous consequences for human society. Information on the magnitudes and frequencies of extreme precipitations is essential for sustainable water resources management, planning for weather-related emergencies, and design of hydraulic structures. In the p...

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Section: Introductionmentioning

confidence: 99%

Regional frequency analysis of daily rainfall extremes using L-moments approach

Malekinezhad¹,

Zare-Garizi²

2014

Atmósfera

View full text Add to dashboard Cite

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“…Baeriswyl and Rebetez 1996;Singh and Singh 1996); cluster analysis (e.g. Easterling 1989;Venkatesh and Jose 2007); combination of PCA and cluster analysis (e.g. Dinpashoh et al 2004); L-moments in association with cluster analysis (e.g.…”

Section: Introductionmentioning

confidence: 99%

Regional frequency analysis of rainfall extremes in Southern Malawi using the index rainfall and L-moments approaches

Ngongondo

Tallaksen

et al. 2011

Stoch Environ Res Risk Assess

117

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Rainfall extremes often result in the occurrence of flood events with associated loss of life and infrastructure in Malawi. However, an understanding of the frequency of occurrence of such extreme events either for design or disaster planning purposes is often limited by data availability at the desired temporal and spatial scales. Regionalisation, which involves ''trading time for space'' by pooling together observations for stations with similar behavior, is an alternative approach for more accurate determination of extreme events even at ungauged areas or sites with short records. In this study, regional frequency analysis of rainfall extremes in Southern Malawi, large parts of which are flood prone, was undertaken. Observed 1-, 3-, 5-and 7-day annual maximum rainfall series for the period 1978-2007 at 23 selected rainfall stations in Southern Malawi were analysed. Cluster analysis using scaled at-site characteristics was used to determine homogeneous rainfall regions. L-moments were applied to derive regional index rainfall quantiles. The procedure also validated the three rainfall regions identified through homogeneity and heterogeneity tests based on Monte Carlo simulations with regional average L-moment ratios fitted to the Kappa distribution. Based on assessments of the accuracy of the derived index rainfall quantiles, it was concluded that the performance of this regional approach was satisfactory when validated for sites not included in the sample data. The study provides an estimate of the regional characteristics of rainfall extremes that can be useful in among others flood mitigation and engineering design.

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“…The study discussed multiple non-linear form, linear regressions, an intensity-duration-frequency (IDF) relationship curve and obtained model accuracies in the range of 18-86%. There has been a number of identical studies up to now, exploring and updating different working methods for RFA of extreme rainfall occurrences, the most prominent ones are Cluster analysis (Easterling 1989;Venkatesh and Jose 2007), L-moments analysis (Hosking 1990), L-moments associated with cluster analysis (Schaefer 1990;Guttman 1993;Wallis et al 2007;Satyanarayana and Srinivas 2008), spatial correlation analysis (Gadgil and Yadumani 1993), homogeneity test (Wiltshire 1986) and regional frequency analysis techniques (Eslamian and Feizi 2007;Ngongondo et al 2011;Hossein and Arash 2014;Zhang and Hall, 2004). Due to the data shortage and hydrometeorological complexity, the L-moment approach for RFA was carried out to ensure efficient estimation of extreme rainfall occurrences, taking account of the spatial variability of the study area.…”

Section: Introductionmentioning

confidence: 99%

Regional frequency analysis for consecutive hour rainfall using L-moments approach in Jeju Island, Korea

Kar

Yang

Lee

et al. 2017

Geoenviron Disasters

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Background: Extreme rainfall events are enormously frequent and abrupt in tropical areas like the Jeju Island of South Korea, impacting the hydrological functions as well as the social and economic situation. Rainfall magnitude and frequency distribution related information are essential for water system design, water resources management and hydro-meteorological emergencies. This study therefore has investigated the use of L-moments approach for hourly regional rainfall frequency estimation so as to ensure better accuracy and efficiency of the estimation process from the usually limited data sets. Results: The Hancheon catchment was considered as the primary study domain and several best fitted statistical tools were used to analyze consecutive hour rainfall data from five hydro-meteorological stations (Jeju, Ara, Eorimok, Witsaeorum and Jindallaebat) adjacent to the area. The cluster analysis and discordancy measure categorized the Hancheon catchment in three regions (1, 2 and 3). Based on the L-moments heterogeneity and goodness-of-fit measure, Gumbel and generalized extreme value (GEV) distribution were identified as robust distributions for the study area. The RMSE ratios for the catchment were found as 0.014 to 0.237 for Gumbel and 0.115 to 0.301 for GEV distribution. The linear regression analysis of the different rainfall quantiles inferred r-square values from 0.842 to 0.974. Conclusions: The L-moments and other statistical information derived from the study can be useful for important hydrological design considerations in connection with flood risk management, mitigation and safety; whereas the methodological framework of the study may be suitable for other small scaled catchment areas with high slope.

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Identification of homogeneous rainfall regimes in parts of Western Ghats region of Karnataka

Cited by 48 publications

References 7 publications

Regional frequency analysis of daily rainfall extremes using L-moments approach

Regional frequency analysis of daily rainfall extremes using L-moments approach

Regional frequency analysis of rainfall extremes in Southern Malawi using the index rainfall and L-moments approaches

Regional frequency analysis for consecutive hour rainfall using L-moments approach in Jeju Island, Korea

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