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

Abstract

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.

Análisis regional de frecuencia de precipitaciones extremas en el Norte de Mozambique

“…De las distribuciones aceptadas, la de mejor ajuste a escala regional es la que cumple con el criterio del valor mínimo de Z DIST ≤ |1.64|, por lo que se optó finalmente por la elección de la función de distribución Logística Generalizada (GLO). Estos resultados son concordantes con los obtenidos por Ngongondo et al (2011) para tres regiones de Malawi, país vecino con la región noroccidental de Mozambique, en las que las distribuciones GLO y GEV también mostraron el mejor ajuste a las series de máximos anuales de precipitaciones. Ingeniería del Agua | 20.1 | 2016 2016, IWA Publishing, Editorial UPV, FFIA En el diagrama de la siguiente figura se muestran las relaciones teóricas entre los LCs y LCk dadas por Hosking y Wallis (1997) para cada una de las funciones de distribución candidatas.…”

“…En Sudáfrica, Smithers y Schulze (2001) utilizaron los L-momentos para el análisis de tormentas de corta duración. En Malawi, país limítrofe con la frontera occidental del norte de Mozambique, Ngongondo et al (2011) aplicaron el análisis regional de frecuencia basado en el método del índice de avenida y los L-momentos a las series anuales de precipitaciones máximas de 23 estaciones localizadas en la región meridional del país.…”

Modelling tree growth to determine the sustainability of current off-take from miombo woodland: a case study from rural villages in Malawi

“…The mean annual temperature is 22°C and annual rainfall, confined to the period from November to April, ranges from 600 mm to 1225 mm (Ngongondo et al 2011). The elevation of the study area ranges from 625 m above sea level in the low lying areas around Lake Chilwa to 1450 m in the Zomba-Malosa Forest Reserve.…”

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

“…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.…”