Sensitivity of SPI to Distribution Functions and Correlation Between its Values at Different Time Scales in Central Africa

Guenang, Guy Merlin; Komkoua, M. A. J.; Pokam, M. W.; Tanessong, Roméo S.; Tchakoutio, S. A.; Vondou, Derbetini A.; Tamoffo, Alain T.; Djiotang, Lucie A. Tchotchou; Mkankam, K. F.

doi:10.1007/s41748-019-00102-3

Cited by 20 publications

(41 citation statements)

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“…A biased drought description would result from an inadequacy of the fitted distribution function to describe precipitation. Such an inadequacy has been identified for the gamma (Guenang et al, 2019;Blain et al, 2018;Blain and Meschiatti, 2015;Stagge et al, 2015;Sienz et al, 2012;Touma et al, 2015;Naresh Kumar et al, 2009;Lloyd-Hughes and Saunders, 2002) as well as the Pearson type III distribution (Blain et al, 2018;Blain and Meschiatti, 2015;Stagge et al, 2015) in many parts of the world. This lead to the request for further investigations of candidate distribution functions (Blain et al, 2018;Blain and Meschiatti, 2015;Stagge et al, 2015;Touma et al, 2015;Sienz et al, 2012;Lloyd-Hughes and Saunders, 2002;Guttman, 1999).…”

Section: Introductionmentioning

confidence: 99%

“…The selected PDF, however, is of critical importance because the choice of this PDF is the key decision involved in the calculation of SPI and indeed many authors have urged to investigate the adequacy of distribution functions for new data-sets and regions before applying them (Blain et al, 2018;Stagge et al, 2015;Touma et al, 2015;Sienz et al, 2012). Such a negligence has potentially far-reaching consequences in terms of a biased drought description (Guenang et al, 2019;Sienz et al, 2012). A biased drought description would result from an inadequacy of the fitted distribution function to describe precipitation.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have investigated the adequacy of PDFs fitted onto observed precipitation while focusing on different candidate distribution functions (Blain and Meschiatti, 2015), different parameter estimation methods in the fitting procedure (Blain et al, 2018), different SPI time scales (Guenang et al, 2019), general drought climatology (Lloyd-Hughes and Saunders, 2002), and even the most appropriate methodology to test different candidate distribution functions (Stagge et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, some common conclusions can be drawn. Most investigations only analyzed 2-parameter distribution functions (Guenang et al, 2019;Blain et al, 2018;Stagge et al, 2015;Lloyd-Hughes and Saunders, 2002). Among those, they agreed depending on the accumulation period and/or the location either on the Weibull or the gamma distribution to be best suited in most cases.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, they advocated for a reevaluation of the widespread use of the 2-parameter gamma distribution (see also Wu et al, 2007). Moreover, a single candidate distribution function was neither suited in each location nor for each accumulation period to properly calculate SPI time series (Guenang et al, 2019;Blain et al, 2018;Stagge et al, 2015;Lloyd-Hughes and Saunders, 2002). Further, at the accumulation period of 3-months, a critical phase transition in precipitation totals seem to manifest which complicates the overall ranking of candidate PDFs (Guenang et al, 2019;Blain et al, 2018;Stagge et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Global and regional performances of SPI candidate distribution functions in observations and simulations

Pieper¹,

Düsterhus²,

Baehr³

2020

Preprint

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Abstract. The Standardized Precipitation Index (SPI) is a widely accepted drought index. Its calculation algorithm normalizes the index via a distribution function. Which distribution function to use is still disputed within literature. This study illuminates the long-standing dispute and proposes a solution which ensures the normality of the index for all common accumulation periods in observations and simulations. We compare the normality of SPI time-series derived with the gamma, Weibull, generalized gamma, and the exponentiated Weibull distribution. Our normality comparison evaluates actual against theoretical occurrence probabilities of SPI categories, and the quality of the fit of candidate distribution functions against their complexity with Akaike's Information Criterion. SPI time-series, spanning 1983–2013, are calculated from Global Precipitation Climatology Project's monthly precipitation data-set and seasonal precipitation hindcasts from the Max Planck Institute Earth System Model. We evaluate these SPI time-series over the global land area and for each continent individually during winter and summer. While focusing on an accumulation period of 3-months, we additionally test the drawn conclusions for other common accumulation periods (1-, 6-, 9-, and 12-months). Our results suggest to exercise caution when using the gamma distribution to calculate SPI; especially in simulations or their evaluation. Further, our analysis shows a distinctly improved normality for SPI time-series derived with the exponentiated Weibull distribution relative to other distributions. The use of the exponentiated Weibull distribution maximizes the normality of SPI time-series in observations and simulations both individual as well as concurrent. Its use further maximizes the normality of SPI time-series over each continent and for every investigated accumulation period. We, therefore, advocate to derive SPI with the exponentiated Weibull distribution, irrespective of the heritage of the precipitation data or the length of analyzed accumulation periods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Global and regional performances of SPI candidate distribution functions in observations and simulations

