IDF relationships using bivariate copula for storm events in Peninsular Malaysia

Ariff, Noratiqah Mohd; Jemain, Abdul Aziz; Ibrahim, Kamarulzaman; Zin, Wan Zawiah Wan

doi:10.1016/j.jhydrol.2012.08.045

Cited by 54 publications

(37 citation statements)

References 29 publications

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“…Therefore, developing an IDAF model able to account for a possible evolution of the ARF with the return period level is a path that has to be explored, copulas being a candidate for such a development (e.g. Singh and Zhang, 2007;Ariff et al, 2012). It is also envisioned to test other IDAF model formulations based on alternative approaches for modelling the scale relationships, among which the method proposed by Overeem et al (2010) seems of particular interest.…”

Section: Discussionmentioning

confidence: 99%

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Characterising the space–time structure of rainfall in the Sahel with a view to estimating IDAF curves

Panthou

Vischel

Lebel

et al. 2014

Hydrol. Earth Syst. Sci.

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Abstract. Intensity-duration-area-frequency (IDAF) curves are increasingly demanded for characterising the severity of storms and for designing hydraulic structures. Their computation requires inferring areal rainfall distributions over the range of space scales and timescales that are the most relevant for hydrological studies at catchment scale. In this study, IDAF curves are computed for the first time in West Africa, based on the data provided by the AMMA-CATCH Niger network, composed of 30 recording rain gauges having operated since 1990 over a 16 000 km 2 area in south-western Niger. The IDAF curves are obtained by separately considering the time (intensity-duration-frequency, IDF) and space (areal reduction factor, ARF) components of the extreme rainfall distribution. Annual maximum intensities are extracted for resolutions between 1 and 24 h in time and from point (rain gauge) to 2500 km 2 in space. The IDF model used is based on the concept of scale invariance (simple scaling) which allows the normalisation of the different temporal resolutions of maxima series to which a global generalised extreme value (GEV) is fitted. This parsimonious framework allows one to use the concept of dynamic scaling to describe the ARF. The results show that coupling a simple scaling in space and time with a dynamical scaling that relates to space and time allows one to satisfactorily model the effect of space-time aggregation on the distribution of extreme rainfall.

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Section: Discussionmentioning

confidence: 99%

“…IDF practical studies are also numerous but focused on regions where long series of sub-daily rainfall are available (e.g. Borga et al, 2005;Gerold and Watkins, 2005;Nhat et al, 2007;Bara et al, 2009;Ben-Zvi, 2009;Overeem et al, 2009;Awadallah, 2011;Ariff et al, 2012). On the other hand, when ARFs are computed from…”

Section: Introductionmentioning

confidence: 99%

Characterising the space–time structure of rainfall in the Sahel with a view to estimating IDAF curves

Panthou

Vischel

Lebel

et al. 2014

Hydrol. Earth Syst. Sci.

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“…Extreme rainfall was a complicated phenomenon and its marginal distributions were not necessarily similar or distributed as normal [2]. According to, historical rainfall data obtained from various rainfall stations can be used to develop Intensity-Duration-Frequency (IDF) curve and it was the most frequently used tool to estimate rainfall [3].…”

Section: Introductionmentioning

confidence: 99%

“…According to, historical rainfall data obtained from various rainfall stations can be used to develop Intensity-Duration-Frequency (IDF) curve and it was the most frequently used tool to estimate rainfall [3]. Extreme rainfall was a complicated phenomenon and its marginal distributions were not necessarily similar or distributed as normal [2]. In order to get accurate hydrologic analyses, reliable rainfall intensity estimations were necessary.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Gev and Gumble’s Distribution for Development of Intensity Duration Frequency Curve for Flood Prone Area in Sabah

Jefrin¹,

Bolong²,

Sentian³

et al. 2018

Pak. j. geol.

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“…In the relatively recent literature, there is a wide application of the copula functions to model the natural variability of hydrometeorological variables, ranging from rainfall (De Michele and Salvadori, 2003;Zhang and Singh, 2007;Balistrocchi and Bacchi, 2011;Singh and Zhang, 2007;Ariff et al, 2012) to floods (Aronica et al, 2012;Balistrocchi et al, 2014;Candela et al, 2014;Domeneghetti et al, 2013;Gräler et al, 2013;Ganguli and Reddy, 2013). …”

Section: Introductionmentioning

confidence: 99%

Dealing with uncertainty in the probability of overtopping of a flood mitigation dam

Michailidi

Bacchi

2017

Hydrol. Earth Syst. Sci.

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Abstract. In recent years, copula multivariate functions were used to model, probabilistically, the most important variables of flood events: discharge peak, flood volume and duration. However, in most of the cases, the sampling uncertainty, from which small-sized samples suffer, is neglected. In this paper, considering a real reservoir controlled by a dam as a case study, we apply a structure-based approach to estimate the probability of reaching specific reservoir levels, taking into account the key components of an event (flood peak, volume, hydrograph shape) and of the reservoir (rating curve, volume-water depth relation). Additionally, we improve information about the peaks from historical data and reports through a Bayesian framework, allowing the incorporation of supplementary knowledge from different sources and its associated error. As it is seen here, the extra information can result in a very different inferred parameter set and consequently this is reflected as a strong variability of the reservoir level, associated with a given return period. Most importantly, the sampling uncertainty is accounted for in both cases (single-site and multi-site with historical information scenarios), and Monte Carlo confidence intervals for the maximum water level are calculated. It is shown that water levels of specific return periods in a lot of cases overlap, thus making risk assessment, without providing confidence intervals, deceiving.

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IDF relationships using bivariate copula for storm events in Peninsular Malaysia

Cited by 54 publications

References 29 publications

Characterising the space–time structure of rainfall in the Sahel with a view to estimating IDAF curves

Characterising the space–time structure of rainfall in the Sahel with a view to estimating IDAF curves

Comparison of Gev and Gumble’s Distribution for Development of Intensity Duration Frequency Curve for Flood Prone Area in Sabah

Dealing with uncertainty in the probability of overtopping of a flood mitigation dam

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