Delimiting homogeneous regions using the multifractal properties of validated rainfall data series

García‐Marín, A. P.; Estévez, Javier; Medina-Cobo, M. T.; Ayuso-Muñoz, J. L.

doi:10.1016/j.jhydrol.2015.07.021

Cited by 28 publications

(16 citation statements)

References 94 publications

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“…(11) Tarim River Basin (TRB). García-Marín et al, 2015;Nam et al, 2015). Smithers and Schulze (2001) applied RFA, which is based on the regional L-moment algorithm, to precipitation data in South Africa.…”

Section: Introductionmentioning

confidence: 99%

A regional frequency analysis of precipitation extremes in Mainland China with fuzzy c‐means and L‐moments approaches

Wang

Zeng

Lai

et al. 2017

Intl Journal of Climatology

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Owing to their essential influence on human society and natural environment exerted by inducing disasters, such as floods and droughts, further studies on precipitation extremes in China are needed. This study presents the regional frequency and spatial‐temporal patterns of precipitation extremes in China based on a high‐resolution (0.5° × 0.5°) daily precipitation dataset from 1961 to 2013. With fuzzy c‐means, L‐moments methods and other scientific statistical tests, a regional frequency analysis (RFA) is conducted, aiming to further understand the regional and spatial distribution of precipitation extremes across China. The results show that: (1) the whole Mainland China can be divided into 50 homogeneous regions on the basis of the characteristics of mean annual precipitation and location indices. (2) For most of the regions, Generalized Extreme Value (GEV), Generalized Normal (GNO) and Pearson type III (PE3) distributions of precipitation extremes fit well, according to the results of goodness‐of‐fit (GOF) test. (3) For RX1DAY, GEV has the best‐fit distribution in the east, northeast and southwest of China, whereas GNO distribution mostly fits the northern and parts of southwest and southeast; in addition, regions which fit PE3 and Generalized Logistic (GLO) distribute dispersedly across the country. (4) For RX5DAY, GEV mainly fits in the middle, southwestern and southern; GNO and PE3 apply best to the northeastern and northern, respectively. (5) Return periods of 20, 50 and 100 years for their best‐fit distributions decrease gradually from southeastern China to northwestern China. Compared with the results of GEV distribution fitted to each grid, RFA may provide more accurate estimates of rainfall quantiles. Definitely, the study results will not only benefit further understanding of the unique and complex features of extreme precipitation in the whole Mainland China but also contribute to the nation‐scale flood prevention, control and management in the backdrop of the changing climate.

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Section: Introductionmentioning

confidence: 99%

A regional frequency analysis of precipitation extremes in Mainland China with fuzzy c‐means and L‐moments approaches

Wang

Zeng

Lai

et al. 2017

Intl Journal of Climatology

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“…Several studies analysed the rainfall process in Spain from a fractal point of view: Oñate Rubalcaba () obtained a mean fractal dimension of 1.32 ± 0.01 for annual rainfall series recorded by 10 stations in the Iberian Peninsula, whereas Meseguer‐Ruiz et al () compared the fractal dimension of 10‐min rainfall data from 20 Spanish observatories with a concentration index (CI) expressing the relative weight of the rainiest days of a series on the total accumulated rainfall of that series. Other studies investigated the multifractal character of rainfall records in Spanish stations (Valencia et al , , ; García‐Marín et al , , ; Rodríguez et al , ).…”

Section: Introductionmentioning

confidence: 99%

A study of the scaling properties of rainfall in spain and its appropriateness to generate intensity‐duration‐frequency curves from daily records

Rodríguez‐Solà

Casas‐Castillo

Navarro

et al. 2016

Intl Journal of Climatology

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A methodology based on the fractal properties of rainfall has been applied to obtain the intensity-duration-frequency, IDF, curves for 100 pluviometric Spanish stations over the Iberian Peninsula and the Balearic Islands from their daily precipitation series. The scaling behaviour of maximum rainfall intensities has been investigated and simple scaling has resulted suitable. This methodology has been verified in three emblematic observatories with available sub-daily registers and current known generalized IDF relationships: the Fabra Observatory of Barcelona, the Ebre Observatory near Tortosa (Tarragona) and the Retiro Observatory of Madrid. Despite some general concordance with the mean annual rainfall distribution over Spain, the spatial distribution of the scaling parameter found for the 100 stations shows some discrepancies in diverse areas probably due to the influence of other features, as the inter-annual rainfall variability and the contribution of convective rainfall to total precipitation, on the characteristic rainfall pattern in these areas.Postprint (author's final draft

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“…In recent works, precipitation datasets from some of these stations have been used as quality records for different characterization analysis (García-Marín et al, 2015;Medina-Cobo et al, 2017) and to develop new validation procedures for rainfall data (Estévez et al, 2015).…”

Section: Andalusia Region (Southern Spain)mentioning

confidence: 99%

The history of rainfall data time-resolution in a wide variety of geographical areas

Morbidelli

García‐Marín

Mamun

et al. 2020

Journal of Hydrology

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Delimiting homogeneous regions using the multifractal properties of validated rainfall data series

Cited by 28 publications

References 94 publications

A regional frequency analysis of precipitation extremes in Mainland China with fuzzy c‐means and L‐moments approaches

A regional frequency analysis of precipitation extremes in Mainland China with fuzzy c‐means and L‐moments approaches

A study of the scaling properties of rainfall in spain and its appropriateness to generate intensity‐duration‐frequency curves from daily records

The history of rainfall data time-resolution in a wide variety of geographical areas

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