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Abstract In this paper frequency analysis of annual extreme daily rainfall of 14 gauging stations located in an arid zone of Iran were performed using parametric and nonparametric approaches. The parametric methods include normal, two- and three-parameter log-normal, two-parameter gamma, Pearson and log-Pearson type III, extreme value type I (Gumbel), generalized extreme value and generalized logistic distributions. The nonparametric approach is Fourier series method. The data were fitted to all of above mentioned models and the results showed that the goodness of fit of the data to Fourier series is much better than other parametric methods. Thus the Fourier series can be used as an alternative approach for frequency analysis of extreme daily rainfall in an arid zone. In addition, the quantiles can be calculated by the Fourier series.

Floods are among the most occurring natural hazards that cause severe damage to infrastructure and loss of life. In India, southern Gujarat is affected during the monsoon season, facing multiple flood events in the Damanganga basin. As the basin is one of the data-scarce regions, evaluating the globally available dataset for flood risk mitigation studies in the Damanganga basin is crucial. In the present study, we compared four open-source digital elevation models (DEMs) (SRTM, Cartosat-1, ALOS-PALSAR, and TanDEMX) for hydrodynamic (HD) modeling and flood risk mapping. The simulated HD models for multiple flood events using HEC-RAS v6.3 were calibrated by adopting different roughness coefficients based on land-use land cover, observed water levels at gauge sites, and peak flood depths in the flood plain. In contrast to the previous studies on the Purna river basin (the neighboring basin of Damanganga), the present study shows that Cartosat-1 DEM provides reliable results with the observed flood depth. Furthermore, the calibrated HD model was used to determine the flood risk corresponding to 10, 25, 50, and 100-year return period floods calculated using Gumbel's extreme value (GEV) and log-Pearson type III (LP-III) distribution techniques. Comparing the obtained peak floods corresponding to different return periods with the observed peak floods revealed that the LP-III method gives more reliable estimates of flood peaks for lower return periods, while the GEV method gives comparatively more reliable estimates for higher return period floods. The study shows that evaluating different open-source data and techniques is crucial for developing reliable flood mitigation plans with practical implications. [ABSTRACT FROM AUTHOR]

In this paper frequency analysis of annual extreme daily rainfall of 14 gauging stations located in an arid zone of Iran were performed using parametric and nonparametric approaches. The parametric methods include normal, two- and three-parameter log-normal, two-parameter gamma, Pearson and log-Pearson type III, extreme value type I (Gumbel), generalized extreme value and generalized logistic distributions. The nonparametric approach is Fourier series method. The data were fitted to all of above mentioned models and the results showed that the goodness of fit of the data to Fourier series is much better than other parametric methods. Thus the Fourier series can be used as an alternative approach for frequency analysis of extreme daily rainfall in an arid zone. In addition, the quantiles can be calculated by the Fourier series. [ABSTRACT FROM AUTHOR]

Hydraulic structures such as surface drainages and culverts are usually constructed in urban areas with the intention of draining runoff into nearby streams and rivers in order to avoid flooding. However, most of these structures frequently fail to serve the intended use due to the occurrence of high intensity rainfall accompanied with long duration, which produce runoff discharge higher than their designed capacities. This is common in many developing countries as drainages and culverts are most times constructed without considering hydrological analysis of the catchment. Hence, this research considered Port Harcourt city as a case study by utilizing 50 years rainfall data to develop rainfall Intensity-Duration-Frequency (IDF) curves that will be used for subsequent design of drainages and culverts within the city and its environs. The IDF curves were developed using Gumbel, Pearson type III and Log-Pearson type III distributions at return periods of 2, 5, 10, 25 and 50 years. However, the durations considered were 5, 10, 20, 30, 45, 60, 90, 120, 150, 180, 210, 240, 300, 360 and 420 minutes. Results showed that the IDF equations developed for the three frequency distributions highly correlate with the observed intensities since there goodness of fit (R2) ranges from 0.9766 - 0.9865. Also, it was noted that there was no significant difference (p < 0.01) between the predicted rainfall intensities from all the IDF equations and the observed intensities. Notwithstanding, the IDF equation developed for Gumbel distribution was recommended to be given higher priority since it has the highest R2 value. [ABSTRACT FROM AUTHOR]

This study aims to carry out the flood frequency analysis (FFA) of river Jhelum in Kashmir basin. Gumbel and Log-Pearson Type III (LP3) probability distributions were employed for simulating the future flood discharge scenarios using annual peak flow data (1956–2014) from three gauging stations of the River Jhelum. The predicted design floods of various return periods (Tr) i.e., 5, 10, 25, 50, 100, and 200 were obtained and compared. The findings reveal that the estimated discharge of all the return periods exceeds the current average carrying capacity (∼35000 cusecs in Srinagar) of the river. It is thus likely that any flood event with the return period of five years or above would overflow the banks of the River Jhelum. Furthermore, the present study demonstrates that the river discharge can be satisfactorily projected by any one of the used probability distribution methods; however, as revealed by the goodness-of-fit test, Log-Pearson Type III was found to be the better fitted probability distribution among the two. [ABSTRACT FROM AUTHOR]

