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

Daily 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 present study, regional frequency analysis of maximum daily rainfalls was investigated for Golestan province located in the northeastern Iran. This study aimed to find appropriate regional frequency distributions for maximum daily rainfalls and predict the return values of extreme rainfall events (design rainfall depths) for the future. L-moment regionalization procedures coupled with an index rainfall methodwere applied to maximum rainfall records of 47 stations across the study area. Due to complex geographicand hydro-climatological characteristics of the region, an important research issue focused on breaking downthe large area into homogeneous and coherent sub-regions. The study area was divided into five homogeneousregions, based on the cluster analysis of site characteristics and tests for the regional homogeneity.The goodness-of-fit results indicated that the best fitting distribution is different for individual homogeneousregions. The difference may be a result of the distinctive climatic and geographic conditions. The estimatedregional quantiles and their accuracy measures produced by Monte Carlo simulations demonstrate that theestimation uncertainty as measured by the RMSE values and 90% error bounds is relatively low when returnperiods are less than 100 years. But, for higher return periods, rainfall estimates should be treated withcaution. More station years, either from longer records or more stations in the regions, would be required forrainfall estimates above T=100 years. It was found from the analyses that, the index rainfall (at-site averagemaximum rainfall) can be estimated reasonably well as a function of mean annual precipitation in Golestanprovince. Index rainfalls combined with the regional growth curves, can be used to estimate design rainfallsat ungauged sites. Overall, it was found that cluster analysis together with the L-moments based regional frequencyanalysis technique could be applied successfully in deriving design rainfall estimates for northeasternIran. The approach utilized in this study and the findings are of great scientific and practical merit, particularlyfor the purpose of planning for weather-related emergencies and design of hydraulic engineering structures

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Regional frequency analysis of rainfall extremes in the Pearl River Basin using L-moments method

2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery ◽

10.1109/fskd.2010.5569801 ◽

2010 ◽

Author(s):

Tao Yang ◽

Xiaoyan Wang ◽

Xiaobo Hao ◽

Huihui Li

Keyword(s):

Frequency Analysis ◽

Regional Frequency Analysis ◽

Pearl River ◽

Pearl River Basin ◽

Moments Method ◽

Rainfall Extremes ◽

L Moments ◽

The Pearl River ◽

The Pearl River Basin ◽

Regional Frequency

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Regional Frequency Analysis of Wabash River Flood Data by L-Moments

Journal of Hydrologic Engineering ◽

10.1061/(asce)1084-0699(1997)2:4(169) ◽

1997 ◽

Vol 2 (4) ◽

pp. 169-179 ◽

Cited By ~ 22

Author(s):

A. Ramachandra Rao ◽

Khaled H. Hamed

Keyword(s):

Frequency Analysis ◽

Regional Frequency Analysis ◽

Wabash River ◽

River Flood ◽

L Moments ◽

Regional Frequency

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At-site and regional frequency analysis of extreme precipitation from radar-based estimates

10.5194/hess-2017-150 ◽

2017 ◽

Author(s):

Edouard Goudenhoofdt ◽

Laurent Delobbe ◽

Patrick Willems

Keyword(s):

Confidence Interval ◽

Frequency Analysis ◽

Extreme Precipitation ◽

Rain Gauge ◽

Regional Frequency Analysis ◽

Extreme Value Analysis ◽

Value Analysis ◽

Rain Gauges ◽

Rainfall Extremes ◽

Regional Frequency

Abstract. In Belgium, only rain gauge time-series have been used so far to study extreme precipitation at a given location. In this paper, the potential of a 12-year quantitative precipitation estimation (QPE) from a single weather radar is evaluated. For the period 2005–2016, independent sliding 1 h and 24 h rainfall extremes from automatic rain gauges and collocated radar estimates are compared. The extremes are fitted to the exponential distribution using regression in QQ-plots with a threshold rank which minimises the mean squared error. A basic radar product used as reference exhibits unrealistic high extremes and is not suitable for extreme value analysis. For 24 h rainfall extremes, which occur partly in winter, the radar-based QPE needs a bias correction. A few missing events are caused by the wind drift of convective cells and strong radar signal attenuation. Differences between radar and gauge values are caused by spatial and temporal sampling, gauge rainfall underestimations and radar errors due to the relation between reflectivity and rain rate. Nonetheless the fit to the QPE data is within the confidence interval of the gauge fit, which remains large due to the short study period. A regional frequency analysis is performed on radar data within 20 km of the locations of 4 rain gauges with records from 1965 to 2008. Assuming that the extremes are correlated within the region, the fit to the two closest rain gauge data is within the confidence interval of the radar fit, which is small due to the sample size. In Brussels, the extremes on the period 1965–2008 from a rain gauge are significantly lower than the extremes from an automatic gauge and the radar on the period 2005–2016. For 1 h duration, the location parameter varies slightly with topography and the scale parameter exhibits some variations from region to region. The radar-based extreme value analysis can be extended to other durations.

