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 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 chapter is to provide two methodologies for the seasonal forecasting of SPI. In the first methodology, the transition probabilities from a current drought condition to another in the future, and from a single value of current SPI to a drought class are derived as functions of the statistics of the underlying monthly precipitation process. The proposed analytical approach appears particularly valuable from a practical stand point in light of the difficulties of applying a frequency approach or a Markov chain approach, due to the limited number of transitions generally observed even on relatively long SPI records. In the second methodology, SPI forecasts at a generic time horizon M are analytically determined, in terms of conditional expectation, as a function of a finite number of past values of monthly precipitation. Forecasting accuracy is estimated through an expression of the Mean Square Error, which enables to derive confidence intervals of prediction. Validation of the derived expressions is carried out by comparing theoretical forecasts and observed SPI values. The methodologies have been applied to the series of SPI, based on monthly precipitation observed in Sicily over 40 rain gauges in the period 1921-2003. Results seem to confirm the reliability of the proposed methodologies, which therefore can find useful application within a drought monitoring system
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bonaccorso, B., Bordi, I., Cancelliere, A., Rossi, G. and Sutera, A. (2003) Spatial variability of drought: an analysis of SPI in Sicily. Water Resource Management 17, pp. 273–296.
Bordi, I., Fraedrich, K., Petitta, M. and Sutera, A. (2005) Methods for predicting drought occurrences. Proc. of the 6th International Conference of the European Water Resources Association, Menton, France, 7–10 September 2005.
Brockwell, P.J. and Davis, R.A. (1996) Introduction to Time Series and Forecasting. Springer-Verlag ed., New York, pp. 1–422.
Cancelliere, A., i Mauro, G., Bonaccorso, B. and Rossi, G. (2007). Drought forecasting using the Standardized Precipitation Index. Water Resource Management 21(5), pp. 801–819.
Cancelliere, A., Di Mauro, G., Bonaccorso, B. and Rossi, G. (2005) Stochastic forecasting of Standardized Precipitation Index. Proc. of XXXI IAHR Congress “Water Engineering for the future: Choice and Challenges”, Seoul, Korea, 11–16 September 2005, pp. 3252–3260.
Cancelliere, A., Rossi, G. and Ancarani, A. (1996) Use of Palmer Index as drought indicator in Mediterranean regions. Proc. IAHR Congress “From Flood to Drought”, Sun City, South Africa, August 5–7, 1996, pp. S4.12. 1–25.
Guttman, N.B. (1998) Comparing the Palmer Drought Severity Index and the Standardized Precipitation Index. Journal of the American Water Resources Association 34(1), pp. 113–121.
Guttman, N.B. (1999) Accepting the Standardized Precipitation Index: a calculating algorithm. Journal of the American Water Resources Association 35(2), pp. 311-323.
Hayes, M.J., Svoboda, M., Wilhite, D.A. and Vanyarkho, O. (1999) Monitoring the 1996 drought using the SPI. Bulletin of the American Meteorological Society 80, pp. 429–438.
Heim, R.R. (2000) Drought indices: a review. In: D.A. Wilhite (Ed.), Drought: A Global Assessment, Routledge, London and New York.
Karl, T., Quinlan, F. and Ezell, D.S. (1986) Drought termination and amelioration: its climatological probability. Journal of Climate and Applied Meteorology 26, pp. 1198–1209.
Keyantash, J. and Dracup, J.A. (2002) The quantification of drought: an evaluation of drought indices. Bulletin of the American Meteorological Society 83(8), pp. 1167–1180.
Kotz, S., Balakrishnan, N. and Johnson, N.L. (2000) Continuous multivariate distributions, Volume 1. John Wiley and Sons, Canada.
Lohani, V.K., Loganathan, G.V. and Mostaghimi, S. (1998) Long-term analysis and short-term forecasting of dry spells by Palmer Drought Severity Index. Nord. Hydrol. 29(1), pp. 21–40.
McKee, T.B., Doesken, N. J. and Kleist, J. (1993) The relationship of drought frequency and duration to time scales. Proc. 8th Conference on Applied Climatology, Anaheim, California, pp. 179–184.
Mood, A. M., Graybill, F. A. and Boes (1974) Introduction to the Theory of Statistics. McGraw-Hill, New York.
Rossi, G., Cancelliere, A. (2002) Early warning of drought: development of a drought bullettin for Sicily. Proc. 2nd International Conference ‘‘New trends in Water and Environmental Engineering for Safety and Life: Eco-compatible Solutions for Aquatic Environments’’, Capri, Italy, June 24–28, 2002, pp. 1–12.
Rossi, G. (2003) Requisites for a drought watch system. In: G. Rossi et al. (Eds.), Tools for Drought Mitigation in Mediterranean Regions, Kluwer Academic Publishing, Dordrecht, pp. 147–157.
Schervish, M.J. (1984) Algorithm AS 195. Multivariate Normal Probabilities with Error Bound. Applied Statistics 33(1), pp. 81–94.
Wilhite, D.A., Hayes, M. J. and Svoboda, M.D. (2000) Drought monitoring and assessment: status and trends in the United States. In: J.V.Vogt, F. Somma (Eds.), Drought and Drought Mitigation in Europe, Kluwer Academic Publishers, Dordrecht, Boston, London, pp. 149–160.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer
About this chapter
Cite this chapter
Cancelliere, A., Mauro, G.D., Bonaccorso, B., Rossi, G. (2007). Stochastic Forecasting of Drought Indices. In: Rossi, G., Vega, T., Bonaccorso, B. (eds) Methods and Tools for Drought Analysis and Management. Water Science and Technology Library, vol 62. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-5924-7_5
Download citation
DOI: https://doi.org/10.1007/978-1-4020-5924-7_5
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-5923-0
Online ISBN: 978-1-4020-5924-7
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)