SPI drought class predictions driven by the North Atlantic Oscillation index using log-linear modeling

Elsa E. Moreira, Carlos L. Pires, Luís S. Pereira

Research output: Contribution to journalArticle

11 Citations (Scopus)

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 theWestern 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).

Original languageEnglish
Article number43
JournalWater (Switzerland)
Volume8
Issue number2
DOIs
Publication statusPublished - 2016

Keywords

  • 3-dimensional log-linear models
  • Confidence intervals
  • Drought class transitions
  • Odds

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