Drought Forecasting Using the Standardized Precipitation Index

University of Catania, Catania, Sicily, Italy
Water Resources Management (Impact Factor: 2.6). 05/2007; 21(5):801-819. DOI: 10.1007/s11269-006-9062-y


Unlike other natural disasters, drought events evolve slowly in time and their impacts generally span a long period of time.
Such features do make possible a more effective drought mitigation of the most adverse effects, provided a timely monitoring
of an incoming drought is available.

Among the several proposed drought monitoring indices, the Standardized Precipitation Index (SPI) has found widespread application
for describing and comparing droughts among different time periods and regions with different climatic conditions. However,
limited efforts have been made to analyze the role of the SPI for drought forecasting.

The aim of the paper is to provide two methodologies for the seasonal forecasting of SPI, under the hypothesis of uncorrelated
and normally distributed monthly precipitation aggregated at various time scales k. In the first methodology, the auto-covariance matrix of SPI values is analytically derived, as a function of the statistics
of the underlying monthly precipitation process, in order to compute the transition probabilities from a current drought condition
to another in the future. The proposed analytical approach appears particularly valuable from a practical stand point in light
of the difficulties of applying a frequency approach due to the limited number of transitions generally observed even on relatively
long SPI records. Also, an analysis of the applicability of a Markov chain model has revealed the inadequacy of such an approach,
since it leads to significant errors in the transition probability as shown in the paper. In the second methodology, SPI forecasts
at a generic time horizon M are analytically determined, in terms of conditional expectation, as a function of past values of monthly precipitation.
Forecasting accuracy is estimated through an expression of the Mean Square Error, which allows one to derive confidence intervals
of prediction. Validation of the derived expressions is carried out by comparing theoretical forecasts and observed SPI values
by means of a moving window technique. Results seem to confirm the reliability of the proposed methodologies, which therefore
can find useful application within a drought monitoring system.

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Available from: Brunella Bonaccorso, Sep 29, 2015
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    • "Meanwhile, the TCCs of the winter corrected NAO are 0.46 and 0.54 based on ECMWF and CNRM, compared with TCCs of 0.34 and 0.46 based on ECMWF and CNRM for the Scheme-I, respectively (see Table 1 and Table 2). In order to accurately assess predictive skill of the Scheme-II for the two coupled models of DEMETER, the 95% prediction intervals are added (Cancelliere et al., 2007), and almost all the observed NAO index fall within the predictive range, suggesting better prediction skill (Figure 7). This also illustrates that the prediction skill of Scheme-II is higher than Scheme-I for both ECMWF and CNRM based two NAO indices, which could be attributed to the NAO/SST association included in Scheme-II. "
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    ABSTRACT: The winter North Atlantic Oscillation (NAO) is a crucial part of our understanding of Eurasian and Atlantic climate variability and predictability. In this study, we developed effective prediction schemes based on the interannual increment prediction method and verified their performance based on the climate hindcasts of the coupled ocean–atmosphere climate models. This approach utilizes the year-to-year increment of a variable (i.e. a difference in a variable between the current year and the previous year, e.g. DY of a variable) as the predictand rather than the anomaly of the variable. The results demonstrate that the new schemes can generally improve prediction skill of the winter NAO compared to the raw coupled model's output. Also, the new schemes show higher skill in prediction of abnormal NAO cases than the climatological prediction. Scheme-I uses just the NAO in the form of year-to-year increments as a predictor that is derived from the direct outputs of the models. Scheme-II is a hybrid prediction model that contains two predictors: the NAO derived from the coupled models and the observed preceding autumn Atlantic sea surface temperature in the form of year-to-year increments. Scheme-II shows an even better prediction skill of the winter NAO than Scheme-I.
    International Journal of Climatology 04/2015; DOI:10.1002/joc.4330 · 3.16 Impact Factor
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    • "In the current study, a comprehensive review was carried out using the SPI in 157 previous studies (from 1998 till early 2013), and it became clear that the 1-, 3-, 6-, 9-, 12-, and 24-months' time scales have been used 65, 97, 89, 46, 96, 42 times, respectively. However, most researchers and users may not have already had any idea of the time scale being used in their works, or they may have simply used a hypothetical short or long time scale to represent a certain statistical application in the drought studies; for instance, analyzing the transition probability of drought classes on the 12-month time scale (Paulo et al. 2005; Paulo and Pereira 2007; Paulo and Pereira 2008) or forecasting drought on the scales of 3, 6, 9, 12, and 24 months (Mishra and Desai 2005; Cancelliere et al. 2007). Note that unawareness of the appropriate time scale and calculating the drought index on several time scales may cause confusion when interpreting the results. "
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    • "In addition, the use of remote sensing technology from satellite imagery allows improving the identification, inventory, mapping, and classification of land wetlands and increased spatial coverage . The SPI (standardized precipitation index) developed by McKee et al. (1993, 1995) is widely used for the identification of drought events and to evaluate its severity (e.g., Moreira et al. 2006, 2008; Cancelliere et al. 2007). "
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    ABSTRACT: The present work studies the trends in drought in northern Algeria. This region was marked by a severe, wide-ranging and persistent drought due to its extraordinary rainfall deficit. In this study, drought classes are identified using SPI (standardized precipitation index) values. A Markovian approach is adopted to discern the probabilistic behaviour of the time series of the drought. Thus, a transition probability matrix is constructed from drought distribution maps. The trends in changes in drought types and the distribution area are analyzed. The results show that the probability of class severe/extreme drought increases considerably rising from the probability of 0.2650 in 2005 to a stable probability of 0.5756 in 2041.
    Journal of Earth System Science 02/2015; 124(1):61-70. DOI:10.1007/s12040-014-0500-6 · 1.04 Impact Factor
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