Estimation of flood frequency using statistical method: Mahanadi River basin, India

Consistent and precise stream flow forecasting is needed for numerous issues such as water resources planning, strategy improvement, maneuver and upkeep events. In water management, forecasting high-quality stream flow and effective usage of this estimate gives substantial financial and communal assistance. For the hydrologic constituent, there is the requirement of interim as well as enduring events of stream flow forecasting for optimizing systems or for planning future growth or drop. Interim forecasting denotes hourly or day-to-day forecasting, which is vital for caution against flood and safety, and enduring forecasting is on the basis of monthly, seasonal or annual timescales which is very beneficial in reservoir processes and irrigation administration choices like distributing water to consumers downstream, arranging discharges, famine extenuation and handling river agreements or applying compacted acquiescence.

Masmoudi & Habaieb (1993) developed seven statistical channeling models, which were used on the Medjerdah River (Tunisia) to forecast dangerous flood occurrences. Model performance is described by statistical measures of accuracy, ultimate fault, and ultimate interruption among the measured and predicted flow with their alterations. 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. Bartholmes & Todini (2005) studied the possibility of extending flood predicting lag times equal to 10 days by engaging an amalgamation of innovative climatological and hydrological models and presented outcomes of the joined approach among a numerical weather forecast system and rainfall-runoff model. Griffis & Stedinger (2007) explored features of LP III distribution in real and log space. Assessments with outlines of US flood data revealed that LP III distribution offers a sensible model for yearly flood sequence distribution from unfettered catchments for log space skews. Moreover, for LP III distribution relations of L-moment ratio were established so as to compare them to overall statistics of a province. Rowinski et al. (2002) discussed two probability density functions, prevalent in hydrological studies, i.e., Log-Gumbel and Log-Logistic, with regard to use of the functions to hydrological numbers and problems ensuing from their mathematical properties. The maximum likelihood method promises merging of the estimators away from the area of reality of the two L-moments. Rath et al. (2018) employed the autoregressive integrated moving average (ARIMA) model to predict flow discharge at Mahanadi River basin. Helsel & Hirsch (1992) discussed probabilistic approaches usually accomplished in hydrology. Gumbel max value and LP III distribution are considered to be the best prevalent probabilistic models related to solving water resources problems. Kamal et al. (2017) applied statistical distribution on discharge data for two locations and discovered that Log-normal is applicable for Haridwar and Gumbel EV1 for Garhmukteshwar. Subsequent to finding an appropriate distribution for a region, the distribution helps in predicting discharge for a certain return period. Brandimarte & Di Baldassarre (2012) proposed another method on the basis of applicability of uncertain flood profile to estimate uncertainty in hydraulic modeling and FFA, where the major considerable uncertainty sources are clearly scrutinized. Ewemoje & Ewemooje (2011) investigated Normal, Lognormal, and LP III distributions to model at-site annual peak flood flow in Ogun-Oshun River, Nigeria. Chen et al. (2012) analyzed the risk of flooding resulting from the occurrence of flood, taking into consideration flood enormity and time of incidence applying LP III and mixed von Mises distribution. Mukherjee (2013) developed a mathematical model regarding peak flood discharge and return period utilizing GEV. Bezak et al. (2014) explored the influence of threshold value in the peaks-over-threshold method on FFA results, compared different statistical distribution functions and evaluated three parameter estimation techniques. Haddad & Rahman (2011a) investigated the usability of the quantile regression method as a feasible regional FFA technique for New South Wales, Australia. Haddad & Rahman (2011b) examined flood data from Tasmania, Australia considering an assortment of models' criteria: Akaike information criterion (AIC), AIC-second order variant, Bayesian IC, and a customized ADC. Results obtained by simulating the Monte Carlo model shows that ADC is better at recognizing parent allocation fittingly. Grimaldi & Serinaldi (2006) modeled trivariate joint distribution of flood peak, volume, and duration using a class of copulas called asymmetric Archimedean copulas. Hirabayashi et al. (2013) presented universal flood hazard for this century on the basis of results obtained from climate models and employed a condition of skill for the universal stream steering model with a barrage system for computing river discharge and flood area. Haddad & Rahman (2012) proposed a model utilizing Bayesian generalized least squares regression in an authoritative area structure for RFFA of ungauged watersheds in eastern Australia. Yue (2001) investigated the usability of a two variable gamma model comprising five constraints to describe combined probability actions of multiple variable flood occurrences. 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 LP-III 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.

