Analysis of Magnitude and Frequency of Floods in the Damanganga Basin: Western India

2021 ◽  
Vol 5 (1) ◽  
pp. 1-11
Author(s):  
Vitthal Anwat ◽  
Pramodkumar Hire ◽  
Uttam Pawar ◽  
Rajendra Gunjal
Keyword(s):  
Probability Distribution ◽  
Probability Distributions ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Western India ◽  
Type I ◽  
Return Periods ◽  
Pearson Type ◽  
Kolmogorov Smirnov ◽  
Anderson Darling

Flood Frequency Analysis (FFA) method was introduced by Fuller in 1914 to understand the magnitude and frequency of floods. The present study is carried out using the two most widely accepted probability distributions for FFA in the world namely, Gumbel Extreme Value type I (GEVI) and Log Pearson type III (LP-III). The Kolmogorov-Smirnov (KS) and Anderson-Darling (AD) methods were used to select the most suitable probability distribution at sites in the Damanganga Basin. Moreover, discharges were estimated for various return periods using GEVI and LP-III. The recurrence interval of the largest peak flood on record (Qmax) is 107 years (at Nanipalsan) and 146 years (at Ozarkhed) as per LP-III. Flood Frequency Curves (FFC) specifies that LP-III is the best-fitted probability distribution for FFA of the Damanganga Basin. Therefore, estimated discharges and return periods by LP-III probability distribution are more reliable and can be used for designing hydraulic structures.

Exploring stage-based flood frequency analysis for flood inundation mapping

2021 ◽  
Author(s):  
Shobhit Singh ◽  
Somil Swarnkar ◽  
Rajiv Sinha
Keyword(s):  
Probability Distribution ◽  
River Basin ◽  
Frequency Analysis ◽  
Probability Distributions ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Flood Inundation ◽  
Return Periods ◽  
Theoretical Probability ◽  
Inundation Maps

<p>Floods are one of the worst natural hazards around the globe and around 40% of all losses worldwide due to natural hazard have been caused by floods since 1980s. In India, more than 40 million hectares of area are affected by floods annually which makes it one of the worst affected country in the world. In particular, the Ganga river basin in northern India which hosts nearly half a billion people, is one of the worst floods affected regions in the country. The Ghaghra river is one of the highest discharge-carrying tributaries of the Ganga river, which originates from High Himalaya. Despite severally affected by floods each year, flood frequencies of the Ghaghra river are poorly understood, making it one of the least studied river basins in the Ganga basin. It is important to note that, like several other rivers in India, the Ghaghra also has several hydrological stations where only stage data is available, and therefore traditional flood frequency analysis using discharge data becomes difficult. In this work, we have performed flood frequency analysis using both stage and discharge dataset at three different gauge stations in the Ghaghra river basin to compare the results using statistical methods. The L-moment analysis is applied to assess the probability distribution for the flood frequency analysis. Further, we have used the TanDEM-x 90m digital elevation model (DEM) to map the flood inundation regions. Our results suggest the Weibull is statistically significant distribution for the discharge dataset. However, stage above danger level (SADL) follows General Pareto (GP3) and Generalized Extreme Value (GEV) distributions. The quantile-quantile plot analysis suggests that the SADL probability distributions (GP3 and GEV) are closely following the theoretical probability distributions. However, the discharge distribution (Weibull) is showing a relatively weak corelation with the theoretical probability distribution. We further used the probability distribution to assess the SADL frequencies at 5-, 10-, 20-, 50- and 100-year return periods. The magnitudes of SADL at different return periods were then used to map the water inundation areas around different gauging stations. These inundation maps were cross-validated with the globally available flooding extent maps provided by Dartmouth flood observatory. Overall, this work exhibits a simple and novel technique to generate inundation maps around the gauging locations without using any sophisticated hydraulics models.</p>

scholarly journals Flood Flow Probability Distribution Model Selection on Niger/Benue River Basins in Nigeria

2021 ◽  
pp. 76-94
Author(s):  
Itolima Ologhadien
Keyword(s):  
Probability Distribution ◽  
Goodness Of Fit ◽  
River Basins ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Distribution Model ◽  
Distribution Models ◽  
Type Iii ◽  
Pearson Type ◽  
Best Fit

