Calibration of Hydrognomon Model for Simulating the Hydrology of Urban Catchment

Garba, Haruna; Ismail, Abdul Razak; Oriola, Folagbade Olusoga Peter

doi:10.4236/ojmh.2013.32010

Cited by 4 publications

(2 citation statements)

References 4 publications

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“…Hydrognomon is data processing software and is not typically used for flood frequency analysis [7]. It has been utilised by scientists to model the hydrological parameters of the Kaduna River in Niger and for simulating future climatic alterations [9]. However, no study has been done on flood frequency analysis using the Hydrognomon software.…”

Section: Image 1 Location Mapmentioning

confidence: 99%

Flood Frequency Analysis for Burhi Gandak River Basin

Hanwat

Pal

Panda

et al. 2020

IJECC

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The study is aimed at finding the best distribution to match the steam ﬂow and calculation of magnitude and frequency of flow. In the current study, we have used several statistical distributions to find the best fit distribution for stream flow and used flood frequency analysis techniques to find the magnitude and frequency of stream flow and non-exceedance probability of peak discharge. The study has been performed at Sikandarpur and Rosera gauging sites of BurhiGandak River. Historical (50 years) maximum annual peak discharge data of each station are used for statistical analysis for estimating maximum peak discharge in 5, 10, 25, 50, 100 year return period. In this study, Lognormal distribution, Galton distribution, Gamma distribution, Log Pearson Type III distribution, Gumbell distribution, Generalised extreme values distribution have been considered to describe the annual maximum stream ﬂow. Flood frequency analysis methods were used for estimating the magnitude of the extreme flow events and their associated return periods. For both Sikandarpur and Rosera stations, Log Pearson type III distributions showed the lowest value of K–S and Chi-square test statistic. The annual probable peak discharge for 5, 10, 25, 50, and 100 years return period is calculated for each distribution. The most suitable distribution for both the stations is found to be the log-Pearson type III distribution.

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Section: Image 1 Location Mapmentioning

confidence: 99%

Flood Frequency Analysis for Burhi Gandak River Basin

Hanwat

Pal

Panda

et al. 2020

IJECC

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“…Although Hydrognomon is not commonly used in reviewed literature for flood frequency analysis, it was, nonetheless, used successfully by researchers to simulate the hydrology of Kaduna River in Niger [29], and for modelling future climatic variation [30]. Few researchers used Hydrognomon for time-series data analysis [29,30]. However, the software is freely available and can perform frequency analysis among many other hydrologic tasks.…”

Section: Flood Frequency Analysismentioning

confidence: 99%

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

et al. 2019

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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.

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Flooding in Nigeria: a review of its occurrence and impacts and approaches to modelling flood data

Umar

Gray

2022

International Journal of Environmental Studies

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Calibration of Hydrognomon Model for Simulating the Hydrology of Urban Catchment

Cited by 4 publications

References 4 publications

Flood Frequency Analysis for Burhi Gandak River Basin

Flood Frequency Analysis for Burhi Gandak River Basin

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

Flooding in Nigeria: a review of its occurrence and impacts and approaches to modelling flood data

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