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

Abstract

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 . 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).

Monthly Prediction of Drought Classes Using Log-Linear Models under the Influence of NAO for Early-Warning of Drought and Water Management

“…Taking into account the NAO state in the current month, the most probable drought class transition for the next month and their confidence intervals are computed. This approach has proved its usefulness in predicting the SPI drought class one or two months in advance [64,65,69].…”

“…Among the techniques used for drought forecasting, statistical models are chosen many times, since they are simple to implement, do not have a high computational burden, and produce useful predictions [58]. There are a variety of statistical methodologies available which can be applied for the intended purpose, namely autoregressive integrated moving average (ARIMA)-type approaches [59,60], artificial neural network (ANN) models [61,62] or even other types of stochastic and probability models, such as Markov chains [63], log-linear models [64,65], and others [66,67]. A thorough discussion on various methodologies used for drought modeling and prediction showing the limitations and advantages of each modeling/technique was done by Mishra and Singh [58].…”

A conceptual prediction model for seasonal drought processes using atmospheric and oceanic standardized anomalies: application to regional drought processes in China

“…To date, a considerable number of studies have focused on predicting discrete drought classes (Aviles et al, 2016;Bonaccorso et al, 2015;Chen et al, 2013;Moreira et al, 2016) and the probability of drought occurrence within certain classes (AghaKouchak, 2014(AghaKouchak, , 2015Hao et al, 2014). Compared with these studies, prediction Figure 12.…”

“…In addition to method improvement, climate indices represent large-scale atmospheric or oceanic drivers of precipitation, partly responsible for effective model performance. These climate indices include typical atmospheric and oceanic circulation patterns, such as the North Atlantic Oscillation (NAO; Hurrell, 1995) and El Niño-Southern Oscillation (ENSO; Ropelewski and Halpert, 1987), which have been widely used for drought prediction in different seasons and regions (Behrangi et al, 2015;Bonaccorso et al, 2015;Chen et al, 2013;Mehr et al, 2014;Moreira et al, 2016).…”

Drought monitoring in the Seybouse basin (Algeria) over the last decades

“…Since 1900, several indices were developed to identify and to assess the severity of drought, such as the rainfall anomaly index (RAI) [ VAN ROOY 1965], the Palmer drought severity index (PDSI) [PALMER 1965], and the standardized precipitation index (SPI) [MCKEE et al 1993;1995] this latter is recommended by many organizations such as the World Meteorological Organization (WMO) and the United States National Oceanic and Atmospheric Administration (NOAA) for characterizing meteorological droughts as well as the other categories of droughts [MOREIRA et al 2016] due to its simplicity ,its robustness, and flexibility for drought analysis as it can be used at different time scales (e.g., weekly, monthly, yearly) .…”