Flood frequency analysis is a crucial component of flood risk management which seeks to establish a quantile relationship between peak discharges and their exceedance (or non-exceedance) probabilities, for planning, design and management of infrastructure in river basins. This paper evaluates the performance of five probability distribution models using the method of moments for parameter estimation, with five GoF-tests and Q-Q plots for selection of best –fit- distribution. The probability distributions models employed are; Gumbel (EV1), 2-parameter lognormal (LN2), log Pearson type III (LP3), Pearson type III(PR3), and Generalised Extreme Value( GEV). The five statistical goodness – of – fit tests, namely; modified index of agreement (Dmod), relative root mean square error (RRMSE), Nash – Sutcliffe efficiency (NSE), Percent bias (PBIAS), ratio of RMSE and standard deviation of the measurement (RSR) were used to identify the most suitable distribution models. The study was conducted using annual maximum series of nine gauge stations in both Benue and Niger River Basins in Nigeria. The study reveals that GEV was the best – fit distribution in six gauging stations, LP3 was best – fit distribution in two gauging stations, and PR3 is best- fit distribution in one gauging station. This study has provided a significant contribution to knowledge in the choice of distribution models for predicting extreme hydrological events for design of water infrastructure in Nigeria. It is recommended that GEV, PR3 and LP3 should be considered in the development of regional flood frequency using the existing hydrological map of Nigeria.

scholarly journals Selecting the best probability distribution for at-site flood frequency analysis; a study of Torne River

2019 ◽  
Vol 1 (12) ◽  
Author(s):  
Mahmood Ul Hassan ◽  
Omar Hayat ◽  
Zahra Noreen
Keyword(s):  
Probability Distribution ◽  
Frequency Analysis ◽  
Goodness Of Fit ◽  
Direct Method ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Goodness Of Fit Tests ◽  
Pearson Type

AbstractAt-site flood frequency analysis is a direct method of estimation of flood frequency at a particular site. The appropriate selection of probability distribution and a parameter estimation method are important for at-site flood frequency analysis. Generalized extreme value, three-parameter log-normal, generalized logistic, Pearson type-III and Gumbel distributions have been considered to describe the annual maximum steam flow at five gauging sites of Torne River in Sweden. To estimate the parameters of distributions, maximum likelihood estimation and L-moments methods are used. The performance of these distributions is assessed based on goodness-of-fit tests and accuracy measures. At most sites, the best-fitted distributions are with LM estimation method. Finally, the most suitable distribution at each site is used to predict the maximum flood magnitude for different return periods.

Flood Frequency Analysis of the Mahi Basin by Using Log Pearson Type III Probability Distribution

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Uttam Pawar ◽  
Pramodkumar Hire
Keyword(s):  
Probability Distribution ◽  
Frequency Analysis ◽  
Return Period ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Series Data ◽  
Period Range ◽  
Type Iii ◽  
Pearson Type ◽  
Data Points

Flood frequency analysis is one of the techniques of examination of peak stream flow frequency and magnitude in the field of flood hydrology, flood geomorphology and hydraulic engineering. In the present study, Log Pearson Type III (LP-III) probability distribution has applied for flood series data of four sites on the Mahi River namely Mataji, Paderdi Badi, Wanakbori and Khanpur and of three sites on its tributaries such as Anas at Chakaliya, Som at Rangeli and Jakham at Dhariawad. The annual maximum series data for the record length of 26-51 years have been used for the present study. The time series plots of the data indicate that two largest ever recorded floods were observed in the year 1973 and 2006 on the Mahi River. The estimated discharges of 100 year return period range between 3676 m3/s and 47632 m3/s. The return period of the largest ever recorded flood on the Mahi River at Wankbori (40663 m3/s) is 127-yr. The recurrence interval of mean annual discharges (Qm) is between 2.73-yr and 3.95-yr, whereas, the return period of large floods (Qlf) range from 6.24-yr to 9.33-yr. The magnitude-frequency analysis curves represent the reliable estimates of the high floods. The fitted lines are fairly close to the most of the data points. Therefore, it can be reliably and conveniently used to read the recurrence intervals for a given magnitude and vice versa.

scholarly journals Distribuições de probabilidade para séries históricas mensais de pressão atmosférica no município de Mossoró-RN

