Abstract
Abstract Estimating stream flow has a substantial financial influence, because this can be of assistance in water resources management and provides safety from scarcity of water and conceivable flood destruction. Four common statistical methods, namely, Normal, Gumbel max, Log-Pearson III (LP III), and Gen. extreme value method are employed for 10, 20, 30, 35, 40, 50, 60, 70, 75, 100, 150 years to forecast stream flow. Monthly flow data from four stations on Mahanadi River, in Eastern Central India, namely, Rampur, Sundargarh, Jondhra, and Basantpur, are used in the study. Results show that Gumbel max gives better flow discharge value than the Normal, LP III, and Gen. extreme value methods for all four gauge stations. Estimated flood values for Rampur, Sundargarh, Jondhra, and Basantpur stations are 372.361 m3/sec, 530.415 m3/sec, 2,133.888 m3/sec, and 3,836.22 m3/sec, respectively, considering Gumbel max. Goodness-of-fit tests for four statistical distribution techniques applied in the present study are also evaluated using Kolmogorov–Smirov, Anderson–Darling, Chi-squared tests at critical value 0.05 for the four proposed gauge stations. Goodness-of-fit test results show that Gen. extreme value gives best results at Rampur, Sundergarh, and Jondhra gauge stations followed by LP III, whereas LP III is the best fit for Basantpur, followed by Gen. extreme value.
Highlights
Consistent and precise stream flow forecasting is needed for numerous issues such as water resources planning, strategy improvement, maneuver and upkeep events
Evensen (1994) discussed a novel chronological data integration technique based on predicting error statistics utilizing Monte Carlo procedures which served as a superior alternative to solve customary and computationally enormous challenging estimated error covariance equations utilized in extended Kalman filter
In most of the cases, Gumbel max gives the peak flood discharge and normal distribution contributes to the least discharge
Summary
Consistent and precise stream flow forecasting is needed for numerous issues such as water resources planning, strategy improvement, maneuver and upkeep events. Bezak et al (2014) explored the influence of threshold value in the peaksover-threshold method on FFA results, compared different statistical distribution functions and evaluated three parameter estimation techniques. Reis & Stedinger (2005) explored Bayesian Markov chain Monte Carlo techniques to evaluate subsequent circulation of flood magnitude, flood menace, and constraints of Log-normal and LP III distributions. Pawar & Hire (2018) applied LP III distribution for flood data of four locations on the Mahi River and studied peak stream flow frequency and magnitude in the field of flood hydrology. Lima et al (2016) estimated local and regional GEV distribution for flood frequency analysis of Rio Doce basin, Brazil in a multilevel, hierarchical Bayesian framework, to explicitly model and reduce uncertainties. Reis & Stedinger (2005) explored Bayesian Markov chain Monte Carlo techniques to evaluate subsequent circulation of flood magnitude, flood menace, and constraints of Log-normal and LP III distributions. Subyani (2011) quantified hydro-geological distinctiveness and probability of flood occurrence of several main valleys in western Saudi Arabia by applying GEV and LP III distributions to peak daily precipitation data. Sraj et al (2015) examined 58 flood occurrences at Litija station on Sava River, Slovenia applying different bivariate copulas and contrasted them utilizing various arithmetic, graphic, and higher extremity reliance experiments. Merz & Thieken (2005) explored the difference between natural and epistemic uncertainty in FFA. Ouarda et al (2001) projected an apparent theoretical framework for application of canonical correlations in RFFA using data from 106 stations in Ontario province, Canada. Micevski et al (2015) presented a substitute RFFA technique that is predominantly valuable when adequately harmonized areas cannot be recognized on the basis of region of influence. Sahoo et al (2020) studied bivariate low flow frequency analysis of Mahanadi basin, which has major deviations in hydrological performance from upstream to downstream, for two main low flow characteristics. Parhi (2018) estimated peak floods at Mahanadi River at the Hirakud dam and Naraj of up to 100 years’ recurrence interval utilizing HEC-RAS and Gumbel’s distribution. Pawar & Hire (2018) applied LP III distribution for flood data of four locations on the Mahi River and studied peak stream flow frequency and magnitude in the field of flood hydrology. Lima et al (2016) estimated local and regional GEV distribution for flood frequency analysis of Rio Doce basin, Brazil in a multilevel, hierarchical Bayesian framework, to explicitly model and reduce uncertainties. Bhat et al (2019) carried out flood frequency analysis of the River Jhelum employing Gumbel and LPIII distributions for simulating future flood discharge scenarios from three positions. Tanaka et al (2017) examined the impact of river overflow and dam operation of upstream areas on downstream extreme flood frequencies at Yodo River basin combining a flood-inundation model of upstream Kyoto City area with a rainfall-based flood frequency model and accounting for the probability of spatial and temporal rainfall pattern over the basin
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