Article

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
 · 
335 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Drought is a dynamic phenomenon seemingly difficult to confront. Nascent sources of difficulties in applying appropriate management responses may be derived from the following causes of confusion: elusive drought definitions; diversified and devastating drought impacts; and absence of systematic response mechanisms. The basic premise of the present effort is that the use of a drought index, such as SPI, can lead to a better understanding of drought magnitude and duration and thus contributing to more comprehensive drought management approaches. Greece has very often in the past faced the hazardous impacts of drought with the worst drought on record the period of 1989-1993. In the current effort, the SPI drought index is developed for all of Greece and is evaluated accordingly to historical precipitation data. The importance of the Index may be marked in its simplicity and its ability to identify the beginning and the end of a drought event. Its application requires long time series of at least 30 years precipitation data and the desired timescale depending on the area under consideration. In the study, different time series of precipitation data from 41 rain stations, covering the periods 1959-2001 and for time scales of 6 and 12 month were used. The application of the index was achieved with the appropriate correction of the source code files obtained from the National Drought Mitigation Center, University of Nebraska-Lincoln, USA. Forty eight (48) interpolation surfaces were produced and assessed using statistical parameters and historical data. The results in combination with the spatial representation by the GIS, suggest its usage as a drought monitoring tool to support drought forecasting and to potentially inspire integrated strategies for drought management.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Indices for characterising hydrological drought are, in general, data demanding and computationally intensive. A very simple and effective index, the Streamflow Drought Index (SDI), has been recently proposed. It is based on cumulative streamflow volumes for overlapping periods of three, six, nine and twelve months within each hydrological year. It allows defining drought states which are modelled as a non-stationary Markov chain. The methodology is validated using data from two river basins in Greece (Evinos and Boeoticos Kephisos). Water from these basins is diverted for water supply of the Athens Metropolitan Area. Thus, the methodology is tested on a real-world system which allows for assessing its applicability within a Drought Watch System in river basins with significant storage works.
  • 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 11/2014; 519:1453–1465. · 2.69 Impact Factor

Full-text (2 Sources)

Download
89 Downloads
Available from
May 27, 2014