scholarly journals BIVARIATE FREQUENCY ANALYSIS OF FLOOD VARIABLES USING COPULA IN KELANTAN RIVER BASIN

2018 ◽  
Vol 30 (3) ◽  
Author(s):  
Mahiuddin Alamgir ◽  
Tarmizi Ismail ◽  
Muhammad Noor
Keyword(s):  
River Basin ◽  
Frequency Analysis ◽  
Return Period ◽  
Peak Flow ◽  
Distribution Functions ◽  
Flood Frequency Analysis ◽  
Flood Duration ◽  
Flood Volume ◽  
Kelantan River Basin ◽  
Univariate Distribution

A copula-based methodology is presented in this study for bivariate flood frequency analysis of Kelantan river basin located in Northeast Malaysia. The joint dependence structures of three flood characteristics, namely, peak flow (Q), flood volume (V) and flood duration (D) were modelled using t-Copula. Various univariate distribution functions of flood variables were fitted with observed flood variables to find the best distributions. Cumulative joint distribution functions (CDF) of peak flow and volume (Q-F), peak flow and duration (Q-D) and volume and duration (V-D) revealed that return period of joint return periods are much higher compared to univariate return period. The joint probabilities of occurrence of 0.8, 0.6, 0.4, 0.2 and 0 can be expected when flood duration greater than 65 h, 54 h, 46 h, and 32 h, and the flood volume higher than 0.62 km3, 0.33 km3, 0.25 km3, and 0.22 km3 respectively.

scholarly journals BIVARIATE FLOOD FREQUENCY ANALYSIS USING GUMBEL COPULA

2018 ◽  
Vol 30 (2) ◽  
Author(s):  
Tarmizi Ismail ◽  
Kamal Ahmed ◽  
Mahiuddin Alamgir ◽  
Mohammad Noor Kakar ◽  
Abu Bakar Fadzil
Keyword(s):  
Frequency Analysis ◽  
Strong Association ◽  
Flood Frequency ◽  
Distribution Functions ◽  
Flood Frequency Analysis ◽  
Flood Duration ◽  
Joint Return ◽  
Kelantan River Basin ◽  
Log Normal ◽  
Univariate Distribution

A copula based methodology is presented in this study for bivariate flood frequency analysis over a station over a Kelantan river basin located in Northeast Malaysia. The joint dependence structures of three flood characteristics, namely, peak flow, flood volume and flood duration were modelled using Gumble Copula. Various univariate distribution functions of flood variables were fitted with observed flood variables to find the best distributions (eg. generalized pareto, log-normal, exponential, gamma distribution, weibull, gumbel, cauchy). The results of study revealed that different variable fits with different distributions and the correlation analysis among variables showed a strong association. Cumulative joint distribution functions (CDF) of peakflow and volume, peakflow and duration and volume and duration revealed that return period of joint return periods are much higher.

scholarly journals Flood susceptibility assessment in Kelantan river basin using copula

2018 ◽  
Vol 7 (2) ◽  
pp. 584 ◽  
Author(s):  
Mohamed Salem Nashwan ◽  
Tarmizi Ismail ◽  
Kamal Ahmed
Keyword(s):  
River Basin ◽  
Flood Risk ◽  
Peak Flow ◽  
Strong Association ◽  
Distribution Functions ◽  
Return Periods ◽  
Flood Duration ◽  
Joint Return ◽  
Kelantan River Basin ◽  
Univariate Distribution

Bivariate frequency analysis of flood variables of different station locations of Kelantan river basin was conducted using copula for the assessment of the geographical distribution of flood risk. Seven univariate distribution functions of flood variables were fitted with flood variables such as peak flow, flood volume, and flood duration to find the best-fitted distributions. The joint dependent structures of flood variables were modeled using Gumbel copula. The results of the study revealed that different variables fit with different distributions. The correlation analysis among variables showed a strong association. Joint distribution functions of peak-flow and volume, peak-flow and duration, and volume and duration revealed that the joint return periods were much higher than univariate return periods of same flood variables. The flood risk analysis based on joint return period of flood variables revealed the highest risk of devastating flood in the downstream. The locations identified as highly susceptible to flood risk by joint distributing of flood variables had experienced most severe floods in recent history, which indicates the effectiveness of the method for the analysis of flood risk. It is expected that this procedure can be helpful for better assessment of flood impacts.

scholarly journals Flood Frequency Analysis at the Downstream of Sg. Perak River Basin using Annual Maximum Flow Discharge Data

2018 ◽  
Vol 7 (4.35) ◽  
pp. 709 ◽  
Author(s):  
Munir Snu ◽  
Sidek L.M ◽  
Haron Sh ◽  
Noh Ns.M ◽  
Basri H ◽  
...  
Keyword(s):  
Probability Distribution ◽  
River Basin ◽  
Frequency Analysis ◽  
Return Period ◽  
Maximum Flow ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Annual Maximum ◽  
Discharge Data ◽  
Flow Discharge

