Application of the non-stationary peak-over-threshold methods for deriving rainfall extremes from temperature projections

Lee, Okjeong; Sim, Inkyeong; Kim, Sangdan

doi:10.1016/j.jhydrol.2019.124318

Cited by 33 publications

(23 citation statements)

References 110 publications

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“…Similar results can be obtained for different sites, different durations, or different return periods. In fact, such results that the reliability of the simulated (or estimated) future rainfall extremes from climate models are not high is not surprising (see [69] for further discussion).…”

Section: Future Idf Curvesmentioning

confidence: 92%

Uncertainty Quantification of Future Design Rainfall Depths in Korea

et al. 2019

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One of the most common ways to investigate changes in future rainfall extremes is to use future rainfall data simulated by climate models with climate change scenarios. However, the projected future design rainfall intensity varies greatly depending on which climate model is applied. In this study, future rainfall Intensity–Duration–Frequency (IDF) curves are projected using various combinations of climate models. Future Ensemble Average (FEA) is calculated using a total of 16 design rainfall intensity ensembles, and uncertainty of FEA is quantified using the coefficient of variation of ensembles. The FEA and its uncertainty vary widely depending on how the climate model combination is constructed, and the uncertainty of the FEA depends heavily on the inclusion of specific climate model combinations at each site. In other words, we found that unconditionally using many ensemble members did not help to reduce the uncertainty of future IDF curves. Finally, a method for constructing ensemble members that reduces the uncertainty of future IDF curves is proposed, which will contribute to minimizing confusion among policy makers in developing climate change adaptation policies.

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Section: Future Idf Curvesmentioning

confidence: 92%

Uncertainty Quantification of Future Design Rainfall Depths in Korea

et al. 2019

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“…Therefore, the change in the rainfall quantile, considering global warming, can be expressed explicitly. While rainfall extremes derived from climate models have significant bias and uncertainty, relatively reliable climate model outputs can be obtained for DPT (O'Gorman, 2012;Lenderink and Attema, 2015;Farnham et al, 2018). There- fore, it can be said that the nonstationary frequency analysis using DPT or SAT has an advantageous structure for examining the effect of global warming on the rainfall quantile (Wasko and Sharma, 2017;Lee et al, 2020).…”

Section: Uncertainty Analysismentioning

confidence: 99%

Uncertainty in nonstationary frequency analysis of South Korea's daily rainfall peak over threshold excesses associated with covariates

Lee

Choi

Won

et al. 2020

Hydrol. Earth Syst. Sci.

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Abstract. Several methods have been proposed to analyze the frequency of nonstationary anomalies. The applicability of the nonstationary frequency analysis has been mainly evaluated based on the agreement between the time series data and the applied probability distribution. However, since the uncertainty in the parameter estimate of the probability distribution is the main source of uncertainty in frequency analysis, the uncertainty in the correspondence between samples and probability distribution is inevitably large. In this study, an extreme rainfall frequency analysis is performed that fits the peak over threshold series to the covariate-based nonstationary generalized Pareto distribution. By quantitatively evaluating the uncertainty of daily rainfall quantile estimates at 13 sites of the Korea Meteorological Administration using the Bayesian approach, we tried to evaluate the applicability of the nonstationary frequency analysis with a focus on uncertainty. The results indicated that the inclusion of dew point temperature (DPT) or surface air temperature (SAT) generally improved the goodness of fit of the model for the observed samples. The uncertainty of the estimated rainfall quantiles was evaluated by the confidence interval of the ensemble generated by the Markov chain Monte Carlo. The results showed that the width of the confidence interval of quantiles could be greatly amplified due to extreme values of the covariate. In order to compensate for the weakness of the nonstationary model exposed by the uncertainty, a method of specifying a reference value of a covariate corresponding to a nonexceedance probability has been proposed. The results of the study revealed that the reference covariate plays an important role in the reliability of the nonstationary model. In addition, when the reference covariate was given, it was confirmed that the uncertainty reduction in quantile estimates for the increase in the sample size was more pronounced in the nonstationary model. Finally, it was discussed how information on a global temperature rise could be integrated with a DPT or SAT-based nonstationary frequency analysis. Thus, a method to quantify the uncertainty of the rate of change in future quantiles due to global warming, using rainfall quantile ensembles obtained in the uncertainty analysis process, has been formulated.

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“…In most studies investigating future rainfall extremes, it is assumed that future rainfall data simulated from global climate models (GCMs) or regional climate models (RCMs) are "observed" data in the future [1,2]. However, even recently developed climate models simulate temperature stably [3][4][5] but still expose many limitations to the simulation of rainfall extremes [6][7][8][9][10]. As can be found in the results of Kim et al [11], very different future projection outputs are obtained depending on which RCM-simulated future rainfall data are used to analyze rainfall extremes.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, they argued that it was not essential to adopt a non-stationary model. On the other hand, studies showing that a co-variate-based non-stationary frequency analysis is more appropriate to convincingly project rainfall extremes in the future have also been actively proposed [10,12,15,31,36].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty of Rate of Change in Korean Future Rainfall Extremes Using Non-Stationary GEV Model

et al. 2021

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Interest in future rainfall extremes is increasing, but the lack of consistency in the future rainfall extremes outputs simulated in climate models increases the difficulty of establishing climate change adaptation measures for floods. In this study, a methodology is proposed to investigate future rainfall extremes using future surface air temperature (SAT) or dew point temperature (DPT). The non-stationarity of rainfall extremes is reflected through non-stationary frequency analysis using SAT or DPT as a co-variate. Among the parameters of generalized extreme value (GEV) distribution, the scale parameter is applied as a function of co-variate. Future daily rainfall extremes are projected from 16 future SAT and DPT ensembles obtained from two global climate models, four regional climate models, and two representative concentration pathway climate change scenarios. Compared with using only future rainfall data, it turns out that the proposed method using future temperature data can reduce the uncertainty of future rainfall extremes outputs if the value of the reference co-variate is properly set. In addition, the confidence interval of the rate of change of future rainfall extremes is quantified using the posterior distribution of the parameters of the GEV distribution sampled using Bayesian inference.

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Application of the non-stationary peak-over-threshold methods for deriving rainfall extremes from temperature projections

Cited by 33 publications

References 110 publications

Uncertainty Quantification of Future Design Rainfall Depths in Korea

Uncertainty Quantification of Future Design Rainfall Depths in Korea

Uncertainty in nonstationary frequency analysis of South Korea's daily rainfall peak over threshold excesses associated with covariates

Uncertainty of Rate of Change in Korean Future Rainfall Extremes Using Non-Stationary GEV Model

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