Drought forecasting using the Standardized Precipitation Index

Water Resources Management (Impact Factor: 2.46). 01/2007; 21(5):801-819. DOI: 10.1007/s11269-006-9062-y

ABSTRACT 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.

1 Bookmark
  • [Show abstract] [Hide abstract]
    ABSTRACT: Drought causes huge losses in agriculture and has many negative influences on natural ecosystems. In this study, the applicability of Adaptive Neuro-Fuzzy Inference System (ANFIS) for drought forecasting and quantitative value of drought indices, the Standardized Precipitation Index (SPI), is investigated. For this aim, 10 rainfall gauging stations located in Central Anatolia, Turkey are selected as study area. Monthly mean rainfall and SPI values are used for constructing the ANFIS forecasting models. For all stations, data sets include a total of 516 data records measured between in 1964 and 2006years and data sets are divided into two subsets, training and testing. Different ANFIS forecasting models for SPI at time scales 1–12months were trained and tested. The results of ANFIS forecasting models and observed values are compared and performances of models were evaluated. Moreover, the best fit models have been also trained and tested by Feed Forward Neural Networks (FFNN). The results demonstrate that ANFIS can be successfully applied and provide high accuracy and reliability for drought forecasting.
    Stochastic Environmental Research and Risk Assessment 23(8):1143-1154. · 1.96 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The impacts of climate change on hydrology and water resources are commonly assessed through future projections of Global Climate Models (GCMs). However, since GCM projections are uncertain due to standard errors in the model structure, scenarios and initial conditions, the reliability of impact assessments becomes questionable. This study proposes a new framework to examine uncertainties in GCM simulations and impact assessment studies. The framework involves quantification of GCM simulation uncertainties and consideration of this uncertainty in the estimation of parameters in impact assessment models. This is done through the following steps: (1) systematic biases in GCM simulations are corrected using the nested bias correction (NBC) approach; (2) uncertainties in projections from GCMs are estimated using an uncertainty metric, the square root error variance (SREV); and (3) uncertainty is accounted during parameter estimation of impact assessment models using simulation–extrapolation (SIMEX). The utility of the proposed framework is illustrated for assessment of future droughts through estimation of improved model parameters of the standard precipitation index (SPI). Precipitation outputs from six GCMs, three scenarios and three realisations from the Coupled Model Inter-comparison Project phase 3 (CMIP3) datasets are considered for the analysis. The results reveal that model structural uncertainty is the main source of standard error in GCM simulations and that correction for biases decreases this error. The SPI model parameters as well as the future drought frequency before and after implementation of the method are found to differ widely. The proposed method allows quantifying and accounting for GCM uncertainties in climate change impact assessment more reliably.
    Journal of Hydrology 01/2014; 519:1453–1465. · 2.96 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Droughts are regional incidents that threat the environment and limit most of the socio-economic activities. Given the dry and wet state sequences for two sites, Xt( 1 )X_t^{\left( 1 \right)} and Xt( 2 )X_t^{\left( 2 \right)} , this paper presents a procedure to reduce the two sequences Xt( 1 )X_t^{\left( 1 \right)} and Xt( 2 )X_t^{\left( 2 \right)} to one sequence Z t for the purpose of simplifying the analysis of drought duration at two sites jointly. Theoretical models to evaluate the expected value and the variance of the process Z t and the occurrence probability of the dry state at two sites jointly are presented and verified using simulation experiments. Historical data for the period 1939–2005 and generated rainy season precipitation data for two gauging sites in Central Jordan, namely Amman Airport and Madaba, is used in the present study to investigate the occurrence of droughts. The joint analysis of drought duration obtained using the historical precipitation at the two sites appears to be inconsistent especially for droughts of duration longer than 3years. On the other hand, the joint analysis of drought duration obtained theoretically by employing the characteristics of the process Z t are found to match well with the more reliable drought statistics obtained empirically by analyzing the long generated precipitation. Considering 25years planning horizon, droughts of 1, 2, and 3years duration are the most frequent droughts in the region of Central Jordan. The return period of such regional droughts ranges from 8–30years.
    Water Resources Management 01/2009; 23(14):3005-3018. · 2.46 Impact Factor

Full-text (2 Sources)

Available from
May 27, 2014