Pieper¹,

Düsterhus²,

Baehr³

2020

Preprint

View full text Add to dashboard Cite

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Performance evaluation of different probability distribution functions for computing Standardized Precipitation Index over diverse climates of Iran

Raziei

2021

Intl Journal of Climatology

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Monthly precipitation time series of 46 synoptic stations distributed over diverse climates of Iran were investigated to identify the best‐fitted probability distribution function (PDF) to the monthly precipitation series aggregated at different timescales. The computed skewness and the probability of zero precipitation (PZP) values of the considered precipitation aggregations show that a large proportion of monthly precipitation data in the study period, especially in the warm months of the year, is zero. The skewness of all 12 individual monthly precipitation time series as well as their aggregation at 3‐ and 6‐months timescales were also found very high in many studied stations, particularly in those of the arid and semi‐arid climates of Iran. To identify the best candidate distribution function for computing Standardized Precipitation Index (SPI) in different climatic areas of Iran, the performances of a dozen statistical PDFs in fitting monthly precipitation aggregated at different timescales were statistically evaluated. The results showed that the two‐parameter gamma distribution best fits monthly precipitation time series over most parts of the country while the Pearson Type III (PE3) was identified as the best‐fitted model for 3‐ and 6‐months aggregation periods and general extreme value (GEV) distribution as the best candidate function in fitting precipitation aggregated at 9‐, 12‐, and 24‐month timescales. PE3 was also identified as the second best‐fitted model for both the shorter time scale (1‐month) and the longer timescales (9‐, 12‐, and 24 months). Considering that PE3 ranked either first or second in fitting precipitation time series aggregated at different timescales and in all climatic areas of Iran, it is chosen as the most appropriate distribution function in fitting precipitation aggregated at all considered timescales throughout Iran.

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Drought variability, changes and hot spots across the African continent during the historical period (1928–2017)

Tall,

Sylla,

Dajuma

et al. 2023

Intl Journal of Climatology

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The spatiotemporal variability of meteorological droughts, its changes and hot spots location across Africa are analysed for the period spanning 1928–2017 using the Standardized Precipitation Index (SPI) applied to the precipitation products from the Climatic Research Unit (CRU), University of Delaware (UDEL) and Global Precipitation Climatology Centre (GPCC). Spatially, an analysis based on rotational empirical orthogonal function identifies five regions of similar drought variability, namely the Sahel, East Africa, East Southern Africa, West Southern Africa and the Gulf of Guinea. Temporally, the most common periods of drought occurrence are the 1970s, the 1980s and, to a lesser extent, the 1990s. Changes in drought characteristics for the intermediate past (1958–1987) and recent past (1988–2017) compared to the far past (1928–1957) indicate robust increases of drought duration, frequency and severity in the Sahel, and to a lower extent in the Gulf of Guinea, some areas of Central Africa, part of Southern Africa and over Madagascar. These changes are stronger (weaker) along the Sahel during the intermediate past (recent past) and stronger (weaker) over Central and Southern Africa and Madagascar during the recent past (intermediate past). As a consequence, drought hot spots, mostly driven by severity during the regions' wet season, are identified in areas confined in the Sahel during the intermediate past and in regions mainly over Central and Southern Africa and Madagascar during the recent past. Our results are useful for drought disaster risk management across Africa and provide a valuable reference for future drought analysis under global warming conditions.

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Sensitivity of SPI to Distribution Functions and Correlation Between its Values at Different Time Scales in Central Africa

Cited by 20 publications

References 42 publications

Global and regional performances of SPI candidate distribution functions in observations and simulations

Global and regional performances of SPI candidate distribution functions in observations and simulations

Performance evaluation of different probability distribution functions for computing Standardized Precipitation Index over diverse climates of Iran

Drought variability, changes and hot spots across the African continent during the historical period (1928–2017)

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