Standardized Precipitation Index
User Guide

WMO-No. 1090

Standardized Precipitation Index
User Guide

2012
WMO-No. 1090

EDITORIAL NOTEMETEOTERM, the WMO terminology database, may be consulted at: http://www.wmo.int/pages/prog/
lsp/meteoterm_wmo_en.html. Acronyms may also be found at: http://www.wmo.int/pages/themes/
acronyms/index_en.html.

WMO-No. 1090
© World Meteorological Organization, 2012
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CONTENTS

Page

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1

1.

Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3

2.

Introduction to the Standardized Precipitation Index . . . . . . . . . . . . . . . . . . . . . . .

3

3.

Description of the Standardized Precipitation Index . . . . . . . . . . . . . . . . . . . . . . . .

4

4.

Strengths and weaknesses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

65.

Interpretation: spatial and temporal flexibility described . . . . . . . . . . . . . . . . . . . .
6
5.1 Short- versus long-term Standardized Precipitation Index values. . . . . . . . . . . . .
7
5.1.1
1-month SPI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
5.1.2
3-month SPI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
5.1.3
6-month SPI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
5.1.4
9-month SPI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
5.1.5
12-month up to 24-month SPI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

6.

Computational methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.1 SPI methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.2 How it works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

9
9
9

7.

How to obtain the program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

98.

How to run the program in Windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

109.

Mapping capabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

13

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

16

Other online resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

16

STANDARDIZED PRECIPITATION INDEX USER GUIDEPREFACEOver the years, there has been much discussion on what drought indices should be used in aparticular climate and for what application. Many drought definitions and indices have beendeveloped and attempts have been made to provide some guidance on this issue.
With this in mind, the Interregional Workshop on Indices and Early Warning Systems for Drought
was organized and held at the University of Nebraska-Lincoln, United States of America, from 8 to
11 December 2009. It was jointly sponsored by the School of Natural Resources (SNR) of the
University of Nebraska, the United States National Drought Mitigation Center (NDMC), the World
Meteorological Organization (WMO), the United States National Oceanic and Atmospheric
Administration (NOAA), the United States Department of Agriculture (USDA) and the United
Nations Convention to Combat Desertification (UNCCD). The workshop brought together 54
participants representing 22 countries from all over the world. They reviewed the drought indicescurrently in use in different regions of the world to explain meteorological, agricultural andhydrological droughts; assessed the capacity for collecting information on the impacts ofdrought; reviewed the current and emerging technologies for drought monitoring, and discussed
the need for consensus standard indices to describe different types of droughts.
The experts at the meeting elaborated and approved the Lincoln Declaration on Drought Indices,which recommended that the Standardized Precipitation Index (SPI) be used by all National
Meteorological and Hydrological Services (NMHSs) around the world to characterizemeteorological droughts, in addition to other drought indices that were in use in their service.
The Lincoln Declaration also recommended the development of a comprehensive SPI user
manual. In June 2011, the Sixteenth World Meteorological Congress adopted a resolution thatendorsed both of these recommendations. The Congress also requested that the SPI manual be
published and distributed in all official languages of the United Nations.
The full Lincoln Declaration on Drought Indices can be found on the WMO website at http://
www.wmo.int/pages/prog/wcp/agm/meetings/wies09/documents/Lincoln_Declaration_Drought_
Indices.pdf.
WMO would like to thank Mark Svoboda, Michael Hayes and Deborah A. Wood of the National
Drought Mitigation Center (NDMC) at the University of Nebraska for preparing this user guide on
the Standardized Precipitation Index1. We hope that it will help countries and institutions tounderstand how to calculate and use the SPI in order to develop or further enhance their own
drought monitoring and early warning capabilities.
For any questions or comments on the content of this guide, including any suggestions for
improvement, please email the WMO Agricultural Meteorology Division at agm@wmo.int.

1This guide should be cited as: World Meteorological Organization, 2012: Standardized Precipitation Index User
Guide (M. Svoboda, M. Hayes and D. Wood). (WMO-No. 1090), Geneva.

STANDARDIZED PRECIPITATION INDEX USER GUIDE

1.