Nonstationary extreme value analysis (NEVA) can improve the statistical representation of observed flood peak distributions compared to stationary (ST) analysis, but management of flood risk relies on predictions of out-of-sample distributions for which NEVA has not been comprehensively evaluated. In this study, we apply split-sample testing to 1250 annual maximum discharge records in the United States and compare the predictive capabilities of NEVA relative to ST extreme value analysis using a log-Pearson Type III (LPIII) distribution. The parameters of the LPIII distribution in the ST and nonstationary (NS) models are estimated from the first half of each record using Bayesian inference. The second half of each record is reserved to evaluate the predictions under the ST and NS models. The NS model is applied for prediction by (1) extrapolating the trend of the NS model parameters throughout the evaluation period and (2) using the NS model parameter values at the end of the fitting period to predict with an updated ST model (uST). Our analysis shows that the ST predictions are preferred, overall. NS model parameter extrapolation is rarely preferred. However, if fitting period discharges are influenced by physical changes in the watershed, for example from anthropogenic activity, the uST model is strongly preferred relative to ST and NS predictions. The uST model is therefore recommended for evaluation of current flood risk in watersheds that have undergone physical changes. Supporting information includes a MATLAB® program that estimates the (ST/NS/uST) LPIII parameters from annual peak discharge data through Bayesian inference. [ABSTRACT FROM AUTHOR]

Flood frequency analysis generally involves the use of simple parametric probability distributions to smooth and extrapolate the information provided by short flood records to estimate extreme flood flow quantiles. Parametric probability distributions can have difficulty simultaneously fitting both the largest and smallest floods. A danger is that the smallest observations in a record can distort the exceedance probabilities assigned to the large floods of interest. The identification and treatment of such Potentially Influential Low Floods (PILFs) frees a fitting algorithm to describe the distribution of the larger observations. This can allow parametric flood frequency analysis to be both efficient, and also robust to deviations from the proposed probability model's lower tail. Historically, PILF identification involved subjective judgement. We propose a new multiple Grubbs-Beck outlier test (MGBT) for objective PILF identification. MGBT PILF identification rates (akin to Type I errors) are reported for the lognormal (LN) distribution and the log-Pearson Type III (LP3) distribution with a variety of skew coefficients. MGBT PILF identification generally matched subjective identification from a recent California flood frequency study. Monte Carlo results show that censoring of PILFs identified by the MGBT algorithm improves the extreme quantile estimator efficiency of the expected moments algorithm (EMA) for negatively skewed LP3 distributions and has little effect for zero or positive skews; simultaneously it protects against deviations from the LP3 in the lower tail, as illustrated by distorted LN examples. Thus, MGBT generally makes flood frequency analysis based on the LP3 distribution with EMA both more accurate and more robust. [ABSTRACT FROM AUTHOR]

The flood magnitudes with 25, 50, and 100 years return periods and the environmental flows (Qenv) are of outmost importance in the context of hydraulic and hydrologic design. In this study, 25 watershed characteristics were linked with the aforementioned recurrence intervals, peak discharge values, as well as Qenv for 15 pristine torrential watersheds with more than 10 years of streamflow records in the Rhodopi mountain range with a view to generating regional relationships for the assessment of discharge annual peaks and environmental flows regarding the ungauged torrential watersheds in the region. The Log-Pearson Type III probability distribution was fitted in the discharge annual peaks time series, so as to predict Q25, Q50, and Q100, whereas the Tennant method was utilised so as to estimate the environmental flows magnitude. Similarly, the Kolmogorov–Smirnov and the Anderson–Darling tests were performed to verify the distribution fitting. The Principal Components Analysis method reduced the explanatory variables number to 14, whilst the stepwise multiple regression analysis indicated that the exponential model is suitable for predicting the Q25, the power model best forecasted the Q50 and Q100, whereas the linear model is appropriate for Qenv prognosis. In addition, the reliability of the obtained regression models was evaluated by employing the R2, the Nash–Sutcliffe efficiency, and the Index of Agreement Statistical Criteria, which were found to range from 0.91–0.96, 0.88–0.95 and 0.97–0.99, respectively, thereby denoting very strong and accurate forecasts by the generated equations. Thus, the developed equations could successfully predict the peak discharge values and environmental flows within the region's ungauged watersheds with the drainage size not exceeding 330 km2. [ABSTRACT FROM AUTHOR]