Computation of Drought Index SPI with Alternative Distribution Functions

Angelidis, Panagiotis; Maris, Fotios; Kotsovinos, Nikolaos; Hrissanthou, Vlassios

doi:10.1007/s11269-012-0026-0

Cited by 185 publications

(105 citation statements)

References 30 publications

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“…While the 3-and 6-month SPI describe droughts that affect plant life and farming, the 12-and 24-month SPI influence the way water supplies and reserves are managed [50,51]. Angelidis et al [52] offered a meticulous description of the method to compute the SPI.…”

Section: Methodsmentioning

confidence: 99%

Drought Analysis in Europe and in the Mediterranean Basin Using the Standardized Precipitation Index

Caloiero

Veltri²,

Caloiero³

et al. 2018

Water

100

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Abstract:In this study, drought events over a large area of the Northern Hemisphere, including continental Europe, Ireland, the United Kingdom, and the Mediterranean basin, were analyzed using the Standardized Precipitation Index (SPI) at various times scales (3, 6, 12, and 24 months). To this purpose, the Global Precipitation Climatology Centre (GPCC) Full Data Monthly Product Version 2018 data set, with spatial resolutions of 0.5 • longitude/latitude and for the period 1951-2016, has been used. First, the temporal evolution of the percentage of grid points, falling within the severe and extreme drought categories, has been evaluated. Then, a trend analysis has been performed at a seasonal scale, considering the autumn-winter and the spring-summer periods, and at an annual scale. The results of this paper highlight that the Mediterranean basin and North Africa are the most consistently vulnerable areas showing a general reduction in SPI values especially for the long time scale.

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Section: Methodsmentioning

confidence: 99%

Drought Analysis in Europe and in the Mediterranean Basin Using the Standardized Precipitation Index

Caloiero

Veltri²,

Caloiero³

et al. 2018

Water

100

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“…precipitation); (ii) it can be estimated for a variety of timescales (by calculating using precipitation data for a range of accumulation periods); (iii) it is relatively simple compared with other widely used indices such as the Palmer Drought Severity Index, PDSI (Palmer, 1965);and (iv) it is spatially constant, again unlike the PDSI. A number of potential disadvantages related to SPI have also been recognised, including: (i) the assumption that suitable probability distributions can be found to model the observed precipitation time series (Guttmann, 1999;Wu et al, 2007;Angelidis et al, 2012); (ii) the requirement for a long precipitation time series (Guttmann, 1999); (iii) the need for time series of consistent length where multiple sites are being evaluated and compared (Wu et al, 2005); and (iv) in regional analyses where the aim is to identify areas that may be more drought prone than others, extreme droughts measured by SPI will tend to occur with the same frequency at all locations as the timescale of analyses increases (Lloyd- Hughes and Saunders, 2002).…”

Section: Context For Development Of the Sgimentioning

confidence: 99%

Analysis of groundwater drought building on the standardised precipitation index approach

Bloomfield

Marchant

2013

Hydrol. Earth Syst. Sci.

341

368

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Abstract.A new index for standardising groundwater level time series and characterising groundwater droughts, the Standardised Groundwater level Index (SGI), is described. The SGI builds on the Standardised Precipitation Index (SPI) to account for differences in the form and characteristics of groundwater level and precipitation time series. The SGI is estimated using a non-parametric normal scores transform of groundwater level data for each calendar month. These monthly estimates are then merged to form a continuous index. The SGI has been calculated for 14 relatively long, up to 103 yr, groundwater level hydrographs from a variety of aquifers and compared with SPI for the same sites. The relationship between SGI and SPI is site specific and the SPI accumulation period which leads to the strongest correlation between SGI and SPI, q max , varies between sites. However, there is a consistent positive linear correlation between a measure of the range of significant autocorrelation in the SGI series, m max , and q max across all sites. Given this correlation between SGI m max and SPI q max , and given that periods of low values of SGI can be shown to coincide with previously independently documented droughts, SGI is taken to be a robust and meaningful index of groundwater drought. The maximum length of groundwater droughts defined by SGI is an increasing function of m max , meaning that relatively long groundwater droughts are generally more prevalent at sites where SGI has a relatively long autocorrelation range. Based on correlations between m max , average unsaturated zone thickness and aquifer hydraulic diffusivity, the source of autocorrelation in SGI is inferred to be dependent on dominant aquifer flow and storage characteristics. For fractured aquifers, such as the Cretaceous Chalk, autocorrelation in SGI is inferred to be primarily related to autocorrelation in the recharge time series, while in granular aquifers, such as the Permo-Triassic sandstones, autocorrelation in SGI is inferred to be primarily a function of intrinsic saturated flow and storage properties of aquifer. These results highlight the need to take into account the hydrogeological context of groundwater monitoring sites when designing and interpreting data from groundwater drought monitoring networks.

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“…Similarly, for the Capo Spartivento rain gauge (Figure 4b), the extreme and moderate probabilities differed from the correspondent SPI values proposed in Table 1, while only the severe probabilities agree with them. This is very important, since for most practical applications the SPI is essential in terms of classification (Table 1) and not as arithmetic values [87].…”

Section: Resultsmentioning

confidence: 99%

“…Indeed, it is generally agreed that the SPI on short-term scales (e.g., 3 or 6 months) describes drought affecting vegetation and agricultural practices, while on long-term scales (e.g., 12 or 24 months) it is a broad proxy for water resource management [40][41][42]. A detailed description of the SPI has been proposed previously [87]. Although a classification restricted only to drought periods has been originally proposed [86], it has become customary to use the index to classify wet periods as well.…”

Section: Standardized Precipitation Indexmentioning

confidence: 99%

An Analysis of the Occurrence Probabilities of Wet and Dry Periods through a Stochastic Monthly Rainfall Model

Caloiero

Sirangelo

Coscarelli

et al. 2016

Water

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Stochastic simulators can effectively generate the intrinsic variability of the rainfall process, which is an important issue in the analysis of the projections uncertainties. In this paper, a procedure for stochastic modeling of precipitation at monthly scale is proposed. The model adopts variable transformations, which are finalized to the deseasonalization and the Gaussianization of the monthly rainfall process, and includes a procedure for testing the autocorrelation. The model was applied to a homogeneous database of monthly rainfall values registered in 12 rain gauges in the region of Calabria (Southern Italy). After the estimation of the model parameters, a set of 10 4 years of monthly rainfall for each rain gauge was generated by means of a Monte Carlo technique. Then, dry and wet periods were analyzed through the application of the standardized precipitation index (SPI). Some results, confirmed through the application of the drought severity index (DSI), showed that the proposed model provided a good representation of the monthly rainfall for the considered rain gauges. Moreover, the results of the SPI application indicate a greater probability of dry conditions than wet conditions, especially when long-term precipitation patterns are considered.

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Computation of Drought Index SPI with Alternative Distribution Functions

Cited by 185 publications

References 30 publications

Drought Analysis in Europe and in the Mediterranean Basin Using the Standardized Precipitation Index

Drought Analysis in Europe and in the Mediterranean Basin Using the Standardized Precipitation Index

Analysis of groundwater drought building on the standardised precipitation index approach

An Analysis of the Occurrence Probabilities of Wet and Dry Periods through a Stochastic Monthly Rainfall Model

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