3

BACKGROUND

Drought is an insidious natural hazard that results from lower levels of precipitations than what is
considered normal. When this phenomenon extends over a season or a longer period of time,
precipitation is insufficient to meet the demands of human activities and the environment.
Drought must be considered a relative, rather than absolute, condition. There are also manydifferent methodologies for monitoring drought. Droughts are regional in extent and each region
has specific climatic characteristics. Droughts that occur in the North American Great Plains will
differ from those that occur in northeast Brazil, southern Africa, western Europe, eastern Australia,
or the North China Plain. The amount, seasonality and form of precipitation differ widely
between each of these locations.
Temperature, wind and relative humidity are also important factors to include in characterizing
drought. Drought monitoring also needs to be application-specific because drought impacts willvary between sectors. Drought means different things to different users such as water managers,
agricultural producers, hydroelectric power plant operators and wildlife biologists. Even within
sectors, there are many different perspectives of drought because impacts may differ markedly.
Droughts are commonly classified by type as meteorological, agricultural and hydrological, anddiffer from one another in intensity, duration and spatial coverage.

2.

INTRODUCTION TO THE STANDARDIZED PRECIPITATION INDEXOver the years, many drought indices were developed and used by meteorologists andclimatologists around the world. Those ranged from simple indices such as percentage of normal
precipitation and precipitation percentiles to more complicated indices such as the Palmer
Drought Severity Index. However, scientists in the United States realized that an index needed tobe simple, easy to calculate and statistically relevant and meaningful. Moreover, theunderstanding that a deficit of precipitation has different impacts on groundwater, reservoir
storage, soil moisture, snowpack and streamflow led American scientists McKee, Doesken andKleist to develop the Standardized Precipitation Index (SPI) in 1993.
The SPI (McKee and others, 1993, 1995) is a powerful, flexible index that is simple to calculate. In
fact, precipitation is the only required input parameter. In addition, it is just as effective inanalysing wet periods/cycles as it is in analysing dry periods/cycles. The program can run in both
Windows and UNIX environments. This SPI user guide describes the Windows version.
Ideally, one needs at least 20-30 years of monthly values, with 50-60 years (or more) being
optimal and preferred (Guttman, 1994). The program can be run with missing data, but it willaffect the confidence of the results, depending on the distribution of the missing data in relation
to the length of the record. More information on usage can be found in section 6, Computational
methodology.
Climatologists would prefer to see serially complete data sets, which means there should be no
missing data. However, it is more than likely that data sets would only have 90% or even 85%
complete records. In reality, many users don’t have this luxury and may have to settle for less(75-85% complete) unless they look to estimation techniques to fill in the gaps in the record. Of
course, long and pristine data records are neither practical nor typical in many cases, so the userneeds to be aware of the statistical limitations of extreme events when dealing with shorter
periods of records for various locations. In the end, users have to make a subjective decision as towhat tolerance of missing data they are willing to incorporate into the SPI calculations andanalyses. Depending on the confidence and method of calculation, the use of estimated data is
acceptable. Naturally, the fewer estimated data used the better.

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3.

STANDARDIZED PRECIPITATION INDEX USER GUIDEDESCRIPTION OF THE STANDARDIZED PRECIPITATION INDEX

Overview: The SPI is based on the probability of precipitation for any time scale. The probabilityof observed precipitation is then transformed into an index. It is being used in research oroperational mode in more than 70 countries.
Who uses it: Many drought planners appreciate the SPI’s versatility. It is also used by a variety of
research institutions, universities, and National Meteorological and Hydrological Services acrossthe world as part of drought monitoring and early warning efforts.
Strengths: Precipitation is the only input parameter. The SPI can be computed for different timescales, provide early warning of drought and help assess drought severity. It is less complex than
the Palmer Drought Severity Index and many other indices.
Weaknesses: It can only quantify the precipitation deficit; values based on preliminary data may
change, and values change as the period of record grows.
Developed by T.B. McKee, N.J. Doesken and J. Kleist, Colorado State University, 1993.

The SPI was designed to quantify the precipitation deficit for multiple timescales. These timescales
reflect the impact of drought on the availability of the different water resources. Soil moisture
conditions respond to precipitation anomalies on a relatively short scale. Groundwater,
streamflow and reservoir storage reflect the longer-term precipitation anomalies. For thesereasons, McKee and others (1993) originally calculated the SPI for 3-, 6-,12-, 24- and 48-monthtimescales.
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 negativevalues indicate less than median precipitation. Because the SPI is normalized, wetter and drier
climates can be represented in the same way; thus, wet periods can also be monitored using the
SPI.
McKee and others (1993) used the classification system shown in the SPI value table below(Table 1) to define drought intensities resulting from the SPI. They also defined the criteria for adrought event for any of the timescales. A drought event occurs any time the SPI is continuously
negative and reaches an intensity of -1.0 or less. The event ends when the SPI becomes positive.
Each drought event, therefore, has a duration defined by its beginning and end, and an intensity
for each month that the event continues. The positive sum of the SPI for all the months within adrought event can be termed the drought’s “magnitude”.

