scholarly journals Regional frequency analysis of daily maximum rainfall in Haryana

MAUSAM ◽  
2021 ◽  
Vol 72 (4) ◽  
pp. 835-846
Author(s):  
MOHIT NAIN ◽  
B. K. HOODA
Keyword(s):  
Frequency Analysis ◽  
Return Period ◽  
Regional Distribution ◽  
Rain Gauge ◽  
Quantile Estimation ◽  
Regional Frequency Analysis ◽  
Daily Maximum ◽  
Maximum Rainfall ◽  
L Moments ◽  
Regional Frequency

This paper is sets-out for the regional frequency analysis of daily maximum rainfall from the 27 rain gauge stations in Haryana using L-moments. As the distribution of rainfall varies spatially in Haryana, the 27 rain gauge stations are grouped into three clusters namely, cluster C1, C2 and C3 using Ward’s clustering method and homogeneity of clusters was confirmed using L-moments-based Heterogeneity measure (H). Using goodness-of-fit measure ( DIST Z ) and L-moment ratios diagram, suitable regional frequency distributions were selected among five candidate distributions;Generalized Logistic (GLO), Generalized Extreme Value (GEV),Generalized Normal (GNO), Generalized Pareto (GPA), and Pearson Type-3 (PE3) for each cluster. Results showed that PE3 and GNO were good fitted regional distribution for the cluster C1 and GEV, PE3 and GNO fitted for cluster C2 while for cluster C3; GLO and GEV were good fitted regional distribution. To select a robust distribution among good fitted distributions accuracy measures calculated using Monte Carlo simulations for each cluster. The simulation result showed that PE3 was the best choice for quantile estimation for cluster C1. For cluster C2, PE3 was the best choicefor a large return period and GEV was best for a small return period. For cluster C3, GEV was the most suitable distribution for quantile estimation. Using these robust distributions rainfall quantiles were estimated at each rain gauge station from 2 to 100 year return periods. These estimated rainfall quantiles may be rough guideline for planning and designing hydraulic structures by policy makers and structural engineers.

Regional frequency analysis of daily maximum rainfall in Haryana

MAUSAM ◽  
2021 ◽  
Vol 72 (4) ◽  
pp. 835-846
Author(s):  
MOHIT NAIN ◽  
B. K. HOODA
Keyword(s):  
Frequency Analysis ◽  
Return Period ◽  
Regional Distribution ◽  
Rain Gauge ◽  
Quantile Estimation ◽  
Regional Frequency Analysis ◽  
Daily Maximum ◽  
Maximum Rainfall ◽  
L Moments ◽  
Regional Frequency

This paper is sets-out for the regional frequency analysis of daily maximum rainfall from the 27 rain gauge stations in Haryana using L-moments. As the distribution of rainfall varies spatially in Haryana, the 27 rain gauge stations are grouped into three clusters namely, cluster C1, C2 and C3 using Ward’s clustering method and homogeneity of clusters was confirmed using L-moments-based Heterogeneity measure (H). Using goodness-of-fit measure (  ) and L-moment ratios diagram, suitable regional frequency distributions were selected among five candidate distributions; Generalized Logistic (GLO), Generalized Extreme Value (GEV),Generalized Normal (GNO), Generalized Pareto (GPA), and Pearson Type-3 (PE3) for each cluster. Results showed that PE3 and GNO were good fitted regional distribution for the cluster C1 and GEV, PE3 and GNO fitted for cluster C2 while for cluster C3; GLO and GEV were good fitted regional distribution. To select a robust distribution among good fitted distributions accuracy measures calculated using Monte Carlo simulations for each cluster. The simulation result showed that PE3 was the best choice for quantile estimation for cluster C1. For cluster C2, PE3 was the best choicefor a large return period and GEV was best for a small return period. For cluster C3, GEV was the most suitable distribution for quantile estimation. Using these robust distributions rainfall quantiles were estimated at each rain gauge station from 2 to 100 year return periods. These estimated rainfall quantiles may be rough guideline for planning and designing hydraulic structures by policy makers and structural engineers.