Here, various statistical methods are established for estimation of flow discharge at four gauge stations in Mahanadi River basin, India. Also, goodness of fit is applied for analyzing data sets. Flow discharge is calculated through various confidence limits (up to 95%) and is also discussed here.

Mahanadi (Figure 1) is a major interstate east-flowing river in peninsular India. The river length from the origin to convergence in the Bay of Bengal is 851 km. In Chhattisgarh the river flows for 357 km and the other 494 km is in Odisha. Details of geographical and hydrological details of four gauging stations are shown in Table 1. Four gauge stations, Rampur, Sundargarh, Jondhra, and Basantpur, are considered for our research.

Generalized extreme value is a continuous probability distribution developed within extreme value theory. It is a combination of Gumbel, Fréchet, and Weibull extreme value distributions and is a bounded distribution of standardized maxima of a series of autonomous and indistinguishable dispersed arbitrary variables. GEV is utilized as an estimate for modeling maxima of lengthy (limited) series of arbitrary variables. Significantly, while using this distribution, the upper bound is unidentified and hence has to be projected; when Weibull is applied, the lower bound is identified as zero.

• K_t = frequency factor

For a given set of data, whether a certain distribution is fit or not is checked using this test. Quality of fit for the observed data set is ranked through calculation of statistical parameters. Affinity of samples from the expected theoretical probability distribution is assessed. To evaluate null hypothesis, it is applied and discarded if the observed test surpasses the critical value for the constant significance level. Chi-squared, Anderson–Darling (AD) and Kolmogorov–Smirnov (KS) tests are employed here at significance level 0.05.

• m= sample size.

Parameters like shape (k,⁠), scale (⁠⁠, and location (⁠ for different distribution methods at the four gauge stations are presented in Table 2. Probability density function (PDF) and the cumulative density function (CDF) graph for respective gauge stations are displayed in Figure 2.

Three goodness-of-fit tests (as presented in the section ‘Goodness-of-fit test’) were used to analyze rainfall data series at the four stations chosen. Test statistics in correspondence to each test were calculated, and hypothesis testing was done at significance level 0.05. For KS, AD, and Chi-squared tests, the tests reject the hypothesis concerning distribution level if the statistics found are more than the critical value 2.5, 0.12555, and 12.592, respectively (Millington et al. 2011). KS, AD and Chi-squared tests were applied in Easy Fit software for selecting the best fit distribution (s) and outcomes obtained are specified in Table 3.

At Rampur, Sundergarh, and Jondhra gauge stations, extreme value distribution gives best results followed by LP III, whereas LP III is the best fit for Basantpur followed by extreme value. Therefore, extreme value can be utilized to calculate flood return periods for the present study area. The poor ranking of Normal distribution fitted results is perhaps due to its nature. Given that Normal distribution is based on central limit theorem while the data considered in this study (annual maximum) are at the extreme right of all considered distributions, it was expected that normal fit to the data would be least efficient. In addition, it is observed that at Rampur, Jondhra, and Basantpur stations the Chi-squared test correctly rejects normal fit to data as both statistics are related to central limit theorem.

For Rampur watershed, the value of flood calculated during monsoon period ranges between 177.4414 m³/sec to 321.6385 m³/sec for 10 years to 150 years' return period (Table 4). Similarly for Sundargarh estimated flood fluctuates from 304.3543 m³/sec to 471.5889 m³/sec. For Jondhra watershed, designed flood lies within 893.1144 m³/sec to 1,944.325 m³/sec for 10 years to 150 years return period. The magnitude of peak floods with respect to the return period is found to be 2,052.522 m³/sec to 3,372.061 m³/sec for Basantpur watershed. This range is the highest among all seasonal peak floods.

