2019
DOI: 10.3390/w11102026
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A Stochastic Approach for the Analysis of Long Dry Spells with Different Threshold Values in Southern Italy

Abstract: A non-homogeneous Poisson model was proposed to analyze the sequences of dry spells below prefixed thresholds as an upgrade of a stochastic procedure previously used to describe long periods of no rainfall. Its application concerned the daily precipitation series in a 60-year time span at four rain gauges (Calabria, southern Italy), aiming at testing the different behaviors of the dry spells below prefixed thresholds in two paired periods (1951–1980 and 1981–2010). A simulation analysis performed through a Mon… Show more

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Cited by 7 publications
(5 citation statements)
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“…The stochastic models used to study dry spells can be subdivided into two different categories: driven data models (e.g., the non-homogeneous Poisson model), which reproduce the primary characteristics of the available data series, and physically based models (e.g., the Markov chain model), which schematize the generating mechanisms of atmospheric precipitation [5]. The Markov chain probabilistic model is widely used to determine the relative chance of occurrence of a given rainfall to characterize a rainfall period as a dry or wet spell [5][6][7][8][9][10][11][12][13][14][15]. This model is also useful for agricultural water management to determine the onset and end of the rainy season, which largely determines the success of rainfed agriculture [16,17].…”
Section: Introductionmentioning
confidence: 99%
“…The stochastic models used to study dry spells can be subdivided into two different categories: driven data models (e.g., the non-homogeneous Poisson model), which reproduce the primary characteristics of the available data series, and physically based models (e.g., the Markov chain model), which schematize the generating mechanisms of atmospheric precipitation [5]. The Markov chain probabilistic model is widely used to determine the relative chance of occurrence of a given rainfall to characterize a rainfall period as a dry or wet spell [5][6][7][8][9][10][11][12][13][14][15]. This model is also useful for agricultural water management to determine the onset and end of the rainy season, which largely determines the success of rainfed agriculture [16,17].…”
Section: Introductionmentioning
confidence: 99%
“…In this context we may also refer to [6,[8][9][10][11][12][13] and [14]. Some researchers are in the opinion that these rainfall models, although statistically fit to the observed (actual or empirical) frequency distributions of spell lengths, have a relatively short memory so that they may limit the model's ability to reproduce adequately long/short dry spells as well as inter-annual variability, see for example, [2,[15][16][17][18] and [19]. These limitations have encouraged researchers to use some efficient alternatives to the MC probabilistic models to analyse variability of dry spell characteristics including the persistence and the pattern of dry periods.…”
Section: Introductionmentioning
confidence: 99%
“…It can be subdivided into driven data models (e.g., the nonhomogeneous Poisson model), which reproduce the primary characteristics of the available data series, and physically based models (e.g., the Markov chain model), which schematize the generating mechanisms of atmospheric precipitation (Sirangelo et al, 2019). The Markov chain probabilistic model is widely used to determine the relative chance of occurrence of a given rainfall to characterize a rainfall period as a dry or wet spell (Gregory et al, 1993;Sirangelo et al, 2019;Adane et al, 2020). Reddy (1990) stated that 3mm rainfall depth per day is the minimum threshold value for crops to satisfy their crop water requirement during a growing season.…”
Section: Introductionmentioning
confidence: 99%
“…As supplementary objective, the evaluation of different dry spells behavior with ve distingue thresholds value. This can be considered as an upgrade of the one proposed by Sirangelo et al (2015) and Sirangelo et al, (2019) for the investigation of dry spells de ned as the number of consecutive days without precipitation.…”
Section: Introductionmentioning
confidence: 99%