Uncertainty Quantification in Rainfall Intensity Duration Frequency Curves Based on Historical Extreme Precipitation Quantiles

Huang, Yuk Feng; Mirzaei, Majid; Amin, Mohd Zaki Mat

doi:10.1016/j.proeng.2016.07.425

Cited by 20 publications

(8 citation statements)

References 16 publications

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“…To quantify the range of possible IDRP curve values generated in this study, confidence intervals (CIs) were calculated based on the estimation method of L moments [53] and the output of GEV distribution. A parametric bootstrap [54][55][56] was performed for historical and future IDRP curves to account for uncertainty.…”

Section: Idrp Methodologymentioning

confidence: 99%

Multi-Hazard Extreme Scenario Quantification Using Intensity, Duration, and Return Period Characteristics

Sfetsos,

Politi,

Vlachogiannis

2023

Climate

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Many modern frameworks for community resilience and emergency management in the face of extreme hydrometeorological and climate events rely on scenario building. These scenarios typically cover multiple hazards and assess the likelihood of their occurrence. They are quantified by their main characteristics, including likelihood of occurrence, intensity, duration, and spatial extent. However, most studies in the literature focus only on the first two characteristics, neglecting to incorporate the internal hazard dynamics and their persistence over time. In this study, we propose a multidimensional approach to construct extreme event scenarios for multiple hazards, such as heat waves, cold spells, extreme precipitation and snowfall, and wind speed. We consider the intensity, duration, and return period (IDRP) triptych for a specific location. We demonstrate the effectiveness of this approach by developing pertinent scenarios for eight locations in Greece with diverse geographical characteristics and dominant extreme hazards. We also address how climate change impacts the scenario characteristics.

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Section: Idrp Methodologymentioning

confidence: 99%

Multi-Hazard Extreme Scenario Quantification Using Intensity, Duration, and Return Period Characteristics

Sfetsos,

Politi,

Vlachogiannis

2023

Climate

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“…Typically, the uncertainty measure for the quantile estimate in the frequency analysis approach is provided as a confidence interval [45] and/or standard error [46]. A predefined probability distribution usually based on the modern extreme value theory is assumed to fit the historical data series (i.e., Gumbel, Frechet, Weibull, and generalized extreme value (GEV) to fit an AM series and generalized Pareto (GP) to fit a POT series) [9,29,45,[47][48][49][50][51]. Numerous approaches have been employed in the literature to generate confidence intervals, for example, using a formula that depends on the probability distributions and the parameter estimation techniques [52][53][54], using the profile-likelihood approach [7,32], using artificial neural networks [55], using deep learning method (such as the long short-term memory (LSTM) method) [56], Bayesian methods [48,50], Monte Carlo simulation methods [57,58], and bootstrap methods [45,49,51].…”

Section: Introductionmentioning

confidence: 99%

Assessment of the Uncertainty Associated with Statistical Modeling of Precipitation Extremes for Hydrologic Engineering Applications in Amman, Jordan

Ibrahim

2022

Sustainability

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Estimates of extreme precipitation are commonly associated with different sources of uncertainty. One of the primary sources of uncertainty in the statistical modeling of precipitation extremes comes from extreme data series (i.e., sampling uncertainty). Therefore, this research aimed to quantify the sampling uncertainty in terms of confidence intervals. In addition, this article examined how the data record length affects predicted extreme precipitation estimates and data set statistics. A nonparametric bootstrap resample was utilized to quantify the precipitation quantile sampling distribution at a particular non exceedance probability. This sampling distribution can provide a point estimation of the precipitation quantile and the confidence interval at a particular non exceedance probability. It has been shown that the different types of probability distributions fit the extreme precipitation data series of various weather stations. Therefore, the uncertainty analysis should be conducted using the best-fit probability distribution for extreme precipitation data series rather than a predefined single probability distribution for all stations based on modern extreme value theory. According to the 95% confidence intervals, precipitation quantiles are subject to significant uncertainty and the band of the uncertainty intervals increases with the return period. These uncertainty bounds need to be integrated into any frequency analysis from historical data. The average, standard deviation, skewness and kurtosis are highly affected by the data record length. Thus, a longer record length is desirable to decrease the sampling uncertainty and, therefore, decrease the error in the predicted quantile values. Moreover, the results suggest that a series of at least 40 years of data records is needed to obtain reasonably accurate estimates of the distribution parameters and the precipitation quantiles for 100 years return periods and higher. Using only 20 to 25 years of data to obtain estimates of the higher return period quantile is risky, since it created high sampling variability relative to the full data length.

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“…There is wealth of literature dealing with the Developing of IDF Curves/Equations in Nigeria. Some of the reviewed literature include [8][9][10][11][12][13][14][15][16], etc. and all the reviewed literature adopted Gumbel extreme type 1 (EV1) the default distribution in the development of IDF curves and models in Nigeria.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Generalized Extreme (GEV), Log-Pearson Type 3 (LP3), Pearson Type 3(P3) and Gumbel (EV1) Distributions for Development of IDF Equations for Warri, Nigeria

Ologhadien

2021

IJECC

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The application of Gumbel (EVI) to the development of rainfall intensity– duration – frequency (IDF) curves has often been criticized on theoretical and empirical grounds as it may underestimate the largest extreme rainfall amounts. The consequences of underestimation are economic losses, property damages, and loss of life. Therefore, it is important that water resources engineering infrastructure be accurately design to avoid these consequences. This paper evaluates the performances of four probability distributions; GEV, EV1, LP3 and P3 using the annual maxima precipitation series of 26 years for Warri Metropolis obtained from Nigerian Meteorological Agency (NiMet). The strength and weakness of the four probability distributions were examined with the goodness of fit (GOF) module of Easyfit software which implemented Kolmogorov - Smirnov (KS) and Anderson - Darling (AD) tests at 5% significance level. The Easyfit software fitted the precipitation series data to the four probability distributions and ranked the four probability distributions across the fifteen rainfall durations. Results show that for both KS and AD tests, GEV distribution was found to be best-fit distribution and it was applied to the development of IDF curves in Warri Metropolis, Nigeria. Furthermore, the IDF values obtained were applied in the development of three-parameter IDF models for return periods of 10 - , 15 -, 20 -, 25 - , 50 -, and 100-years. The mean absolute error, Nash – Sutcliffe Efficiency (NSE) and Root Mean Square Error (RMSE) indices computed for the IDF models increase with increasing return periods. The IDF curves and models depicted the general attributes of IDF curves and models. This study could be of significant academic value and improvement to professional practice in the design of storm water drainage systems. Therefore, the developed IDF curves and models are recommended to the Warri Urban Authority for inclusion in her stormwater handbooks and manuals.

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Uncertainty Quantification in Rainfall Intensity Duration Frequency Curves Based on Historical Extreme Precipitation Quantiles

Cited by 20 publications

References 16 publications

Multi-Hazard Extreme Scenario Quantification Using Intensity, Duration, and Return Period Characteristics

Multi-Hazard Extreme Scenario Quantification Using Intensity, Duration, and Return Period Characteristics

Assessment of the Uncertainty Associated with Statistical Modeling of Precipitation Extremes for Hydrologic Engineering Applications in Amman, Jordan

Evaluation of Generalized Extreme (GEV), Log-Pearson Type 3 (LP3), Pearson Type 3(P3) and Gumbel (EV1) Distributions for Development of IDF Equations for Warri, Nigeria

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