scholarly journals Regional Frequency Analysis of Maximum Monthly Rainfall in Haryana State of India Using L-Moments

2021 ◽  
Author(s):  
Mohit Nain ◽  
B. K. Hooda
Keyword(s):  
Return Period ◽  
Goodness Of Fit ◽  
Regional Distribution ◽  
Rain Gauge ◽  
Monthly Rainfall ◽  
Regional Frequency Analysis ◽  
Goodness Of Fit Test ◽  
Pearson Type ◽  
L Moments ◽  
Regional Frequency

The paper aims to select the appropriate regional frequency distribution for the maximum monthly rainfall and estimation of quantiles using L-moments for the 27 rain gauge stations in Haryana. These 27 rain gauge stations were grouped into three homogeneous regions (Region-1, Region-2, and Region-3) using Ward’s method of cluster analysis. To confirm the homogeneity of each region, L-moments based measure of heterogeneity was used. For each homogeneous region, a regional distribution was selected with the help of the L-moments ratio diagram and goodness-of-fit test. Results of the goodness-of-fit test and L-moments ratio diagram indicated that Generalized Logistic and Generalized Extreme Value distributions were best- fitted regional frequency distributions for the Region-1 and Region-2 respectively while for Region-3, Pearson Type-3) was best-fitted distribution. The quantiles for each region were calculated and the regional growth curves were developed. The accuracy measurements were determined using Monte Carlo simulations for the regional quantiles. Results of simulations showed that uncertainty in regional quantiles measured by Root Mean Square Error value and 90 percent error limits were small when the return period was low but uncertainty in quantiles increases as the return period increases.

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

Atmósfera ◽  
2015 ◽  
Vol 27 (4) ◽  
pp. 411-427
Author(s):  
HOSSEIN MALEKINEZHAD ◽  
ARASH ZARE-GARIZI
Keyword(s):  
Cluster Analysis ◽  
Frequency Analysis ◽  
Regional Frequency Analysis ◽  
Human Society ◽  
Maximum Rainfall ◽  
Design Rainfall ◽  
L Moments ◽  
Index Rainfall ◽  
Regional Frequency ◽  
Rainfall Estimates

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

scholarly journals Regional frequency analysis of extreme precipitation in northeastern Algeria

2018 ◽  
Vol 39 (1) ◽  
pp. 27-37
Author(s):  
Soufiane Dad ◽  
Tamara Benabdesselam
Keyword(s):  
Frequency Analysis ◽  
Regional Distribution ◽  
Regional Frequency Analysis ◽  
Study Region ◽  
Site Model ◽  
L Moments ◽  
Definition Of ◽  
Homogeneous Regions ◽  
Regional Frequency

AbstractThe aim of the study is to improve the quality of estimating of the annual maximum daily precipitations of the northeastern area of Algeria. The regional frequency analysis based on L-moments was used. The investigated area is represented by 58 measuring stations. The main stages of the study were the definition of homogeneous regions and the identification of the regional distribution. It has been defined that the study region is homogeneous in terms of L-moments ratios despite the climatic differences within the region. Among the different tested distributions; the generalised extreme value (GEV) distribution has been identified as the most appropriate regional distribution for modelling precipitation in the region. The growth curve, derived from the regional distribution, was established. Therefore, to estimate the different return period’s precipitation quantiles in a given site of the region, the mean precipitation of the site has to be multiplied by the corresponding regional quantile (growth factor). Comparison of the quantiles estimated from the regional and at-site frequency analysis showed that in the majority of stations (82.8%) at-site model underestimates the quantiles having high return periods.

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

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.

