Appropriate Application of the Standardized Precipitation Index in Arid Locations and Dry Seasons

Authors

Hong Wu, Texas Institute for Applied Environmental Research, Tarleton State University, Stephenville, TX
Mark D. Svoboda, University of Nebraska - LincolnFollow
Michael J. Hayes, University of Nebraska-LincolnFollow
Donald A. Wilhite, University of Nebraska - LincolnFollow
Fujiang Wen, City of Dallas Water Utilities Department, Dallas, TX

Date of this Version

2007

Citation

Published in International Journal of Climatology 27 (2007), pp. 65–79

Comments

Copyright © 2006 Royal Meteorological Society. Used by permission.

Abstract

The Standardized Precipitation Index (SPI) is now widely used throughout the world in both a re-search and an operational mode. For arid climates, or those with a distinct dry season where zero values are common, the SPI at short time scales is lower bounded, referring to non-normally distributed in this study. In these cases, the SPI is always greater than a certain value and fails to indicate a drought occurrence. The nationwide statistics based on our study suggest that the non-normality rates are closely related to local precipitation climates. In the eastern United States, SPI values at short time scales can be used in drought/flood monitoring and research in any season, while in the western United States, because of its distinct seasonal precipitation distribution, the appropriate us-age and interpretation of this index becomes complicated. This would also be the case for all arid climates. From a mathematical point of view, the non-normally distributed SPI is caused by a high probability of no-rain cases represented in the mixed distribution that is employed in the SPI construction. From a statistical point of view, the 2-parameter gamma model used to estimate the precipitation probability density function and the limited sample size in dry areas and times would also reduce the confidence of the SPI values. On the basis of the results identified within this study, we recommend that the SPI user be cautious when applying short-time-scale SPIs in arid climatic regimes, and interpret the SPI values appropriately. In dry climates, the user should focus on the duration of the drought rather than on just its severity. It is also worth noting that the SPI results from a statistical product of the input data. This character makes it difficult to link the SPI data to the physical functioning of the Earth system.