Time distribution of heavy rainfall events in south west of Iran

Ghassabi, Zahra; Kamali, Gholamali; Meshkatee, Amir-Hussain; Hajam, S; Javaheri, Nasrolah

doi:10.1016/j.jastp.2016.03.006

Cited by 15 publications

(11 citation statements)

References 7 publications

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“…The 50% curve is the single most representative huff curve in each quartile (Huff 1990); however, the selection of one single huff curve for practical purposes is still unclear (Bonta 2004). Four synthetic dimensionless mass curves named type I, IA, II, and III developed for use in US watersheds (US-SCS 1986) have been used in studies such as (Awadallah and Younan 2012; Ghassabi et al 2016) to define the shape of design storms. However, these curves have been found to overestimate peak discharges (Guo and Hargadin 2009;Kimoto et al 2011;Dullo et al 2017).…”

Section: Introductionmentioning

confidence: 99%

A multistate first-order Markov model for modeling time distribution of extreme rainfall events

Rohith

Gitau

Chaubey

et al. 2020

Stoch Environ Res Risk Assess

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The time distribution of extreme rainfall events is a significant property that governs the design of urban stormwater management structures. Accuracy in characterizing this behavior can significantly influence the design of hydraulic structures. Current methods used for this purpose either tend to be generic and hence sacrifice on accuracy or need a lot of model parameters and input data. In this study, a computationally efficient multistate first-order Markov model is proposed for use in characterizing the inherently stochastic nature of the dimensionless time distribution of extreme rainfall. The model was applied to bivariate extremes at 10 stations in India and 205 stations in the United States (US). A comprehensive performance evaluation was carried out with one-hundred stochastically generated extremes for each historically observed extreme rainfall event. The comparisons included: 1-h (15-min); 2-h (30-min); and, 3-h (45-min) peak rainfall intensities for India and (US) stations, respectively; number of first, second, third, and fourth-quartile storms; the dependence of peak rainfall intensity on total depth and duration; and, return levels and return periods of peak discharge when these extremes were applied on a hypothetical urban catchment. Results show that the model efficiently characterizes the time distribution of extremes with: Nash–Sutcliffe-Efficiency > 0.85 for peak rainfall intensity and peak discharge; < 20% error in reproducing different quartile storms; and, < 0.15 error in correlation analysis at all study locations. Hence the model can be used to effectively reproduce the time distribution of extreme rainfall events, thus increasing the confidence of design of urban stormwater management structures.

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Section: Introductionmentioning

confidence: 99%

A multistate first-order Markov model for modeling time distribution of extreme rainfall events

Rohith

Gitau

Chaubey

et al. 2020

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

show abstract

“…Terranova and Iaquinta [34] improved Huff's curves by proposing a new criterion for classifying standardized rainfall profiles. Ghassabi and Kamali [35] computed the rainfall temporal patterns based on a three-parametric logistic function. Jun and Qin [36] established a representative quartile of the design storm by considering inter-event time definition in Huff's method.…”

Section: Introductionmentioning

confidence: 99%

Temporal Pattern Analysis of Local Rainstorm Events in China During the Flood Season Based on Time Series Clustering

Wang

2020

Water

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Similar to the rainfall depth, duration and intensity, the temporal pattern is also an important characteristic of rainstorm events. Studies have shown that temporal patterns will influence runoff modelling, flash flood warning thresholds as well as urban and infrastructure flood inundation simulations. In this study, a time series clustering method using dynamic time warping (DTW) as similarity measurement criteria is proposed to analyze rainfall temporal patterns. Compared with the existing approaches, it can better reflect the real rainfall processes. Based on this novel method, five representative temporal patterns were extracted from 13,299 rainstorm events during the flood season in China. Through the analysis of their statistical characteristics, the disaster-causing risks of each temporal pattern were compared. Furthermore, we found that for rainstorm events whose durations are less than 24 h, the rainfall is mainly concentrated in 3 to 6 h, which proposes higher requirements for the design of flood control and drainage projects compared with those using average intensities of 12 or 24 h as design standards. Finally, through regional analysis, we found that the rainfall depth, intensity and peak value are affected by the macroclimate. However, the temporal patterns are not strongly related to the macroclimate but are more likely to be affected by the local climate and topography, which needs further studies at smaller scales.

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“…They are used to attribute a temporal structure to design rainfall depths in order to generate a hyetograph (Bérod et al 1992;Veneziano and Villani 1999;Prodanovic and Simonovic 2004). RMCs have been introduced 50 years ago and are used in particular in the context of extreme event estimation (Tsihrintzis and Sidan 1998;Golian et al 2010;Caballero and Rahman 2013;Vernieuwe et al 2015;Ghassabi et al 2016) The first comprehensive analysis of RMCs is due to Huff (1967), who analyzed 30-min precipitation data from 49 gauges spread over 1037 km 2 in the State of Illinois (as reported in Azli and Rao 2010). These curves remain an important reference today (Dolsak et al 2016;Pan et al 2017).…”

Section: Introductionmentioning

confidence: 99%

“…A potential solution to overcome this case study specificity is the use of so-called design Huff curves (Natural Resources Conservation Service 1986), which are synthetic temporal rainfall structures for the construction of design storms. However, these curves have been shown to lead to unrealistic results (Kimoto et al 2011;Guo and Hargadin 2009;Ghassabi et al 2016), similar to the Chicago method (Keifer and Chu 1957), which has been shown to give too intense burst peaks compared to observed storm peaks (Alfieri et al 2008;Watt and Marsalek 2013).…”

Section: Introductionmentioning

confidence: 99%

Swiss Rainfall Mass Curves and their Influence on Extreme Flood Simulation

Zeimetz

Schaefli

Artigue

et al. 2018

Water Resour Manage

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Time distribution of heavy rainfall events in south west of Iran

Cited by 15 publications

References 7 publications

A multistate first-order Markov model for modeling time distribution of extreme rainfall events

A multistate first-order Markov model for modeling time distribution of extreme rainfall events

Temporal Pattern Analysis of Local Rainstorm Events in China During the Flood Season Based on Time Series Clustering

Swiss Rainfall Mass Curves and their Influence on Extreme Flood Simulation

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