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

2017 ◽  
Vol 2017 ◽  
pp. 1-20 ◽  
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.

scholarly journals Drought Analysis in East Nusa Tenggara (Indonesia) Using Regional Frequency Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Heri Kuswanto ◽  
Anggi Wahyu Puspa ◽  
Imam Safawi Ahmad ◽  
Fausania Hibatullah
Keyword(s):  
Frequency Analysis ◽  
Return Period ◽  
Extreme Drought ◽  
Regional Frequency Analysis ◽  
Drought Risk ◽  
Drought Analysis ◽  
Daily Precipitation Data ◽  
Pearson Type ◽  
Normal Rainfall ◽  
Regional Frequency

Drought is a condition of a shortage of water that has an impact on economic activity. This research studies the severe drought area in Indonesia using Regional Frequency Analysis (RFA), based on daily precipitation data recorded at nine stations. The analysis reveals five homogeneous regions, based on discordancy and heterogeneity tests. Furthermore, the L-moment approach is applied to investigate the regional distribution and suggests that the Pearson type III distribution is the distribution that best fits the five regions. This distribution is also used to calculate the regional growth curve that is employed in the drought analysis. The drought return period analysis, for conditions of 40% of normal rainfall, concludes that the region containing the Fransiskus Xaverius, Gewayantana, and Mali stations has the highest drought risk, indicated by the fastest return period estimate of 2 years and 4 months. Moreover, the extreme drought analysis shows that two of the regions have the potential to experience the return of extreme drought, with less than 20% of normal rainfall, in less than four years.

scholarly journals Regional Frequency Analysis of Precipitation Extremes and Its Spatio-Temporal Patterns in the Hanjiang River Basin, China

Atmosphere ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 130 ◽  
Author(s):  
Wenlong Hao ◽  
Zhenchun Hao ◽  
Feifei Yuan ◽  
Qin Ju ◽  
Jie Hao
Keyword(s):  
River Basin ◽  
Frequency Analysis ◽  
Return Period ◽  
Extreme Events ◽  
Extreme Precipitation ◽  
Precipitation Extremes ◽  
Regional Frequency Analysis ◽  
Hanjiang River ◽  
Spatio Temporal ◽  
Regional Frequency

Extreme events such as rainstorms and floods are likely to increase in frequency due to the influence of global warming, which is expected to put considerable pressure on water resources. This paper presents a regional frequency analysis of precipitation extremes and its spatio-temporal pattern characteristics based on well-known index-flood L-moments methods and the application of advanced statistical tests and spatial analysis techniques. The results indicate the following conclusions. First, during the period between 1969 and 2015, the annual precipitation extremes at Fengjie station show a decreasing trend, but the Wuhan station shows an increasing trend, and the other 24 stations have no significant trend at a 5% confidence level. Secondly, the Hanjiang River Basin can be categorized into three homogenous regions by hierarchical clustering analysis with the consideration of topography and mean precipitation in these areas. The GEV, GNO, GPA and P III distributions fit better for most of the basin and MARE values range from 3.19% to 6.41% demonstrating that the best-fit distributions for each homogenous region is adequate in predicting the quantiles estimates. Thirdly, quantile estimates are reliable enough when the return period is less than 100 years, however estimates for a higher return period (e.g., 1000 years) become unreliable. Further, the uncertainty of quantiles estimations is growing with the growing return periods and the estimates based on R95P series have a smaller uncertainty to describe the extreme precipitation in the Hanjiang river basin (HJRB). Furthermore, In the HJRB, most of the extreme precipitation events (more than 90%) occur during the rainy season between May and October, and more than 30% of these extreme events concentrate in July, which is mainly impacted by the sub-tropical monsoon climate. Finally, precipitation extremes are mainly concentrated in the areas of Du River, South River and Daba Mountain in region I and Tianmen, Wuhan and Zhongxiang stations in region III, located in the upstream of Danjiangkou Reservoir and Jianghan Plain respectively. There areas provide sufficient climate conditions (e.g., humidity and precipitation) responsible for the occurring floods and will increase the risk of natural hazards to these areas.

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