Advances in Deriving the Exact Distribution of Maximum Annual Daily Precipitation

Michele, Carlo De

doi:10.3390/w11112322

Cited by 19 publications

(13 citation statements)

References 53 publications

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“…As an alternative to the recently questioned EVT (De Michele, 2019;Veneziano et al, 2009), some studies are turning to non-extreme distributions (Papalexiou, 2018;Zaghloul et al, 2020) or proposing alternative distributions that relaxes the asymptotic assumptions, assuming a known parent distribution F(x) of ordinary rainfall events (Miniussi & Marani, 2020). Here, based on the recent finds of Papalexiou (2022) and Marra et al (2023) and on physical reasoning (Wilson & Toumi, 2005), we assumed (and further verified) that the tail of the distribution of rainfall events is stretched-exponential or follows a Weibull distribution tail (Equation 1).…”

Section: Parametric Approach-assessing Changes In Extreme Rainfall Ev...mentioning

confidence: 99%

Frequency Rather Than Intensity Drives Projected Changes of Rainfall Events in Brazil

Ballarin,

Wendland,

Zaerpour

et al. 2024

Earth's Future

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Extreme rainfall events are expected to intensify with global warming, posing significant challenges to both human and natural environments. Despite the importance of such assessments, they are unevenly widespread across the globe. Here, using bias corrected climate simulations of the latest phase of the Coupled Model Intercomparison Project (CMIP6), we provide a comprehensive assessment on how different rainfall events are expected to change across Brazil. Specifically, (a) we explored the projected changes in both intensity and frequency of rainfall events belonging to the right‐tail of the rainfall distribution using a non‐parametric approach, and (b) quantified how rainfall events associate with different return periods are expected to intensify, using a parametric approach. We found that extreme rainfall events will become more frequent and intense by the end of the century, with averaged projected changes for rainfall exceeding the historical rainfall quantile q0.99 of nearly 100% and 10% on frequency and intensity, respectively. Non‐extreme rainfall events, in contrast, are expected to be less frequent, aligning with the compensation hypothesis. For instance, Brazilian 100‐year rainfall are anticipated to intensify, on average, 17% and 31% under the moderate and the highest CMIP6 emission scenarios, respectively. Finally, our findings suggest that frequency, rather than intensity, dictates the projected changes of rainfall. We believe that the evidence gathered here will certainly contribute to not only an improved understanding of Brazilian rainfall events but also to a better comprehension of the different rainfall properties, their interplay and how the different ways of assessing them may affect climate studies.

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Section: Parametric Approach-assessing Changes In Extreme Rainfall Ev...mentioning

confidence: 99%

Frequency Rather Than Intensity Drives Projected Changes of Rainfall Events in Brazil

Ballarin,

Wendland,

Zaerpour

et al. 2024

Earth's Future

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“…The four distributions tested are the Gamma, the left-truncated Cauchy, the Uniform, and the Generalized Pareto. The Gamma distribution has a smoothly varying form and it is commonly adopted for description of skewed hydrological variables, such as precipitation depth of wet days (Chow et al 1988). For example, it is usually used to fit monthly aggregated precipitation data in the computation of the SPI.…”

Section: Setup Of Simulation Studymentioning

confidence: 99%

“…Upper extreme quantiles are used, for example, in the design of hydraulic structures. Structures like sewers and dams require design return periods between 2 and 10 years, and between 500 and 10000 years respectively (Chow et al 1988). Due to the limited length of time series, typically 50-90 years (Apipattanavis et al 2010), the quantile of interest is often not observed in the time series (Brazdil and Kundzewicz 2006).…”

