Drought is ranked second in type of natural phenomena associated with billion dollars weather disaster during the past years. It is estimated that in EU countries the number of people affected by drought was increased by 20% over the last decades. It is widely recognized that the Standardized Precipitation Index (SPI) can effectively provide drought characteristics in time and space. The paper questions the standard approach to estimate the SPI based on the Gamma probability distribution function, assessing the fitting performance of different biparametric distribution laws to monthly precipitation data. We estimate SPI time series, for different scale of temporal aggregation, on an unprecedented dataset consisting of 332 rain gauge stations deployed across Italy with observations recorded between 1951 and 2000. Results show that the Lognormal distribution performs better than the Gamma in fitting the monthly precipitation data at all time scales, affecting drought characteristics estimated from SPI signals. However, drought events detected using the original and the best fitting approaches does not diverge consistently in terms of return period. This suggests that the SPI in its original formulation can be applied for a reliable detection of drought events and for promoting mitigation strategies over the Italian peninsula.

SPI-Based Drought Classification in Italy: Influence of Different Probability Distribution Functions

Benedetta Moccia
;
Elena Ridolfi;Fabio Russo;Francesco Napolitano
2022

Abstract

Drought is ranked second in type of natural phenomena associated with billion dollars weather disaster during the past years. It is estimated that in EU countries the number of people affected by drought was increased by 20% over the last decades. It is widely recognized that the Standardized Precipitation Index (SPI) can effectively provide drought characteristics in time and space. The paper questions the standard approach to estimate the SPI based on the Gamma probability distribution function, assessing the fitting performance of different biparametric distribution laws to monthly precipitation data. We estimate SPI time series, for different scale of temporal aggregation, on an unprecedented dataset consisting of 332 rain gauge stations deployed across Italy with observations recorded between 1951 and 2000. Results show that the Lognormal distribution performs better than the Gamma in fitting the monthly precipitation data at all time scales, affecting drought characteristics estimated from SPI signals. However, drought events detected using the original and the best fitting approaches does not diverge consistently in terms of return period. This suggests that the SPI in its original formulation can be applied for a reliable detection of drought events and for promoting mitigation strategies over the Italian peninsula.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1660704
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