2016 ◽  
Vol 11 (2) ◽  
pp. 135
Author(s):  
Janilson Pinheiro de Assis ◽  
Roberto Pequeno de Sousa ◽  
Paulo César Ferreira Linhares ◽  
Thiago Alves Pimenta ◽  
Elcimar Lopes da Silva
Keyword(s):  
Atmospheric Pressure ◽  
Probability Distributions ◽  
Probability Density Distribution ◽  
Chi Square ◽  
Pearson Type ◽  
Kolmogorov Smirnov ◽  
Anderson Darling ◽  
Log Normal ◽  
Von Mises ◽  
Cramer Von Mises

<p>Objetivou-se verificar o ajuste de 12 séries históricas de pressão atmosférica mensal (milibar) no período de 1970 a2007, em Mossoró, RN, à sete modelos de distribuição densidade de probabilidade Normal, Log-Normal, Beta, Gama, Log-Pearson (Tipo III), Gumbel e Weibull, através dos testes Kolmogorov-Smirnov, Qui-Quadrado, Cramer Von-Mises, Anderson Darling e Kuiper a 10 % de probabilidade e utilizando-se o Logaritmo da Máxima Verossimilhança. Verificou-se a superioridade do ajustamento da distribuição de probabilidade Normal, quando comparada com as outras seis distribuições. No geral, os critérios de ajuste concordaram com a aceitação da hipótese H<sub>0</sub>, no entanto, deve-se salientar que o teste de Kolmogorov-Smirnov apresenta um nível de aprovação de uma distribuição sob teste muito elevado, gerando insegurança aos critérios do teste, porém, como neste estudo os dados são aproximadamente simétricos, esse é o mais recomendado.</p><p align="center"><strong><em>Probability distributions for historic series of monthly atmospheric pressure </em></strong><strong><em>in city</em></strong><strong><em> of Mossoró-RN</em></strong></p><p><strong>Abstract</strong><strong>: </strong>The aim of this study was to determine the set of 12 time series of monthly atmospheric pressure (millibars) in the period 1970-2007, in Natal, RN, the seven models of the probability density distribution Normal, Log-Normal, Beta, Gamma, Log -Pearson (Type III), Gumbel and Weibull, through the Kolmogorov-Smirnov tests, Chi-Square, Cramer-von Mises, Anderson Darling and Kuiper 10 probability and using the logarithm of the maximum likelihood. It is the superiority of adjusting the normal probability distribution compared to the other six distributions. Overall, the fit criteria agreed with the acceptance of the hypothesis, however, it should be noted that the Kolmogorov-Smirnov test shows a level of approval of a distribution under test very high, which creates some uncertainty to the criteria of test, but in this study as the data are roughly symmetrical it is the most recommended.</p>

scholarly journals Rainfall models – a study over Gangtok

MAUSAM ◽  
2021 ◽  
Vol 61 (2) ◽  
pp. 225-228
Author(s):  
K. SEETHARAM
Keyword(s):  
Distribution Function ◽  
Probability Distribution ◽  
Uniform Distribution ◽  
Probability Distribution Function ◽  
Null Hypothesis ◽  
Probability Distributions ◽  
Data Sets ◽  
Type I ◽  
Anderson Darling ◽  
Pearsonian Type

In this paper, the Pearsonian system of curves were fitted to the monthly rainfalls from January to December, in addition to the seasonal as well as annual rainfalls totalling to 14 data sets of the period 1957-2005 with 49 years of duration for the station Gangtok to determine the probability distribution function of these data sets. The study indicated that the monthly rainfall of July and summer monsoon seasonal rainfall did not fit in to any of the Pearsonian system of curves, but the monthly rainfalls of other months and the annual rainfalls of Gangtok station indicated to fit into Pearsonian type-I distribution which in other words is an uniform distribution. Anderson-Darling test was applied to for null hypothesis. The test indicated the acceptance of null-hypothesis. The statistics of the data sets and their probability distributions are discussed in this paper.

scholarly journals Improved Mixed Distribution Model Considering Historical Extraordinary Floods under Changing Environment

Water ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 1016 ◽  
Author(s):  
Jianzhu Li ◽  
Yanchen Zheng ◽  
Yimin Wang ◽  
Ting Zhang ◽  
Ping Feng ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Distribution Model ◽  
Return Periods ◽  
Design Flood ◽  
Mixed Distribution ◽  
Pearson Type ◽  
The Difference ◽  
Pearson Type Iii Distribution

