YACT (Yet Another Climate Tool)? The SPI Explorer

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1 YACT (Yet Another Climate Tool)? The SPI Explorer Mike Crimmins Assoc. Professor/Extension Specialist Dept. of Soil, Water, & Environmental Science The University of Arizona

2 Yes, another climate tool for a specific purpose Surveys and workshop evaluations indicated a general interest in learning more about drought indices and climate forecasting USFS uses a forest-wide drought trigger threshold of SPI<-1 Most USFS personnel and livestock producers wanted to learn more about what the SPI was and how it was used Developed online tool and accompanying exercise to illustrate main concepts behind SPI and seasonal climate outlooks

3 Drought and climate information training exercise

4 Small Group Exercise SPI Explorer Tool

5 Focused on two drought/climate tools: Standardized Precipitation Index Seasonal Climate Outlooks (forecasts) Applied to two potential AOI meetings February August

6 What is the Standardized Precipitation Index (SPI)? Drought index based on historical monthly precipitation data and expressed in standard deviation units

7 Some precip data What is a distribution? year total precip years Choose some bins to place the data Bins Plot the counts of data in each bin Avg: 11 inches Total counts equals 25 years

8 SPI conveys probability of occurrence From Quiring 2009

9 SPI can be calculated over different timescales Jan-14 Feb-14 Mar-14 Apr-14 May-14 Jun-14 Jul-14 Aug-14 Sep-14 Oct-14 Nov-14 Dec-14 Jan-15 Feb-15 Mar-15 Apr-15 May-15 Jun-15 Jul-15 Aug-15 Sep-15 Oct-15 Nov-15 Dec-15 Jan-16 Monthly Precip Data Jan 12-mo SPI Jan-14 Feb-14 Mar-14 Apr-14 May-14 Jun-14 Jul-14 Aug-14 Sep-14 Oct-14 Nov-14 Dec-14 Jan-15 Feb-15 Mar-15 Apr-15 May-15 Jun-15 Jul-15 Aug-15 Sep-15 Oct-15 Nov-15 Dec-15 Jan-16 Sep 6-mo SPI Jan-14 Feb-14 Mar-14 Apr-14 May-14 Jun-14 Jul-14 Aug-14 Sep-14 Oct-14 Nov-14 Dec-14 Jan-15 Feb-15 Mar-15 Apr-15 May-15 Jun-15 Jul-15 Aug-15 Sep-15 Oct-15 Nov-15 Dec-15 Jan-16 Sep 3-mo SPI

10 SPI Timescales 1-month 3-month 12-month SPI can be calculated for any timescale length 12-month and longer timescales capture slowly varying drought, while 6-month and shorter captures seasonal changes

11 Worked example: Tucson, AZ

12 Worked example: Tucson, AZ January 12-month SPI 0.71 January 1-month SPI 0.92

13 Seasonal climate outlooks Probabilistic, categorical precipitation and temperature forecasts 1-month and 3-month outlooks; overlapping periods extend out to 13 months Driven largely by El Niño- Southern Oscillation for precip and trend in temps Based on normals periods (currently ) Also on Dashboard

14 Worked Example: Tucson, AZ ~50% March-April-May 2016

15 Tucson, AZ: Mar-Apr-May Total Precip, Below normal (33%tile, inches): 1 Median (50%tile, inches): 1.3 Above normal (66%tile, inches): 1.6 Below-normal Near normal Above-normal

16 Worked Example: Tucson, AZ < >1.6 March-April-May 2016

17 More on Seasonal Climate Outlooks They do not forecast amounts of precip, only shifts in probability of observing amounts in one of the terciles El Niño=above average winter precip, La Niña=below average winter precip Summer monsoon season precipitation outlooks are notoriously poor Climatology or statistics of past climate can also be used as a forecast tool

18 Example using SPI Probabilities can be calculated based on historical values of precipitation, SPI and transitions between different values If past values are grouped into categories (for example wet vs. dry) their sequences can be counted and formed into probabilities For example, how often was a dry winter followed by a dry summer? What is the chance of this occurring?

19 Worked example: Tucson, AZ How often is a very dry winter followed by a very dry summer? Use March 3-month SPI for winter precip Use September 3-month SPI for summer precip Come up with categories to define levels of drought Category Period 1 Period 2 1 very dry (<-1) < 0.99 in. < 3.97 in. 2 dry (-1-0) 0.99 to 2.01 in to 5.54 in. 3 wet (0-1) 2.01 to 3.63 in to 7.43 in. 4 very wet(>1) > 3.63 in. > 7.43 in. Look at historical record and assign each winter and summer period to one of these categories With 4 categories there are 16 possible sequences; count up these transitions and divide them by the total possible to get percentages Percentages can be interpreted as probabilities based on the historical record

20 March 3-mo SPI (winter) Worked example: Tucson, AZ Sept 3-mo SPI (summer) Category Period 1 (winter) Period 2 (summer) 1 very dry (<-1) < 0.99 in. < 3.97 in. 2 dry (-1-0) 0.99 to 2.01 in to 5.54 in. 3 wet (0-1) 2.01 to 3.63 in to 7.43 in. 4 very wet(>1) > 3.63 in. > 7.43 in.

21 SPI Explorer Tool

22 What type of information would help drought planning and management? Rain gauges. Can we design a better rain gauge for remote, range monitoring?: Cow proof, easy to read and maintain, inexpensive, rugged and longlasting

23

24

25 Precipitation Logbook Generator

26 Printable logbook for tracking precip at depth gauge location Working to tie curves to specific decision points and management actions Cumulative Precip on Aug 3 rd - Very Wet: Increase stocking - Wet: No action - Dry: Increase monitoring - Very Dry: supplement feed/water; relocate cattle

27

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