Description of Standardized Precipitation Index (SPI) and Canadian Evaluation of SPI in Diverse Climates

1. Description of Standardized Precipitation Index (SPI) and Canadian Evaluation of SPI in Diverse Climates Richard R. Heim Jr.NOAA/NESDIS/National Climatic Data Center Asheville, North Carolina, USA E.G. (Ted) O’BrienEnvironment Canada Regina, Saskatchewan, Canada

2. Description of the Standardized Precipitation Index (SPI) Richard R. Heim Jr.*NOAA/NESDIS/National Climatic Data Center Asheville, North Carolina, USA * material from Mark Svoboda, National Drought Mitigation Center Global Drought Assessment Workshop Asheville, NC, USA – 21 April 2010

3. Applying the Standardized Precipitation Index as a Drought Indicator Mark Svoboda, Climatologist Monitoring Program Area Leader National Drought Mitigation Center University of Nebraska-Lincoln Inter-Regional Workshop on Indices and Early Warning Systems for Drought Lincoln, NE December 8-11, 2009

4. Characteristics of the Standardized Precipitation Index (SPI) • Developed by McKee et al. in 1993 • Simple index--precipitation is the only parameter (probability of observed precipitation transformed into an index) • Being used in research or operational mode in over 60 countries • Multiple time scales allow for temporal flexibility in evaluation of precipitation conditions and water supply

5. How it Works • It is NOTsimply the “difference of precipitation from the mean… divided by the standard deviation” • Precipitation is normalized using a probability distribution function so that values of SPI are actually seen as standard deviations from the median • A normalized distribution allows for estimating both dry and wetperiods • Accumulated values can be used to analyze drought severity (magnitude)

6. How it Works • Need 30 years of continuous monthly precipitation data (the longer the better) • SPI time scale intervals shorter than 1 month and longer than 24 months may be unreliable • Is spatially invariant in its interpretation • Probability based (probability of observed precipitation transformed into an index) nature is well suited to risk management

7. SPI Methodology • The SPI calculation for any location is based on the long-term precipitation record for a desired period. This long-term record is fitted to a probability distribution, which is then transformed into a normal distribution so that the mean SPI for the location and desired period is zero (Edwards and McKee, 1997) • Positive SPI values indicate greater than median precipitation, and negative values indicate less than median precipitation • Because the SPI is normalized, wetter and drier climates can be represented in the same way, and wet periods can also be monitored using the SPI.

8. SPI Methodology • Overview: The SPI is an index based on the probability of precipitation for any time scale. • Who uses it: Many drought planners appreciate the SPI’s versatility. U.S. + North American Drought Monitors, U.S. State Drought Plans. Over 60 countries. • Pros: The SPI can be computed for different time scales • can provide early warning of drought and help assess drought severity • less complex than the Palmer. • One number/has historical context • Cons: Based on Precipitation only • no Temp, no ET. • Values based on preliminary data may change. • Not as applicable to CC analysis

9. SPI data used in the U.S. Drought Monitor • D0 Abnormally Dry: SPI value of -0.5 to -0.7 • D1 Moderate Drought: -0.8 to -1.2 • D2 Severe Drought: -1.3 to -1.5 • D3 Extreme Drought: -1.6 to -1.9 • D4 Exceptional Drought: -2.0 or less • NDMC Daily Gridded SPI Product

10. Probability of Recurrence

11. NDMC Distribution of SPI Provided to over 60 countries 150 + scientists Over 50+ visiting scientists

12. Considerations • Different probability functions used to fit the SPI will result in different values • Different periods of record used to standardize the SPI will result in different values • The same probability distribution functions and period of record need to be used for all stations to ensure spatial comparability between stations & between countries • The various time scales for the SPI reflect different forcings and types of drought (meteorological vs. hydrological); this may vary with location and season

13. Canadian Preliminary Evaluation of SPI in Diverse Climates Presented by E. G. (Ted) O’Brien, Environment Canada, Meteorological Services of Canada E. G. (Ted) O’Brien and Jennifer Stroich, 2004

14. Canadian Test Sites Swift Current, Saskatchewan Guelph, Ontario Kentville, Nova Scotia (Annapolis Valley)

15. Swift Current Streamflow Findings(1960 – 2001) SPIs for Swift Current compared to monthly streamflow - Swift Current Creek near Leinan (regulated) – Gross Drainage Area 3730 km2 • Tested against 1-,2-,3-,6-,12-,24- month SPI’s - ECORC’s Swift Current CDA • SPIs under 12 months correlate best with May to August streamflow • 2- to 3-month SPIs strong correlations during summer months • 6- month SPI strongest correlations during summer months • Beyond 6-month SPI significant correlations decline

17. Guelph Streamflow Findings(1970 – 2001) SPIs for Guelph compared to monthly streamflow – Grand River at Galt(regulated) - Gross Drainage Area 3520 km2 • Tested against 1-,2-,3-,6-,12-,24- month SPI’s from ECORC’s Waterloo-Wellington A and Fergus Shand Dam • 1- to 2-month SPI - correlations sporadic • 3- month SPI correlations - significant all times of year, exception April • Beyond 3- month SPI the number of significant correlations declines

18. Kentville Streamflow Findings(1960 – 1995) SPIs for Kentville compared to monthly streamflow at the Annapolis River at Wilmot (regulated) – Gross Drainage Area 546 km2 • Tested against 1-,2-,3-,6-, 12-, 24- month SPI’s from ECORC’s Kentville CDA • 2- and 3- month SPI correlations strongest • Beyond 6- month SPI number of significant correlations decline

19. Swift Current Yield Findings(1960 – 2001) SPI Swift Current CDA and AAFC spring wheat research trial at Swift Current CDA (fallow-wheat rotation) • Tested against 1-,2-,3-,6-,12-,24- month SPI’s from ECORC’s Swift Current CDA • Spring wheat yields correlated best with July SPI. • July 2-year SPI (r = 0.750) • July 3-month SPI (r = 0.712)

21. Guelph Yield Findings(1970 – 2001) SPIs compared to AGRICORP Wellington county crop yield data • Tested 1-, 2-, 3-, 6-, 12-mth, 2-, 3-, 4-, 5-yr SPI calculated from ECORC’s Waterloo-Wellington A and Fergus Shand Dam • 2- month SPI strongest correlations • Correlations are negative or positive • Investigated years of above average, average and below average precipitation • SPI or percent of average precipitation do not appear to apply to yields • 1989 dry July – September resulted in yields below the 10th percentile for canola and spring grain • Temperature; strong positive correlations exist

22. Kentville Yield Findings(1960 – 1995) SPIs for Kentville compared to crop yields. • Tested against 1-,2-,3-,6-, 12-, 24- month SPI’s from ECORC’s Kentville CDA • No correlations exist with apple yield data • Information obtained for Kings County indicated that most of the potatoes, beans and peas and 90% of wheat grown in the province is grown here • Wheat yields obtained at the provincial level - results indicate statistically significant correlations - precipitation accounts for ~ 20% of the variability in yield

23. Conflicts among indices • Moderate drought (D1) using the percent of normal precipitation while the SPI values classified an extreme drought (D3). • Example: 1989 Guelph, ON • D1 (moderate drought) using percent of normal precipitation from July, August and September • D4 (exceptional drought) using the September 3-month SPI • For this reason we recommend using precipitation percentiles

24. Findings continued • Dry spells of 4 – 8 weeks duration and temperature extremes • Hydrologic drought (at a broad scale) – use of monthly or seasonal streamflow volume percentiles • Statistical correlations with precipitation explain very little (less than 25%) of observed yield variations in more humid regions