The intended flood value for Rampur watershed lies within 198.8535 m³/sec to 372.361 m³/sec for 10 years to 150 years' return period (Table 5). Correspondingly for Sundargarh, the appraised flood diverges from 329.1873 m³/sec to 530.415 m³/sec. For Jondhra watershed, the premeditated flood lies within 986.1719 m³/sec to 2,133.888 m³/sec for 10 years to 150 years' return period. The magnitude of peak floods with respect to the return period is found to be 2,248.463 m³/sec to 3,836.22 m³/sec for Basantpur watershed.

For 10 years to 150 years' return period the calculated flood value deviates within 175.76 m³/sec to 255.892 m³/sec for Rampur watershed (Table 6). Consistently for Sundargarh, the assessed flood is from 302.4046 m³/sec to 395.3386 m³/sec. For Jondhra watershed, premeditated flood contrasts within 885.808 m³/sec to 1,234.06 m³/sec for 10 years to 150 years' return period. The enormity of extreme flood with respect to the return period is found to be 2,037.138 m³/sec to 2,770.419 m³/sec for Basantpur watershed.

The gauged flood value diverges within 177.4024 m³/sec to 317.6723 m³/sec for 10 years to 150 years' return period for Rampur watershed (Table 7). Reliably for Sundargarh, the projected flood is from 303.5037 m³/sec to 532.3849 m³/sec. For Jondhra watershed, the planned flood contrasts within 897.3183 m³/sec to 2,183.191 m³/sec for 10 years to 150 years' return period. The enormity of extreme flood with respect to the return period is established to be 2,047.682 m³/sec to 3,525.389 m³/sec for Basantpur watershed.

Actual data from 2011 to 2019 are considered here for testing purposes. Comparison graphs of observed and simulated flood discharge for all proposed stations are presented in Figure 3.

where

• K = frequency factor given by

• standard deviation

• N= sample size

  

For different values of T, is calculated and shown in Figure 4. Also 95, 90, 85, 80, and 75% confidence limits for various values of T are shown. It is seen that while the confidence probability rises, the confidence interval also increases. Further increase in T causes the confidence band to spread. Thus, Gumbel distribution will give erroneous results if the sample has a value of very much different from 1.14.

For the Normal distribution method, the probability factor is dependent on the required return period (T), which is inversely proportional. Frequency factor (K_t) varies with return periods. Predicted discharge (Q_p) increases with respect to the increase in required return period, while the probability factor (P) decreases. When the frequency factor increases, predicted discharge increases. Predicted flood increases with regard to the increase in the required return period, while at the same time, frequency factor increases with decrease of standard deviation in the case of the Gen. extreme value method. Predicted flood increases with reference to the increase in the required return period, while at the same time, frequency factor also increases, whereas reduced mean (Y_n) and reduced standard deviation (S_n) remain constant for all recurrence intervals; however, reduced variate (Y_t) increases in Gumbel max. In LP III, predicted flood increases with an increase in the required return period, while at the same time, the frequency factor also increases, whereas the coefficient of skewness (C_s) and reduced standard deviation remain constant for all recurrence intervals.

In this paper, an effort has been made to forecast discharges at various return periods using statistical methods. Here, four statistical methods are used to predict flow discharge in the Mahanadi River basin, covering four stations. Four statistical distribution methods, namely, Normal, LP III, Gumbel max, and Gen. extreme value method are employed here. Based on the trends of the last 60 years, the maximum and minimum discharges are found at 150 years and 10 years' return period, respectively. The rate of increase of discharge is very high at the initial return periods and then it becomes constant and eventually lower. The shapes of the graphs are common in nature and most of the time they do not intersect with each other. In most of the cases, Gumbel max gives the peak flood discharge and normal distribution contributes to the least discharge. The Gumbel max is the most widely used method to obtain flood discharge as it can be used for infinite sample sizes. The influencing factor of frequency is analyzed on the basis of analysis of the runoff complexity from drainage basins. It is found that flow probability increases at the upstream of Mahanadi, which may be characterized by the underlying surface condition change influenced by human activities and geomorphology changes, and be considered for future scope. In other sections, the purpose of the research is to diminish future flood damage in the river basin. Hence, forecast of flow discharge is a key indication towards hydrological modeling and development for water resources engineering.

All relevant data are included in the paper or its Supplementary Information.