YACT (Yet Another Climate Tool)? The SPI Explorer Mike Crimmins Assoc. Professor/Extension Specialist Dept. of Soil, Water, & Environmental Science The University of Arizona
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
Drought and climate information training exercise https://uaclimateextension.shinyapps.io/spitool/
Small Group Exercise SPI Explorer Tool
Focused on two drought/climate tools: Standardized Precipitation Index Seasonal Climate Outlooks (forecasts) Applied to two potential AOI meetings February August https://cals.arizona.edu/droughtandgrazing/dashboard
What is the Standardized Precipitation Index (SPI)? Drought index based on historical monthly precipitation data and expressed in standard deviation units
Some precip data What is a distribution? year total precip 1990 15.86 1991 10.85 1992 16 1993 15.39 1994 13.22 1995 9.64 1996 9.64 1997 10.93 1998 13.54 1999 9.65 2000 13.04 2001 9.56 2002 7.62 2003 9.78 2004 9.05 2005 10.77 2006 11.99 2007 11.04 2008 10.34 2009 6.71 2010 12.3 2011 10.35 2012 8.86 2013 7.58 2014 10.98 25 years Choose some bins to place the data Bins 6-8 8-10 10-12 12-14 14-16 Plot the counts of data in each bin Avg: 11 inches Total counts equals 25 years
SPI conveys probability of occurrence From Quiring 2009
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
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
Worked example: Tucson, AZ
Worked example: Tucson, AZ January 12-month SPI 0.71 January 1-month SPI 0.92
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 1981-2010) http://www.cpc.ncep.noaa.gov/products/predictions/long_range/ Also on Dashboard
Worked Example: Tucson, AZ ~50% March-April-May 2016
Tucson, AZ: Mar-Apr-May Total Precip, 1981-2010 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
Worked Example: Tucson, AZ < 1 1-1.6 >1.6 March-April-May 2016
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
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?
Worked example: Tucson, AZ 1916-2015 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. 3.97 to 5.54 in. 3 wet (0-1) 2.01 to 3.63 in. 5.54 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
March 3-mo SPI (winter) Worked example: Tucson, AZ 1916-2015 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. 3.97 to 5.54 in. 3 wet (0-1) 2.01 to 3.63 in. 5.54 to 7.43 in. 4 very wet(>1) > 3.63 in. > 7.43 in.
SPI Explorer Tool https://uaclimateextension.shinyapps.io/spitool/
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
Precipitation Logbook Generator https://uaclimateextension.shinyapps.io/precipchart/
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