Is the covariate based non-stationary rainfall IDF curve capable of encompassing future rainfall changes?

Agilan, V.; Umamahesh, N. V.

doi:10.1016/j.jhydrol.2016.08.052

Cited by 52 publications

(26 citation statements)

References 64 publications

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“…According to the extreme value theory, the behavior of these maxima can generally be described by one of the three extreme value distributions: Gumbel, Fréchet, and Weibull. The generalized extreme value (GEV) distribution, a single parametric family, represents the aforementioned three extreme distributions [ Khaliq et al ., ; Agilan and Umamahesh , ]. The cumulative distribution function of the GEV is described as [ Koutsoyiannis , ; Hosking and Wallis , ] follows:

F_{normalG normalE normalV} ((), x true| μ, σ, ξ) = normalexp ([], prefix- {(1 + ξ (\frac{x - μ}{σ}))}^{prefix- 1 true/ ξ}) .

…”

Section: Methodsmentioning

confidence: 99%

“…The literature on stationary‐based risk assessment of precipitation intensity shows a variety of approaches in the context of univariate and multivariate processing [ Veneziano and Yoon , ; Endreny and Imbeah , ; Blanchet et al ., ; Bezak et al ., ]. Very few publications discuss the incorporation of climate‐related nonstationarity in IDF curves [ Agilan and Umamahesh , ; Cheng and AghaKouchak , ; Lima et al ., ]. The present study develops a flexible time‐varying risk framework using Bayesian techniques to model different complex forms of nonstationarity.…”

Section: Introductionmentioning

confidence: 99%

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Time‐varying extreme rainfall intensity‐duration‐frequency curves in a changing climate

Sarhadi

Soulis

2017

Geophysical Research Letters

132

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Anthropogenic climate change influences the nature and probabilistic behavior of extreme climate phenomena over time. Current infrastructure design of water systems, however, is based on intensity‐duration‐frequency (IDF) curves that assume extreme precipitation will not significantly change. To sustain the reliability of infrastructure designs in a changing environment, time‐varying nonstationary‐based IDF curves must replace the static stationary‐based IDF curves. This study outlines a fully time varying risk framework using Bayesian Markov chain Monte Carlo techniques to incorporate the impact of different complex nonstationary conditions on the occurrence of extreme precipitation in the Great Lakes area. The results demonstrate the underestimation of the extreme precipitation using stationary assumptions and the importance of updating infrastructure design strategies in a changing climate.

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F_{normalG normalE normalV} ((), x true| μ, σ, ξ) = normalexp ([], prefix- {(1 + ξ (\frac{x - μ}{σ}))}^{prefix- 1 true/ ξ}) .

…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Time‐varying extreme rainfall intensity‐duration‐frequency curves in a changing climate

Sarhadi

Soulis

2017

Geophysical Research Letters

132

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“…According to National Research Council [36], these changes in climate are the result of increased emissions of greenhouse gases like carbon dioxide (CO2) from the burning of fossil fuels and destruction of tropical forests. As observed by Agilan and Umamehesh [1], nowadays it is widely recognized that global climate changes are intensifying extreme rainfall events and creating a non-stationary component in the extreme rainfall time series.…”

Section: Introductionmentioning

confidence: 99%

Prediction vs. Performance of IDF Curves from Precipitation Data of Regional Climate Models for a Brazilian Coastal County

Martins¹,

Kruk²,

Queiroz³

et al. 2018

Preprint

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Drainage systems are usually dimensioned for design storms based on intensity-duration-frequency (IDF) curves of extreme precipitation. For each location, different IDF curves are established based on local hydrological conditions. Recent research shows that these curves also vary with time, and should be updated with recent data. The purpose of this study is to evaluate IDF curves obtained from precipitation simulations from the Eta RCM, comparing them with IDF curves obtained from data of a rainfall station. Climate models can be a useful tool for assessing the impacts of climate changes on drainage systems, referring precipitation forecasts. In this study, the Eta RCM was forced by two global climate models: HadGEM2-ES and MIROC5. The bias of the precipitation data, generated by RCM models, was corrected using a Gamma distribution. The Juqueriquerê River Basin, in the cities of Caraguatatuba and São Sebastião, São Paulo State, Brazil, was chosen as a case study. The results show a good correlation between the IDF curves of simulated and observed rainfall for the control period (1960-2005), indicating the strong possibility of using the Eta RCM precipitation forecasts for 2007 - 2099 to establish future IDFs thereby, taking into account climate changes in urban drainage design.

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“…Zamani et al [20] showed that monthly, seasonal and annual flow discharges have decreasing [27] utilized SDSM and A2 and B2 scenarios in Wami-Ruvu River Basin of Tanzania and showed that rainfall and maximum temperature will increase and minimum temperature will reduce; Lima et al [28] uses of GCMs and the traditional Generalized Extreme Value (GEV) distribution for generation of Intensity-Duration-Frequency (IDF) curves in Korea and they showed that intensity of rainfalls with short durations and long return periods will increase in future; Agilan & Umamahesh [29] uses of GCMs and 'K' Nearest Neighbor (KNN) weather generator downscaling method in the Hyderabad city of India for generation of IDF curves and they showed that intensity of rainfalls will increase in future; Mailhot et al [30] Base Function (RBF) ANN for downscaling and concluded that rainfall intensity will increase for return periods less than 2.33 years while it will decrease for return periods more than 2.33 years. Sahraei et al [44]…”

Section: Introductionmentioning

confidence: 99%

Prediction of meteorological and hydrological phenomena by different climatic scenarios in the Karkheh watershed (south west of Iran)

2018

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This research evaluates effects of climatic change on future temperature, precipitation and flow discharge in the Karkheh watershed (a watershed in south west of Iran). For this purpose, it utilizes General Circulation Models (GCMs) and the non parametric Mann-Kendall (MK) trend test. Considered hydrometric station is the Jelogir station at the upstream of the Karkheh dam. Base time period is 1971-2014 and future time period is 2030-2073 for prediction of meteorological and hydrometric phenomena in the Jelogir station. For GCM model, the Canadian Climate Change Scenarios Network (CCCSN) database represents data 2 of HadCM3 model for A2 and B2 scenarios. For using in a watershed, this research applies SDSM downscaling model and introduces predicted precipitation and temperature of future time period to IHACRES model for prediction of flow discharge. Also the non parametric Mann-Kendall trend test and the Theil-Sen approach (TSA) estimator distinguishes trend of observed and predicted data. Results of scenarios A2 and B2 have not much difference. Different climatic scenarios show that temperature increases and precipitation and flow discharge decrease, also MK test and TSA estimator represent that slope of their variations will slow down in future and most of changes are related to winter and spring.

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Is the covariate based non-stationary rainfall IDF curve capable of encompassing future rainfall changes?

Cited by 52 publications

References 64 publications

Time‐varying extreme rainfall intensity‐duration‐frequency curves in a changing climate

Time‐varying extreme rainfall intensity‐duration‐frequency curves in a changing climate

Prediction vs. Performance of IDF Curves from Precipitation Data of Regional Climate Models for a Brazilian Coastal County

Prediction of meteorological and hydrological phenomena by different climatic scenarios in the Karkheh watershed (south west of Iran)

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