Section: Introductionmentioning

confidence: 99%

Nonparametric extrapolation of extreme quantiles: a comparison study

Banfi

Cazzaniga

Michele

2021

Stoch Environ Res Risk Assess

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The extrapolation of quantiles beyond or below the largest or smallest observation plays an important role in hydrological practice, design of hydraulic structures, water resources management, or risk assessment. Traditionally, extreme quantiles are obtained using parametric methods that require to make an a priori assumption about the distribution that generated the data. This approach has several limitations mainly when applied to the tails of the distribution. Semiparametric or nonparametric methods, on the other hand, allow more flexibility and they may overcome the problems of the parametric approach. Therefore, we present here a comparison between three selected semi/nonparametric methods, namely the methods of Hutson (Stat and Comput, 12(4):331–338, 2002) and Scholz (Nonparametric tail extrapolation. Tech. Rep. ISSTECH-95-014, Boeing Information and Support Services, Seattle, WA, United States of America, 1995) and kernel density estimation. While the first and third methods have already applications in hydrology, Scholz (Nonparametric tail extrapolation. Tech. Rep. ISSTECH-95-014, Boeing Information and Support Services, Seattle, WA, United States of America, 1995) is proposed in this context for the first time. After describing the methods and their applications in hydrology, we compare their performance for different sample lengths and return periods. We use synthetic samples extracted from four distributions whose maxima belong to the Gumbel, Weibull, and Fréchet domain of attraction. Then, the same methods are applied to a real precipitation dataset and compared with a parametric approach. Eventually, a detailed discussion of the results is presented to guide researchers in the choice of the most suitable method. None of the three methods, in fact, outperforms the others; performances, instead, vary greatly with distribution type, return period, and sample size.

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“…Note that the methodology discussed in our article, can be applied to other statistical distributions related to the extreme value analysis. On this regard, we mention two articles [13,14]. In [13], a useful software for regional and at-site statistical analyses of annual maxima time series is proposed.…”

Section: Introductionmentioning

confidence: 99%

“…The software showed good performance with the annual maxima series for the Italian rain-gauge network by implementing Gumbel, GEV and Two Component Extreme Value distributions. In [14], the results about the derivation of the exact distribution of maximum annual daily precipitation are summarized and discussed, with special attention on compound/super statistical distributions. The results have a 20% higher accuracy in terms of the Root Mean Square Error (RMSE) value, compared to the Naive approach [11].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Parameter Estimation of the Generalized Extreme Value Distribution Using Artificial Neural Network Approach

et al. 2023

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Parameter estimation strategies have long been a focal point in research due to their significant implications for understanding data behavior, including the dynamics of big data. This study offers an advancement in these strategies by proposing an adaptive parameter estimation approach for the Generalized Extreme Value distribution (GEVD) using an artificial neural network (ANN). Through the proposed adaptive parameter estimation approach, based on ANNs, this study addresses the parameter estimation challenges associated with the GEVD. By harnessing the power of ANNs, the proposed methodology provides an innovative and effective solution for estimating the parameters of the GEVD, enhancing our understanding of extreme value analysis. To predict the flood risk areas in the Chi river watershed in Thailand, we first determine the variables that are significant in estimation of the three GEVD parameters μ,σ, and ξ by considering the respective correlation coefficient and then estimating these parameters. The data were compiled from satellite and meteorological data in the Chi watershed gathered from the Meteorological Department and 92 meteorological stations from 2010 to 2021, and consist of such variables as the Normalized Difference Vegetation Index (NDVI), climate, rainfall, runoff, and so on. The parameter estimation focuses on the GEVD. Taking into consideration that the processes could be stationary (parameters are constant over time, S) or non-stationary (parameters change over time, NS), maximum likelihood estimation and ANN approaches are applied, respectively. Both cases are modeled with the GEVD for the monthly maximum rainfall. The Nash-Sutcliffe coefficient (NSE), is used to compare the performance and accuracy of the models. The results illustrate that the non-stationary model was suitable for 82 stations, while the stationary model was suitable for only 10 stations. The NSE values in each model range from 0.6 to 0.9. This indicated that all 92 models were highly accurate. Furthermore, it is found that meteorological variables, geographical coordinates, and NDVI, that are correlated with the shape parameter in the ANN model, are more significant than others. Finally, two-dimensional maps of the return levels in the 2, 5, 10, 20, 50, and 100-year return periods are presented for further application. Overall, this study contributes to the advancement of parameter estimation strategies in the context of extreme value analysis and offers practical implications for water resource management and flood risk mitigation.

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Advances in Deriving the Exact Distribution of Maximum Annual Daily Precipitation

Cited by 19 publications

References 53 publications

Frequency Rather Than Intensity Drives Projected Changes of Rainfall Events in Brazil

Frequency Rather Than Intensity Drives Projected Changes of Rainfall Events in Brazil

Nonparametric extrapolation of extreme quantiles: a comparison study

Adaptive Parameter Estimation of the Generalized Extreme Value Distribution Using Artificial Neural Network Approach

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