scholarly journals Regional Frequency Analysis of Rainfall Using L-Moment Method as A Design Rainfall Prediction

2020 ◽  
Vol 1000 (1000) ◽  
Author(s):  
Devita Mayasari
Keyword(s):  
Probability Distribution ◽  
Frequency Analysis ◽  
Moment Method ◽  
Regional Frequency Analysis ◽  
Test Model ◽  
Discharge Data ◽  
Maximum Rainfall ◽  
Generalized Logistic Distribution ◽  
Design Rainfall ◽  
The Moment

Frequency analysis is a method for predicting the probability of future hydrological events based on historical data. Frequency analysis of rain data and discharge data is generally carried out using the moment method, but the moment method has a large bias, variant, and slope so that it has the potential to produce inaccurate hydrological design magnitudes. The L-moment method is a linear combination of Probability Weighted Moment which processes data in a concise and linear manner. This research was conducted that L-moment method will obtain a regional probability distribution and design rainfall which can be used as a basis for calculating hydrological planning in anticipation of disasters. The location of the study in Mount Merapi area was chosen in order to more accurately predict the maximum rainfall that could cause cold lava in the area to reduce the risk of loss to the people living around Mount Merapi. The results showed that the entire rainfall stations homogeneous and no data was released. The L-moment regional ratio results τ2R  = 0.203, τ3R = 0.166, dan τ4R  = 0.169. The homogeneity and heterogeneity tests show that all rainfall stations are uniform or homogeneous. No data were released from the discordance test results. Growth factor value increases in each design rainfall return periods. The regional probability distribution that is suitable for the research area is Generalized Logistic distribution with design rainfall equation has been formulated. Test model showed the minimum RBias = 0.45%, maximum RBias = 41.583%, minimum RRSME = 0.45%, and maximum RRSME = 71.01%. The stability of L-moment method showed by model test minimum error = 1.64% and maximum error = 16.60%.

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 Flood Frequency Analysis at the Downstream of Sg. Perak River Basin using Annual Maximum Flow Discharge Data

2018 ◽  
Vol 7 (4.35) ◽  
pp. 709 ◽  
Author(s):  
Munir Snu ◽  
Sidek L.M ◽  
Haron Sh ◽  
Noh Ns.M ◽  
Basri H ◽  
...  
Keyword(s):  
Probability Distribution ◽  
River Basin ◽  
Frequency Analysis ◽  
Return Period ◽  
Maximum Flow ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Annual Maximum ◽  
Discharge Data ◽  
Flow Discharge

The recent flood event occurred in 2014 had caused disaster in Perak and Sungai Perak is the main river of Perak which is a major natural drainage system within the state. The aim of this paper is to determine the expected discharge to return period downstream for Sg. Perak River Basin in Perak by using annual maximum flow data. Flood frequency analysis is a technique to assume the flow values corresponding to specific return periods or probabilities along the river at a different site. The method involves the observed annual maximum flow discharge data to calculate statistical information such as standard deviations, mean, sum, skewness and recurrence intervals. The flood frequency analysis for Sg. Perak River Basin was used Log Pearson Type-III probability distribution method. The annual maximum peak flow series data varying over period 1961 to 2016. The probability distribution function was applied to return periods (T) where T values are 2years, 5years, 10years, 25years, 50years, and 100years generally used in flood forecasting. Flood frequency curves are plotted after the choosing the best fits probability distribution for annual peak maximum data. The results for flood frequency analysis shows that Sg. Perak at Jambatan Iskandar much higher inflow discharge  which is 3714.45m3/s at the 100years return period compare to Sg. Plus at Kg Lintang and Sg. Kinta at Weir G. With this, the 100years peak flow at Sg Perak river mouth is estimated to be in the range of 4,000 m3/s. Overall, the analysis relates the expected flow discharge to return period for all tributaries of Sg. Perak River Basin.

scholarly journals Hierarchical Bayesian Model for Assessing Extreme Daily Rainfall Through Combining Climatic and Topographic Information

2021 ◽  
Vol 21 (4) ◽  
pp. 1-12
Author(s):  
Jeonghoon Lee ◽  
Jeongeun Won ◽  
Jeonghyeon Choi ◽  
Sangda Kim
Keyword(s):  
Frequency Analysis ◽  
Bayesian Model ◽  
Daily Rainfall ◽  
Regional Frequency Analysis ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Model ◽  
Model Framework ◽  
Generalized Logistic Distribution ◽  
Topographic Information ◽  
Regional Frequency

Frequency analysis of the annual maximum rainfall time series is essential for designing infrastructures to provide protection against local floods and related events. However, the results of the frequency analysis obtained are ambiguous. In this study, we aimed to develop a spatial hierarchical Bayesian model framework through combining the climatic and topographic information. To confirm the applicability of the proposed method, the results of at-site frequency analysis and regional frequency analysis using the index flood method were compared in the Busan-Ulsan-Gyeongnam region. Furthermore, a hierarchical Bayesian model was developed, in which the parameters of the generalized logistic distribution comprised relatively simple covariate relationships upon considering the possibility of expansion into various probability distributions and more complex covariate structures. The uncertainty of this model was analyzed using the coefficient of variation of rainfall quantile ensemble. The results confirmed that the regional frequency analysis using the hierarchical Bayesian model combined with the climatic and topographic information could provide an accurate estimate of extreme daily rainfall with relatively good agreement with the estimate at a specific site, but is a more reliable approach.

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.

Multivariate frequency analysis of urban rainfall characteristics using three-dimensional copulas

2018 ◽  
Vol 2017 (1) ◽  
pp. 206-218 ◽  
Author(s):  
Chenglin Liu ◽  
Yuwen Zhou ◽  
Jun Sui ◽  
Chuanhao Wu
Keyword(s):  
Probability Distribution ◽  
Frequency Analysis ◽  
Three Dimensional ◽  
Rainfall Data ◽  
Rainfall Characteristics ◽  
Design Rainfall ◽  
Rainfall Depth ◽  
Multivariate Frequency Analysis ◽  
Continuous Rainfall

Abstract Urban runoff is a major cause of urban flooding and is difficult to monitor in the long term. In contrast, long term continuous rainfall data are generally available for any given region. As a result, it has become customary to use design rainfall depth as a proxy for runoff in urban hydrological analyses, with an assumption of the same frequency for runoff and rainfall. However, this approach has lack of overall coordination and cannot fully reflect the variability of rainfall characteristics. To address this issue, this study presents a three-dimensional copula-based multivariate frequency analysis of rainfall characteristics based on a long term (1961–2012) rainfall data from Guangzhou, China. Firstly, continuous rainfall data were divided into individual rainfall events using the rainfall intensity method. Then the characteristic variables of rainfall (design rainfall depth, DRD; total rainfall depth, TRD; peak rainfall depth, PRD) were sampled using the annual maximum method. Finally, a copula method was used to develop the multivariate joint probability distribution and the conditional probability distribution of rainfall characteristics. The results showed that the copula-based method is easy to implement and can better reflect urban rainstorm characteristics. It can serve a scientific reference for urban flood control and drainage planning.

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.

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