Droughts Prediction: a Methodology Based on Climate Seasonal Forecasts

E. Arnone (), Marco Cucchi, Sara Dal Gesso, Marcello Petitta and Sandro Calmanti
Additional contact information
E. Arnone: Università degli Studi di Udine
Marco Cucchi: University of Reading
Sara Dal Gesso: Amigo s.r.l.
Marcello Petitta: ENEA, SSPT-MET-CLIM
Sandro Calmanti: ENEA, SSPT-MET-CLIM

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2020, vol. 34, issue 14, No 2, 4313-4328

Abstract: Abstract This study proposes a methodology for the drought assessment based on the seasonal forecasts. These are climate predictions of atmospheric variables, such as precipitation, temperature, wind speed, for upcoming season, up to 7 months. In regions particularly vulnerable to droughts and to changes in climate, such as the Mediterranean areas, predictions of precipitation with months in advance are crucial for understanding the possible shifts, for example, in water resource availability. Over Europe, practical applications of seasonal forecasts are still rare, because of the uncertainties of their skills; however, the predictability varies depending on the season and area of application. In this study, we describe a methodology which integrates, through a statistical approach, seasonal forecast and reanalysis data to assess the climate state, i.e. drought or not, of a region for predefined periods in the next future, at monthly scale. Additionally, the skill of the forecasts and the reliability of the released climate state assessment are estimated in terms of the false rate, i.e. the probability of missing alerts or false alarms. The methodology has been first built for a case study in Zakynthos (Greece) and then validated for a case study in Sicily (Italy). The selected locations represent two areas of the Mediterranean region often suffering from drought and water shortage situations. Results showed promising findings, with satisfying matching between predictions and observations, and false rates ranging from 1 to 50%, depending on the selected forecast period.

Keywords: Climate; Drought; Seasonal forecasts; System 5; Water resources (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11269-020-02623-3

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