Historical extraordinary floods are an important factor in non-stationary flood frequency analysis and they may occur at any time, regardless of whether the environment is changing or not. Based on mixed distribution (MD) modeling, this paper proposed an improved mixed distribution (IMD) model to consider the discontinuity and non-stationarity of flood samples simultaneously, which adds historical extraordinary floods in both sub-series divided by a change point. As a case study, the annual maximum peak discharge and volume series of Ankang hydrological station, located in the upper Hanjiang River Basin of China, were selected to identify non-stationarity by using the variation diagnosis system. MD and IMD were used to fit the flood characteristic series and a genetic algorithm was employed to estimate the optimal parameters. Compared with the design flood values fitted by the stationary Pearson type-III distribution, the results computed by IMD decreased at low return periods and increased at high return periods, with the difference varying from −6.67% to 7.19%. The results highlighted that although the design flood values of IMD are slightly larger than those of MD with different return periods, IMD provided a better result than MD. IMD provides a new perspective for non-stationary flood frequency analysis.

scholarly journals Establishment of a Stochastic Model for Sustainable Economic Flood Management in Yewa Sub-Basin, Southwest Nigeria

2016 ◽  
Vol 2 (12) ◽  
pp. 646-655 ◽  
Author(s):  
O.A Agbede ◽  
Oluwatobi Aiyelokun
Keyword(s):  
Probability Distribution ◽  
Stochastic Model ◽  
Goodness Of Fit ◽  
Probability Distributions ◽  
Flood Management ◽  
Return Periods ◽  
Probability Models ◽  
Goodness Of Fit Test ◽  
Southwest Nigeria ◽  
Anderson Darling

Of all natural disasters, floods have been considered to have the greatest potential damage. The magnitude of economic damages and number of people affected by flooding have recently increased globally due to climate change. This study was based on the establishment of a stochastic model for reducing economic floods risk in Yewa sub-basin, by fitting maximum annual instantaneous discharge into four probability distributions. Daily discharge of River Yewa gauged at Ijaka-Oke was used to establish a rating curve for the sub-basin, while return periods of instantaneous peak floods were computed using the Hazen plotting position. Flood magnitudes were found to increase with return periods based on Hazen plotting position. In order to ascertain the most suitable probability distribution for predicting design floods, the performance evaluation of the models using root mean square error was employed. In addition, the four probability models were subjected to goodness of fit test besed on Anderson-Darling (A2) and Kolmogorov-Smirnov (KS). As a result of the diagnostics test the Weibul probability distribution was confirmed to fit well with the empirical data of the study area. The stochastic model  generated from the Weibul probability distribution, could be used to enhance sustainable development by reducing economic flood damages in the sub-basin.

scholarly journals At Site Flood Frequency Analysis of Baitarani River at Champua Watershed, Odisha

2019 ◽  
pp. 54-64
Author(s):  
Rebati Sinam
Keyword(s):  
Frequency Analysis ◽  
Return Period ◽  
Goodness Of Fit ◽  
Probability Distributions ◽  
Gumbel Distribution ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Floodplain Management ◽  
Chi Squared ◽  
Anderson Darling

For any development of hydraulic structures and dam modelling, flood frequency analysis is an effective tool to determine the appropriate measures and strategy. Flood frequency analysis has been conventionally used in hydraulic engineering and floodplain management. The present study is an attempt to estimate the expected flood using two probability distributions: Gumbel distribution and Log Pearson III distribution at Champua watershed, Upper Baitarani River Basin, Odisha. The analysis is based on annual maximum flood time series from 1991 to 2018 (28 years) obtained from Water Resources Information System at the Champua gauging station. Three Goodness of fit methods namely Kolmogorov Smirnov, Anderson Darling and Chi Squared tests are used to choose the better model. From the analysis, expected flood for return period 2, 10, 25, 50, 100 and 1000 years are calculated. Gumbel give an expected flood 521.72 cumecs while Log Pearson III give an expected flood of 493.17 cumecs for 2 years return period. It is observed that Gumbel estimated a higher values for all the said return period except for 1000 years where Log Pearson III predicted a much higher values. Goodness of test show inconsistent results. While Chi-squared test indicate Gumbel Method as the better model, the other two tests show that Log Pearson III is the better fitting model for the given dataset. Therefore, Log Pearson III is chosen as the best model. However, the results from both the distributions can be referred for storm management.

