2019
DOI: 10.3390/w11122489
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Jointly Modeling Drought Characteristics with Smoothed Regionalized SPI Series for a Small Island

Abstract: The paper refers to a study on droughts in a small Portuguese Atlantic island, namely Madeira. The study aimed at addressing the problem of dependent drought events and at developing a copula-based bivariate cumulative distribution function for coupling drought duration and magnitude. The droughts were identified based on the Standardized Precipitation Index (SPI) computed at three and six-month timescales at 41 rain gauges distributed over the island and with rainfall data from January 1937 to December 2016. … Show more

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Cited by 14 publications
(7 citation statements)
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“…It is widely recommended that drought should be estimated from a regional perspective, because results from different case studies are not necessarily comparable with those from other regions [48,49]. In regional studies, drought indices should be standardized [50] accordingly, and the SPI index has this characteristic.…”
Section: Drought and Aridity Indicesmentioning
confidence: 99%
See 1 more Smart Citation
“…It is widely recommended that drought should be estimated from a regional perspective, because results from different case studies are not necessarily comparable with those from other regions [48,49]. In regional studies, drought indices should be standardized [50] accordingly, and the SPI index has this characteristic.…”
Section: Drought and Aridity Indicesmentioning
confidence: 99%
“…This distribution is sometimes used to determine the appropriate model according to the dataset [51,52]. The SPI is the most used drought indicator worldwide because of its applicability in all climate regimes [49]. Table 1 shows the classification of SPI values.…”
Section: Drought and Aridity Indicesmentioning
confidence: 99%
“…Thus, regional analysis has proven to be more efficient for drought management than the punctual approach [35,36]. Regionalization techniques are essential to reduce random fluctuations of a point-based approach and homogenizing drought analysis [37][38][39]. Clustering techniques that consider the point-wise correlation of a temporal series are essential and are increasing in use in hydrological applications [16,[40][41][42][43].…”
mentioning
confidence: 99%
“…The spatial interpolation throughout the river basin was performed using multi-dimension inverse distance weighting (IDW) in ArcMap by Environmental Systems Research Institute (ESRI) [21] to obtain the spatial distribution of SPI [22]. Other approaches to define spatial distribution include using principle component analysis for clustering homogenous regions based on SPI [23].…”
Section: Discussionmentioning
confidence: 99%