The recent flood event occurred in 2014 had caused disaster in Perak and Sungai Perak is the main river of Perak which is a major natural drainage system within the state. The aim of this paper is to determine the expected discharge to return period downstream for Sg. Perak River Basin in Perak by using annual maximum flow data. Flood frequency analysis is a technique to assume the flow values corresponding to specific return periods or probabilities along the river at a different site. The method involves the observed annual maximum flow discharge data to calculate statistical information such as standard deviations, mean, sum, skewness and recurrence intervals. The flood frequency analysis for Sg. Perak River Basin was used Log Pearson Type-III probability distribution method. The annual maximum peak flow series data varying over period 1961 to 2016. The probability distribution function was applied to return periods (T) where T values are 2years, 5years, 10years, 25years, 50years, and 100years generally used in flood forecasting. Flood frequency curves are plotted after the choosing the best fits probability distribution for annual peak maximum data. The results for flood frequency analysis shows that Sg. Perak at Jambatan Iskandar much higher inflow discharge  which is 3714.45m3/s at the 100years return period compare to Sg. Plus at Kg Lintang and Sg. Kinta at Weir G. With this, the 100years peak flow at Sg Perak river mouth is estimated to be in the range of 4,000 m3/s. Overall, the analysis relates the expected flow discharge to return period for all tributaries of Sg. Perak River Basin.

scholarly journals Flood Frequency Analysis Using Participatory GIS and Rainfall Data for Two Stations in Narok Town, Kenya

Hydrology ◽  
2019 ◽  
Vol 6 (4) ◽  
pp. 90 ◽  
Author(s):  
Houessou-Dossou ◽  
Gathenya ◽  
Njuguna ◽  
Gariy
Keyword(s):  
Frequency Analysis ◽  
Return Period ◽  
Computational Modelling ◽  
Flood Frequency ◽  
Meteorological Station ◽  
Distribution Functions ◽  
Rainfall Data ◽  
Flood Frequency Analysis ◽  
Return Periods ◽  
Participatory Gis

Flood management requires in-depth computational modelling through assessment of flood return period and river flow data in order to effectively analyze catchment response. The participatory geographic information system (PGIS) is a tool which is increasingly used for collecting data and decision making on environmental issues. This study sought to determine the return periods of major floods that happened in Narok Town, Kenya, using rainfall frequency analysis and PGIS. For this purpose, a number of statistical distribution functions were applied to daily rainfall data from two stations: Narok water supply (WS) station and Narok meteorological station (MS). The first station has a dataset of thirty years and the second one has a dataset of fifty-nine (59) years. The parameters obtained from the Kolmogorov–Smirnov (K–S) test and chi-square test helped to select the appropriate distribution. The best-fitted distribution for WS station were Gumbel L-moment, Pareto L-moment, and Weibull distribution for maximum one day, two days, and three days rainfall, respectively. However, the best-fitted distribution was found to be generalized extreme value L-moment, Gumbel and gamma distribution for maximum one day, two days, and three days, respectively for the meteorological station data. Each of the selected best-fitted distribution was used to compute the corresponding rainfall intensity for 5, 10, 25, 50, and 100 years return period, as well as the return period of the significant flood that happened in the town. The January 1993 flood was found to have a return period of six years, while the April 2013, March 2013, and April 2015 floods had a return period of one year each. This study helped to establish the return period of major flood events that occurred in Narok, and highlights the importance of population in disaster management. The study’s results would be useful in developing flood hazard maps of Narok Town for different return periods.

scholarly journals Joint return periods in hydrology: a critical and practical review focusing on synthetic design hydrograph estimation

2012 ◽  
Vol 9 (5) ◽  
pp. 6781-6828 ◽  
Author(s):  
S. Vandenberghe ◽  
M. J. van den Berg ◽  
B. Gräler ◽  
A. Petroselli ◽  
S. Grimaldi ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Return Period ◽  
Three Dimensional ◽  
Distribution Functions ◽  
Return Periods ◽  
Design Variables ◽  
Joint Return ◽  
Design Values ◽  
Multivariate Frequency Analysis ◽  
Joint Return Period

Abstract. Most of the hydrological and hydraulic studies refer to the notion of a return period to quantify design variables. When dealing with multiple design variables, the well-known univariate statistical analysis is no longer satisfactory and several issues challenge the practitioner. How should one incorporate the dependence between variables? How should the joint return period be defined and applied? In this study, an overview of the state-of-the-art for defining joint return periods is given. The construction of multivariate distribution functions is done through the use of copulas, given their practicality in multivariate frequency analysis and their ability to model numerous types of dependence structures in a flexible way. A case study focusing on the selection of design hydrograph characteristics is presented and the design values of a three-dimensional phenomenon composed of peak discharge, volume and duration are derived. Joint return period methods based on regression analysis, bivariate conditional distributions, bivariate joint distributions, and Kendal distribution functions are investigated and compared highlighting theoretical and practical issues of multivariate frequency analysis. Also an ensemble-based method is introduced. For a given design return period, the method chosen clearly affects the calculated design event. Eventually, light is shed on the practical implications of a chosen method.

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