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Water 2016, 8(2), 43; doi:10.3390/w8020043

SPI Drought Class Predictions Driven by the North Atlantic Oscillation Index Using Log-Linear Modeling

1
Center of Mathematics and Applications (CMA), Faculty of Sciences and Technology, New University of Lisbon, Caparica 2829-516, Portugal
2
Instituto Dom Luiz (IDL), Faculty of Sciences, University of Lisbon, Lisboa 1749-016, Portugal
3
Research Center for Landscape, Environment, Agriculture and Food (LEAF), Institute of Agronomy, University of Lisbon, Lisbon 1349-017, Portugal
*
Author to whom correspondence should be addressed.
Academic Editor: Athanasios Loukas
Received: 13 October 2015 / Revised: 26 January 2016 / Accepted: 27 January 2016 / Published: 30 January 2016
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Abstract

This study aims at predicting the Standard Precipitation Index (SPI) drought class transitions in Portugal, considering the influence of the North Atlantic Oscillation (NAO) as one of the main large-scale atmospheric drivers of precipitation and drought fields across the Western European and Mediterranean areas. Log-linear modeling of the drought class transition probabilities on three temporal steps (dimensions) was used in an SPI time series of six- and 12-month time scales (SPI6 and SPI12) obtained from Global Precipitation Climatology Centre (GPCC) precipitation datasets with 1.0 degree of spatial resolution for 10 grid points over Portugal and a length of 112 years (1902–2014). The aim was to model two monthly transitions of SPI drought classes under the influence of the NAO index in its negative and positive phase in order to obtain improvements in the predictions relative to the modeling not including the NAO index. The ratios (odds ratio) between transitional probabilities and their confidence intervals were computed in order to estimate the probability of one drought class transition over another. The prediction results produced by the model with the forcing of NAO were compared with the results produced by the same model without that forcing, using skill scores computed for the entire time series length. Overall results have shown good prediction performance, ranging from 73% to 76% in the percentage of corrects (PC) and 56%–62% in the Heidke skill score (HSS) regarding the SPI6 application and ranging from 82% to 85% in the PC and 72%–76% in the HSS for the SPI12 application. The model with the NAO forcing led to improvements in predictions of about 1%–6% (PC) and 1%–8% (HSS), when applied to SPI6, but regarding SPI12 only seven of the locations presented slight improvements of about 0.4%–1.8% (PC) and 0.7%–3% (HSS). View Full-Text
Keywords: 3-dimensional log-linear models; drought class transitions; odds; confidence intervals 3-dimensional log-linear models; drought class transitions; odds; confidence intervals
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Moreira, E.E.; Pires, C.L.; Pereira, L.S. SPI Drought Class Predictions Driven by the North Atlantic Oscillation Index Using Log-Linear Modeling. Water 2016, 8, 43.

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