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Regional flood frequency analysis has been carried out for estimating peak discharge at regional level over the Kerala State, India, along with at-site flood frequency analysis. For the study, the annual peak discharges of 43 gauging stations having length of data from 14 to 47 years spread over the Kerala State were used. Using L moments and L mom...
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... There can be different probability distributions with different parameter estimation methods suitable for modeling maximum water flow data from different gauging sites. In the literature concerning at-site flood frequency analysis, different probability distributions like Generalized Extreme Value (GEV), GEV type-1, GEV type-2, normal, generalized normal, two-parameter lognormal, three-parameter lognormal, two-parameter gamma, three-parameter gamma, Pearson type-III, Gumbel, reverse Gumbel, exponential, generalized logistic, four-parameter Wakeby, five-parameter Wakeby, and generalized Pareto have been applied for flood frequency analysis of different rivers in different countries [1,5,6,[12][13][14][15][16][17][18]. A brief review can be seen in some studies [19][20][21]. ...
... Just like the method of moments, parameter estimation through the L-moments approach is done by solving a system of simultaneous equations obtained by comparing theoretical and corresponding sample L-moments. The LM method has gained much attention recently due to its computational simplicity [5,6,12,35]. ...
The article deals with at-site flood frequency analysis for different gauging stations of the Chenab River in Pakistan. The study aimed at recommending the most suitable probability distribution and efficient method of parameter estimation for each gauging site. Generalized extreme value, generalized logistic, Gumbel, generalized Pareto, and reverse Gumbel probability models are fitted to the annual peak flow/discharge. For each gauging site, the parameters of these distributions are estimated through L-moments, maximum likelihood, least squares, weighted least squares, and relative least squares methods. For each site, the probability models with a particular estimation method are ranked on the basis of goodness-of-tests and accuracy measures, and then the most suitable pair of model and estimation method is identified through a total rank. The results indicate that the generalized Pareto distribution is the best fit for Marala, Khanki, Qadirabad, and Punjnad, while the generalized extreme value distribution is the most suited for the Trimmu gauging site. As far as the estimation method is concerned, least squares and weighted least squares methods are more accurate for most of the gauging sites. Finally, for each gauging site, the best-suited probability model is used to estimate the annual peak flow and to construct associated confidence intervals for different return years.
... GoF tests such as Mean Absolute Deviation Index (MADI), Probability Plot Correlation Coefficient (PPCC), A-D test, and L-moment and TL moment ratio diagram methods were used by [16]. Darissa et al, [17] employed the Chi-Square test along with a ranking method using statistical indicators and the L-moment ratio diagram for GoF tests in their analysis. This approach was conducted to compare the at-site and regional data using L-moments. ...
... 130 -30M 120 +12M 110 -M 100(17) ...
Kalu River, sustains Sri Lanka's Western province and experiences an annual precipitation of 4000mm to 5000mm. Despite the region's susceptibility to frequent flooding, no previous studies had estimated flood return periods at Ratnapura. This research focused on Flood Frequency Analysis (FFA) in the Ratnapura region of Sri Lanka to estimate the return period of floods, which is crucial for flood management and infrastructure planning. Annual Peak Discharge (APD) data from the Ratnapura station over 44 years were fitted to five probability distributions: Generalized Extreme Value (GEV), Gumbel (GUM), Pearson Type 3 (PT3), Log Normal 3 (LN3), and Generalized Logistic (GLO). Stationarity was assessed using the Mann-Kendall and Levene's tests, indicating no trends in the series. Serial independence was analyzed using Turning Point and Anderson's autocorrelation tests, confirming randomness. L-moments, a parameter estimation method, were utilized, and the GEV distribution was the best fit from the L moment ratio diagram. The study estimated a major flood occurrence in Ratnapura approximately once every three years. However, accurate FFA is vital to minimize economic and social impact due to floods and to design resilient infrastructure. Further, this study shows that the GEV distribution is a potential model for regional FFA of Sri Lankan rivers.
... [22] Over time, various researchers have advanced the understanding of flood-generating mechanisms and related factors for RFFA in India, including techniques for delineating homogeneous regions [23][24][25][26] and adding more topographical features. [27,28] At present, the Central Water Commission (CWC) of India recommends using these regionally developed formulas, which correlate the flood quantiles with watershed attributes such as drainage area and slope of the watershed, length of the mainstream, length of a stream from the centroid of a watershed, and average areal rainfall. However, the watersheds in India exhibit diverse characteristics, including varying land cover, soil types, topography, climatic conditions, and anthropogenic influences, which contribute to the complexity of flood behavior and make it challenging to develop a regional flood quantile estimation model. ...
