Probabilistic forecasting of drought class transitions in Sicily (Italy) using Standardized Precipitation Index and North Atlantic Oscillation Index

Bonaccorso, Brunella; Cancelliere, Antonino; Rossi, Giuseppe

doi:10.1016/j.jhydrol.2015.01.070

Cited by 83 publications

(52 citation statements)

References 54 publications

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“…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].…”

Section: Introductionmentioning

confidence: 99%

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

Moreira

Russo

Trigo

2018

Water

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Drought class transitions over a sector of Eastern Europe were modeled using log-linear models. These drought class transitions were computed from time series of two widely used multiscale drought indices, the Standardized Preipitation Evapotranspiration Index (SPEI) and the Standardized Precipitation Index (SPI), with temporal scales of 6 and 12 months for 15 points selected from a grid over the Prut basin in Romania over a period of 112 years . The modeling also took into account the impact of North Atlantic Oscillation (NAO), exploring the potential influence of this large-scale atmospheric driver on the climate of the Prut region. To assess the probability of transition among different drought classes we computed their odds and the corresponding confidence intervals. To evaluate the predictive capabilities of the modeling, skill scores were computed and used for comparison against benchmark models, namely using persistence forecasts or modeling without the influence of the NAO index. The main results indicate that the log-linear modeling performs consistently better than the persistence forecast, and the highest improvements obtained in the skill scores with the introduction of the NAO predictor in the modeling are obtained when modeling the extended winter months of the SPEI6 and SPI12. The improvements are however not impressive, ranging between 4.7 and 6.8 for the SPEI6 and between 4.1 and 10.1 for the SPI12, in terms of the Heidke skill score.

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Section: Introductionmentioning

confidence: 99%

“…Some examples of this type of approach include the use of ANN models and time series of drought indices, including the NAO index as a covariate [68], or the use of probabilistic models resulting from evaluating conditional probabilities of future SPI classes based on current SPI and NAO classes [67].…”

Section: Introductionmentioning

confidence: 99%

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

Moreira

Russo

Trigo

2018

Water

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“…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.…”

Section: Discussionmentioning

confidence: 99%

“…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).…”

Section: Introductionmentioning

confidence: 99%

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

Liu

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. Reliable drought prediction is fundamental for water resource managers to develop and implement drought mitigation measures. Considering that drought development is closely related to the spatial-temporal evolution of largescale circulation patterns, we developed a conceptual prediction model of seasonal drought processes based on atmospheric and oceanic standardized anomalies (SAs). Empirical orthogonal function (EOF) analysis is first applied to drought-related SAs at 200 and 500 hPa geopotential height (HGT) and sea surface temperature (SST). Subsequently, SA-based predictors are built based on the spatial pattern of the first EOF modes. This drought prediction model is essentially the synchronous statistical relationship between 90-day-accumulated atmospheric-oceanic SA-based predictors and SPI3 (3-month standardized precipitation index), calibrated using a simple stepwise regression method. Predictor computation is based on forecast atmospheric-oceanic products retrieved from the NCEP Climate Forecast System Version 2 (CFSv2), indicating the lead time of the model depends on that of CFSv2. The model can make seamless drought predictions for operational use after a year-to-year calibration. Model application to four recent severe regional drought processes in China indicates its good performance in predicting seasonal drought development, despite its weakness in predicting drought severity. Overall, the model can be a worthy reference for seasonal water resource management in China.

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“…The methodologies include regression analysis [33], time series modeling such as ARIMA and seasonal ARIMA [34,35], artificial neural network models (ANN) [36,37] and stochastic and probability models such as Markov chains [38][39][40], log-linear models [31,41] and others [42,43]. Also, hybrid models combining two techniques have been used, for instance wavelet transforms and neural networks [44], stochastic and neural network modeling [45], wavelet and fuzzy logic models [46], adaptive neuro-fuzzy inference [47] and data mining and ANFIS techniques [48].…”

Section: Introductionmentioning

confidence: 99%

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

Moreira

Pires

Pereira

2016

Water

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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 . 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).

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Probabilistic forecasting of drought class transitions in Sicily (Italy) using Standardized Precipitation Index and North Atlantic Oscillation Index

Cited by 83 publications

References 54 publications

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

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

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

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

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