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Regional Frequency Analysis of Extremes Precipitation Using L-Moments and Partial L-Moments

Advances in Meteorology ◽

10.1155/2017/6954902 ◽

2017 ◽

Vol 2017 ◽

pp. 1-20 ◽

Cited By ~ 3

Author(s):

Said Arab Khan ◽

Ijaz Hussain ◽

Tajammal Hussain ◽

Muhammad Faisal ◽

Yousaf Shad Muhammad ◽

...

Keyword(s):

Frequency Analysis ◽

Extreme Precipitation ◽

Regional Frequency Analysis ◽

Monthly Precipitation ◽

Monte Carlo Simulation Study ◽

Khyber Pakhtunkhwa ◽

Northern Areas ◽

L Moments ◽

Social Environmental ◽

Regional Frequency

Extremes precipitation may cause a series of social, environmental, and ecological problems. Estimation of frequency of extreme precipitations and its magnitude is vital for making decisions about hydraulic structures such as dams, spillways, and dikes. In this study, we focus on regional frequency analysis of extreme precipitation based on monthly precipitation records (1999–2012) at 17 stations of Northern areas and Khyber Pakhtunkhwa, Pakistan. We develop regional frequency methods based on L-moment and partial L-moments (L- and PL-moments). The L- and PL-moments are derived for generalized extreme value (GEV), generalized logistic (GLO), generalized normal (GNO), and generalized Pareto (GPA) distributions. The Z-statistics and L- and PL-moments ratio diagrams of GNO, GEV, and GPA distributions were identified to represent the statistical properties of extreme precipitation in Northern areas and Khyber Pakhtunkhwa, Pakistan. We also perform a Monte Carlo simulation study to examine the sampling properties of L- and PL-moments. The results show that PL-moments perform better than L-moments for estimating large return period events.

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Regional frequency analysis of extreme rainfall in Belgium based on radar estimates

Hydrology and Earth System Sciences ◽

10.5194/hess-21-5385-2017 ◽

2017 ◽

Vol 21 (10) ◽

pp. 5385-5399 ◽

Cited By ~ 11

Author(s):

Edouard Goudenhoofdt ◽

Laurent Delobbe ◽

Patrick Willems

Keyword(s):

Confidence Interval ◽

Frequency Analysis ◽

Extreme Rainfall ◽

Extreme Value ◽

Regional Frequency Analysis ◽

Extreme Value Analysis ◽

Value Analysis ◽

Temporal Sampling ◽

Rainfall Extremes ◽

Regional Frequency

Abstract. In Belgium, only rain gauge time series have been used so far to study extreme rainfall at a given location. In this paper, the potential of a 12-year quantitative precipitation estimation (QPE) from a single weather radar is evaluated. For the period 2005–2016, 1 and 24 h rainfall extremes from automatic rain gauges and collocated radar estimates are compared. The peak intensities are fitted to the exponential distribution using regression in Q-Q plots with a threshold rank which minimises the mean squared error. A basic radar product used as reference exhibits unrealistic high extremes and is not suitable for extreme value analysis. For 24 h rainfall extremes, which occur partly in winter, the radar-based QPE needs a bias correction. A few missing events are caused by the wind drift associated with convective cells and strong radar signal attenuation. Differences between radar and gauge rainfall values are caused by spatial and temporal sampling, gauge underestimations and radar errors. Nonetheless the fit to the QPE data is within the confidence interval of the gauge fit, which remains large due to the short study period. A regional frequency analysis for 1 h duration is performed at the locations of four gauges with 1965–2008 records using the spatially independent QPE data in a circle of 20 km. The confidence interval of the radar fit, which is small due to the sample size, contains the gauge fit for the two closest stations from the radar. In Brussels, the radar extremes are significantly higher than the gauge rainfall extremes, but similar to those observed by an automatic gauge during the same period. The extreme statistics exhibit slight variations related to topography. The radar-based extreme value analysis can be extended to other durations.

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