scholarly journals EVALUATION OF THE BEST-FIT PROBABILITY OF DISTRIBUTION AND RETURN PERIODS OF RIVER DISCHARGE PEAKS. CASE STUDY: AWETU RIVER, JIMMA, ETHIOPIA

2019 ◽  
Vol 4 (4) ◽  
pp. 361-368 ◽  
Author(s):  
Tolera Abdisa Feyissa ◽  
Nasir Gebi Tukura
Keyword(s):  
Distribution Function ◽  
Probability Distribution ◽  
Peak Discharge ◽  
Environmental Damage ◽  
Return Periods ◽  
Chi Squared ◽  
Kolmogorov Smirnov ◽  
Anderson Darling ◽  
Log Normal ◽  
Best Fit

The identification of the best distribution function is essential to estimate a river peak discharge or magnitude of river floods for management of watershed and ecosystems. However, inadequate estimation of the river peak discharge and floods magnitude may decrease the efficiency of water-resources management, resulting in soil erosion, landslides, environmental damage and ecosystem degradation. To overcome this problem in hydrology, different methods have been employed, applying a probability distribution.In this study to determine the suitable probability of distribution for estimating the annual discharge series with different return periods, the annual mean and peak discharges of the Awetu River (Jimma, Ethiopia) over a 24 years’ time period have been collected and used. After the homogeneity and consistency test, data were analyzed to predict extreme values and were applied in seven different probability distribution functions by using L-moment and easy fit methods. Then, three goodness of fit tests, Anderson-Darling (AD), Kolmogorov-Smirnov (KS), and Chi-Squared (x2) tests, were used to select the best probability distribution function for the study area. The obtained results indicate that, Log-normal distribution function is the best-fit distribution to estimate the peak discharge recurrence of the Awetu River. The 5-year, 10-year, 25-year, 50-year, 100-year and 1000-year return periods of discharge were calculated for this river. The results of this study are useful for the development of more accurate models of flooding inundation and hazard analysis. AVALIAÇÃO DA MELHOR PROBABILIDADE DE AJUSTE DE DISTRIBUIÇÃO E PERÍODOS DE RETORNO DOS PICOS DE DESCARGA FLUVIAL. ESTUDO DE CASO: AWETU RIVER, JIMMA, ETIÓPIAResumoAvaliação da melhor função de probabilidade de distribuição e de períodos de retorno de picos de descarga de rio. Estudo de caso: Rio Awetu, Jimma, Etiópia. A identificação da melhor função de distribuição é essencial para estimar um pico de descarga de rios ou a magnitude das inundações de bacias hidrográficas e ecossistemas, tendo em vista a gestão dos sistemas hídricos e dos ecossistemas. Entretanto, uma estimativa inadequada da magnitude do pico de vazão e inundações do rio pode diminuir a eficiência do gerenciamento dos recursos hídricos, resultando em erosão do solo, deslizamentos de terra, danos ambientais e degradação do ecossistema. Para superar esse problema na hidrologia, diferentes métodos foram empregados, aplicando funções de probabilidade de distribuição e retorno.Neste estudo, para determinar a probabilidade adequada de distribuição e para estimar séries de descarga anuais com diferentes períodos de retorno, foram usados dados de médias anuais de picos de descarga do Rio Awetu (Jimma, Etiópia) durante um período de 24 anos. Após o teste de homogeneidade e consistência, os dados foram analisados para prever valores extremos e foram aplicados a sete funções diferentes de probabilidade de distribuição, usando o momento L e métodos de ajuste fácil. Em seguida foram utilizados, três testes de qualidade de ajuste, Anderson-Darling (AD), Kolmogorov-Smirnov (KS), and Chi-Squared (x2), para selecionar a melhor função de probabilidade de distribuição para a área de estudo. Os resultados obtidos indicam que, a função de distribuição log-normal é a que mais se adequa para estimar a recorrência de picos de descarga do Rio Awetu. Os períodos de retorno de descarga de 5 anos, 10 anos, 25 anos, 50 anos, 100 anos e 1000 anos foram calculados para este rio. Os resultados deste estudo são úteis para o desenvolvimento de modelos mais precisos de inundação e análise de risco.Palavras-chave: Descarga de Rio. Qualidade de ajuste. Log Pearson Tipo III. Distribuição de probabilidade. 

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