Estimates of the flood quantile for ungauged watersheds are crucial for water resources management but challenging due to the nonlinear complex hydrological system. For ungauged watersheds, estimating flood quantiles relies on various interdependent physiometeorological variables, many of which are not adequately considered in regional flood frequency analysis (RFFA). In this study, we utilized the random forest (RF) and support vector regression (SVR) algorithms, which can learn the nonlinear relationship between the physiometeorological variables and flood quantiles for RFFA. Thirteen physiometeorological variables that were not collectively employed before were used to estimate the 10‐year, 50‐year, and 100‐year return period flood quantiles (Q10, Q50, and Q100), respectively, for 39 watersheds spread across India. The RF and SVR models were trained on 29 (75%) watersheds to estimate individual flood quantiles and were subsequently tested on the remaining ten (25%) ungauged watersheds. The R ² achieved by RF is 0.862, 0.813, and 0.845, and SVR is 0.807, 0.793, and 0.789 for Q10, Q50, and Q100, respectively. Overall, the results indicate that RF can effectively learn the nonlinear relationships, while SVR with a linear kernel requires further improvement to estimate reliable flood quantiles. The study demonstrates that machine learning algorithms, with appropriate physiometeorological input datasets, can be used to estimate flood quantiles even in the sparse data region.
... Only few studies have been carried out lingering around the FFA of Kerala. Drissia et al. [7] analysed daily and flood discharge of 43 gauging stations in Kerala's west flowing rivers (WFRs) and constructed regional frequency curves incorporating watershed characteristics to estimate flood magnitude using L-moments technique [8]. To the best of our knowledge, there have been no studies undertaken in Kerala that utilise the idea of non-stationarity to create FF curves that account for a warming planet. ...
... The availability of the recorded peak discharge data is critical in flood frequency research, yet it is often limited or unavailable. As a result, estimating the peak flood for various return periods is challenging [13]. Historical reports indicate how the Kuantan River Basin (KRB) suffered numerous serious flood events in 2001, 2013, and 2016. ...
... The PGII distribution has a broad use in the analysis of extreme events such as precipitation frequency analysis [10][11][12][13][14][15], low flow frequency analysis [16] and in flood frequency analysis [4,5,[17][18][19][20]. ...
This article analyzes six probability distributions from the Generalized Pareto family, with three, four and five parameters, with the main purpose of identifying other distributions from this family with applicability in flood frequency analysis compared to the distribution already used in the literature from this family such as Generalized Pareto Type II and Wakeby. This analysis is part of a larger and more complex research carried out in the Faculty of Hydrotechnics regarding the elaboration of a norm for flood frequency analysis using the linear moments method. In Romania, the standard method of parameter estimation is the method of ordinary moments, thus the transition from this method to the method of linear moments is desired. All the necessary elements for the distribution use are presented, such as the probability density functions, the complementary cumulative distribution functions, the quantile functions, and the exact and approximate relations for estimating parameters, for both methods of parameter estimation. All these elements are necessary for a proper transition between the two methods, especially since the use of the method of ordinary moments is done by choosing the skewness of the observed data depending on the origin of the maximum flows. A flood frequency analysis case study, using annual maximum and annual exceedance series, was carried out for the Prigor River to numerically present the analyzed distributions. The performance of this distribution is evaluated using a linear moments diagram.
... The accuracy of frequency analysis determines the accuracy of hydrological event prediction. Hydrologists have worked to improve the accuracy of frequency analysis, constantly improving existing methods and introducing new ones (Razmkhah et al. 2022;Drissia et al. 2019;Tsakiris et al. 2015). ...
Trimmed L-moments (TL-moments) can reduce the undesirable effects of small sample events when estimating large return period events. This study proposes a new method to investigate the TL-moments method for the Pearson type III (P-III) distribution and derives new formulas for the TL-moments of the P-III distribution. The formulas for the TL-moments of the P-III distribution derived by Mat Jan and Shabri (Theor Appl Climatol 127(1–2):213–227, 2015) are also corrected. From the simulation results, the TL-moments method of the P-III distribution proposed in this paper is almost the same as the TL-moments method proposed by Mat Jan and Shabri (Theor Appl Climatol 127(1–2):213–227, 2015), and it can show good parameter estimation performance when the sample size is small and Cs>2.0Cv. The annual maximum streamflow data in northern Shaanxi, China is used as a case study. The results show that the TL-moments (2, 0) method is the most suitable method for the Zaoyuan and Liujiahe stations and the Huangling station is best fitted with the TL-moments (3, 0) method.
... There are two methods of flood frequency estimation-At site estimation and Regional. At the site frequency analysis has been done by Abdo et al. (2006), Rahman et al. (2014), Drissia et al. (2019), Leščešen and Dolinaj (2019), Ganamala and Sundar Kumar (2017), Kamal et al. (2017), Cassalho et al. (2018). Rahman et al. (2013) Karim and Chowdhury (1995). ...
