Some best‐fit probability distributions for at‐site flood frequency analysis of the Ume River

Kousar, Samara; Khan, Abrar Raza; Hassan, Mahmood Ul; Noreen, Zahra; Bhatti, Sajjad Haider

doi:10.1111/jfr3.12640

Cited by 23 publications

(21 citation statements)

References 10 publications

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“…The principal statistical assumptions of at-site and RFA include independence, stationarity and randomness of data series at each gauging site (see e.g., Ul Hassan et al 2019;Hussain et al 2017;Shahzadi et al 2013 andKousar et al 2020). To investigate the serial correlation of the observed data series at each gauging site, we use the lag-1 correlation coefficient (r).…”

Section: Resultsmentioning

confidence: 99%

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Regional frequency analysis of annual daily rainfall maxima in Skåne, Sweden

Hassan

Noreen

Ahmed

2021

Intl Journal of Climatology

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Extreme daily rainfall events are critical for the urban drainage system, human life, agriculture and small catchments. The information about extreme rainfall magnitudes and frequencies is immensely important for civil engineers, city planners, scientists related to water management, rescue operations and flood control works. This study illustrates the results of regional frequency analysis (RFA) of annual maximum daily rainfall (AMDR) of Skåne County, Sweden. L-moments based heterogeneity measure (H) reveals that the Skåne County is a homogeneous region. Based on the L-moment ratio diagram and Z Dist statistic results, the generalized normal (GNO) distribution is selected as the most suitable regional distribution. The accuracy measures used in K-fold cross validation indicate that support vector machine (SVM) model is an appropriate model to find the index rainfall at ungauged sites in the region. The sites characteristics, elevation and latitude are identified as the most important variables to explain the variation in mean annual maximum daily rainfall (MAMDR). Finally, spatial maps of predicted MAMDR for different return periods are constructed by using index rainfall combined with regional quantiles. Spatial maps offer an overall view of the expected MAMDR in the region that is helpful for multiple decision makers including infrastructure planners, city planners, emergency managers, engineers and many others. K E Y W O R D S K-fold cross validation, L-moments, Monte Carlo simulation, quantile estimates, regional frequency analysis, spatial map 1 | INTRODUCTION Daily extreme rainfall is one of the utmost environmental events which cause sizeable damages throughout the world. The rainfall recorded in the last century was the highest over the past 8,500 years (Hong et al., 2014). A reliable estimate of extreme rainfall magnitude is important for flood management, irrigation, rescue operations, water management, construction of urban drainage systems and many other purposes. Skåne is the tenth largest county of Sweden. It has the best agricultural land and is known as a breadbasket of Sweden. It stands for about half of Sweden's food production (Skåne Region, 2014). Rainfall is an important factor for agriculture in Skåne. Skåne agriculture and food industries are highly dependent on this hydrological

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Section: Resultsmentioning

confidence: 99%

“…2013 and Kousar et al . 2020). To investigate the serial correlation of the observed data series at each gauging site, we use the lag‐1 correlation coefficient ( r ).…”

Section: Resultsmentioning

confidence: 99%

Regional frequency analysis of annual daily rainfall maxima in Skåne, Sweden

Hassan

Noreen

Ahmed

2021

Intl Journal of Climatology

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“…Where the K‐S test returns a p ‐value

\ge

0.05, we considered that annual streamflow follows the assumed distribution. We used the method of maximum likelihood estimation to fit the distribution parameters, which is a powerful and efficient method accepted by many researchers (Ceola et al., 2010; Hasan et al., 2021; and Kousar et al., 2020) because it estimates parameters with the smallest sampling variance (Rao & Hamed, 2019).…”

Section: Model Evaluationmentioning

confidence: 99%

A Simple Analytical Method to Assess Multiple‐Priority Water Rights in Carryover Systems

Ren

Stewardson

Peel

2022

Water Resources Research

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Simple analytical storage–reliability–yield relationships have traditionally only considered a single reliability for a single yield, yet many reservoirs supply water of different priorities. Simulation models may be used to handle such multiple‐priority water rights but these models are complex and usually system specific. Here we propose a simple analytical method based on Gould‐Dincer to estimate yields in dual and triple priority allocation systems from a carryover storage. This allows rapid assessment of changes in water resource availability for different water priorities, potentially over large spatial scales. We use a dam simulation model to assess this method at 15 sites across six continents and find that the “dual‐priority” and “triple‐priority” G‐D methods can reproduce the results of the dam simulation model. Thus, the method could be generalized for multiple priority allocation systems use. We demonstrate the potential utility of the “dual‐priority” G‐D method through an evaluation of the optimum yield between high and low‐priority water rights (HPWR and LPWR) from hypothetical (but realistic) carryover systems. It confirms the possibility that “dual‐priority” water allocation may be beneficial overall compared to a single‐priority water right. By balancing the yield of HPWR and LPWR, the optimum marginal value of available water (i.e., sum of high‐ and low‐priority water) can be achieved. Overall, the method provides a simple way for rapid assessment across multiple sites allowing insights into optimal allocation practices and the interacting driving factors that affect them at a regional‐to‐global scale.

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“…Therefore, it is necessary to make accurate estimations of the flow rate for divided return periods. In the literature, there are studies in which flood discharge was calculated for several return periods (Kamal et al, 2017;Garmdareh et al, 2018;Bhat et al, 2019;Kousar et al, 2020;Mangukiya et al, 2022). Design flow rates are derived from the annual maximum discharges observed over many years.…”

Section: Introductionmentioning

confidence: 99%

A comparative study of probability distribution models for flood discharge estimation

Turhan

DEĞERLİ

2022

Geofizika (Online)

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Due to climate change, floods have been more frequent in recent years. Estimating the flood discharge as a result of flood frequency analysis is very substantial to make necessary preparations for possible floods. Data covering 36 years were collected from different stream gauging stations (SGS No: D17A016 and EIEI 1731) in Eastern Mediterranean Basin. With these data, flood discharge values were computed for return periods of 2, 5, 10, 25, 50, 100, 200, 500 and 1000 years. Normal, Log-Normal, Gumbel, Pearson Type III and Log-Pearson Type III statistical distribution methods were used. Kolmogorov-Smirnov (K-S) and Chi-square goodness-of-fit tests were performed to determine which distribution fitted the flood discharge the best. The study showed that the highest flood discharge among the probability distributions for both SGSs came from the Log-Normal distribution, and the lowest discharge was calculated with the Normal distribution. The K-S tests showed that all probability distributions conformed to the 20% significance level. For SGS D17A016, the flood values calculated with Log-Normal distribution were compatible with a 90% confidence interval according to the Chi-square test. Flood values obtained with the other distributions were found within the 10% significance level. In the Chi-square test for SGS EIEI-1731, all probability distributions fell within a 10% significance.

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Some best‐fit probability distributions for at‐site flood frequency analysis of the Ume River

Cited by 23 publications

References 10 publications

Regional frequency analysis of annual daily rainfall maxima in Skåne, Sweden

Regional frequency analysis of annual daily rainfall maxima in Skåne, Sweden

A Simple Analytical Method to Assess Multiple‐Priority Water Rights in Carryover Systems

A comparative study of probability distribution models for flood discharge estimation

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