Baitarani River is one of the important rivers of India falling in the state of Odisha and Jharkhand. It has been affected by the problem of floods and inundation annually. At site Flood Frequency Analysis using three probability distribution models—Log Pearson III, Generalized Extreme Value Distribution and Gumbel Distribution- have been analyzed to find the most suitable and reliable model for the river based on two station data (Anandapur and Champua) using an annual maximum series of 43 years (1976–2018) and 28 years (1991–2018) respectively. Three Goodness of fit (GOF) tests—Anderson–Darling, Kolmogorov–Smirnov and Chi-Square tests are executed to find the best fit model at 95% significance level. From the study, Generalized Extreme Value distribution is the best distribution model for the sub-basin. The significance of the study lies in the fact that finding a suitable distribution model is a pre-requisite for developing an accurate hydraulic structure and efficient flood management strategy. Nevertheless, the study proposed further studies using various other multiparametric models and new advanced techniques for finding the most accurate distribution model for the river sub-basin.KeywordsFlood frequency analysisBaitarani RiverGumbel distributionGeneralized extreme valueLog Pearson III
... As an alternative approach, the POT method is typically applied to short-term observation data extracted above a given sample threshold. Both methods have been widely used in the frequency analysis of extreme events [8,9,10,11,12,13,14,15]. To fit extreme-value samples, an appropriate probability distribution is applied. ...
... The most effective distribution for different sites of interest or extreme events varies. Drissia et al. [10] used five probability distributions to analyze the flood frequency of 43 gauging stations in Kerala State, India, and found that GP was the best for 27 stations. Garrote et al. [17] chose the GEV to estimate flood hazards in Zamora City (Spain). ...
The accurate prediction of extreme events based on measured data is an important task because it can facilitate infrastructure reliability design, risk assessment, and disaster mitigation. However, owing to limited sample size, considerable uncertainties are introduced to extreme value predictions with respect to different probabilities of exceedance. This paper proposes an uncertainty quantification analysis algorithm for extreme value statistics using the Monte Carlo technique. The study is focused on three widely used probability distributions fitted by the maximum likelihood method: generalized extreme value, generalized Pareto, and Pearson Type III. Three simplified models of the standard error for extreme value quantiles are explicitly developed as a function of the sample size, return period, and distribution parameters. These models eliminate the constraints resulting from the assumption of normality as well as provide more straightforward and accurate expressions that are convenient for engineering applications. The results indicate that the standard error increases with the return period as well as the shape and scale parameters of the distribution; however, it decreases with the sample size. The developed simplified models were applied to extreme event analyses of wind speed and precipitation at several typical sites. To clarify the practicability and rationality of the method, a wind hazard map for non-typhoon winds was developed for mainland China.
... However, this and other studies into the optimal choice of distribution in regions of India have recommended the use of other distributions. The acceptability of the GPA distribution for AMAX flows is variously supported [39,40] and contradicted [41][42][43] by previous studies on much smaller datasets of 4 to 18 Indian stations. ...
... In Kerala, south-west India, the Chi-square test, ranking of statistical indicators and the L-moment ratio diagram highlighted the Generalized Pareto (GPA) and Generalized Logistic (GLO) distributions for at-site analyses [39]. In the Tel basin in the Mahandi river system in east India, Guru and Jha [40] considered four stations and, via Kolmogorov-Smirnov tests, found the GPA to fit best to annual maxima data and the Generalized Log-Normal (GLN) to fit best to peak-over-threshold data. ...
Monsoon-related extreme flood events are experienced regularly across India, bringing costly damage, disruption and death to local communities. This study provides a route towards estimating the likely magnitude of extreme floods (e.g., the 1-in-100-year flood) at locations without gauged data, helping engineers to design resilient structures. Gridded rainfall and evapotranspiration estimates were used with a continuous simulation hydrological model to estimate annual maximum flow rates at nine locations corresponding with river flow gauging stations in the Wainganga river basin, a data-sparse region of India. Hosking–Wallis distribution tests were performed to identify the most appropriate distribution to model the annual maxima series, selecting the Generalized Pareto and Pearson Type III distributions. The L-moments and flood frequency curves of the modeled annual maxima were compared to gauged values. The Probability Distributed Model (PDM), properly calibrated to capture the dynamics of peak flows, was shown to be effective in approximating the Generalized Pareto distribution for annual maxima, and may be useful in modeling peak flows in areas with sparse data. Confidence in the model structure, parameterization, input data and catchment representation build confidence in the modeled flood estimates; this is particularly relevant if the method is applied in a location where no gauged flows exist for verification.