Table 1. SPI values
2.0+

extremely wet

1.5 to 1.99

very wet

1.0 to 1.49

moderately wet-.99 to .99

near normal-1.0 to -1.49

moderately dry-1.5 to -1.99

severely dry-2 and lessextremely dry

STANDARDIZED PRECIPITATION INDEX USER GUIDE

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Based on an analysis of stations across Colorado in the United States, McKee determined that the
SPI indicates mild drought 24% of the time, moderate drought 9.2% of the time, severe drought
4.4% of the time and extreme drought 2.3% of the time (McKee and others,1993). Because the
SPI is standardized, these percentages are expected from a normal distribution of the SPI. The
2.3% of SPI values within the “extreme drought” category is a percentage that is typically
expected for an “extreme” event. In contrast, the Palmer Drought Severity Index reaches its“extreme” category more than 10% of the time across portions of the central Great Plains in the
United States. This standardization allows the SPI to determine the rarity of a current drought(Table 2), as well as the probability of the precipitation necessary to end it (McKee and others,1993). It also allows the user to confidently compare historical and current droughts between
different climatic and geographic locations when assessing how rare, or frequent, a given drought
event is.

Table 2. Probability of recurrence
SPI

Category

Number oftimes in 100
years

Severity ofevent

0 to -0.99

Mild dryness

33

1 in 3 yrs.

-1.00 to-1.49

Moderatedryness

10

1 in 10 yrs.

-1.5 to-1.99

Severedryness

5

1 in 20 yrs.

< -2.0Extremedryness

2.5

1 in 50 yrs.

Some key points:•

Because the SPI is normalized, wetter and drier climates can be represented in the same way;
thus, wet periods can also be monitored using the SPI. However, it must be stressed that the
SPI is not suitable for climate change analysis because temperature is not an input parameter.

•

The SPI was designed to quantify the precipitation deficit for multiple timescales.

•

These timescales reflect the impact of drought on the availability of the different water
resources, which was the initial intent of the SPI’s creators.

•

Soil moisture conditions respond to precipitation anomalies on a relatively short timescale.
Groundwater, streamflow and reservoir storage reflect the longer-term precipitation
anomalies. So, for example, one may want to look at a 1- or 2-month SPI for meteorologicaldrought, anywhere from 1-month to 6-month SPI for agricultural drought, and something
like 6-month up to 24-month SPI or more for hydrological drought analyses and applications.

STANDARDIZED PRECIPITATION INDEX USER GUIDE

64.

STRENGTHS AND WEAKNESSESThe strengths and weaknesses of the SPI can be summarized as follows:

Strengths•

It is flexible: it can be computed for multiple timescales•

Shorter timescale SPIs, for example 1-, 2- or 3-month SPIs, can provide early warning ofdrought and help assess drought severity•

It is spatially consistent: it allows for comparisons between different locations in different
climates•

Its probabilistic nature gives it historical context, which is well suited for decision-making.

Weaknesses•

It is based only on precipitation•

No soil water-balance component, thus no ratios of evapotranspiration/potential
evapotranspiration (ET/PET) can be calculated.

•

A new variation of the index by Vicente-Serrano and others (2010) attempts to address the
PET issue by including a temperature component in the calculation of their new index called
the Standardized Precipitation and Evapotranspiration Index (SPEI). The inputs required to run
the program are precipitation, mean temperature and latitude of the site(s). Further
information on the SPEI is available at http://sac.csic.es/spei/index.html.

5.

INTERPRETATION: SPATIAL AND TEMPORAL FLEXIBILITY DESCRIBEDThere is no single definition of drought (Wilhite and Glantz, 1985). We can generally group theminto meteorological, agricultural, hydrological and socioeconomic droughts. Drought is a very
complex hazard to define and detect. It spans multiple sectors and timescales. Just as there is no
single definition of drought, there is no single drought index that meets the requirements of all
applications.
That said, a real strength of the SPI is its ability to be calculated for many timescales, which makes
it possible to deal with many of the drought types described above. The ability to compute the
SPI on multiple timescales allows for temporal flexibility in the evaluation of precipitation
conditions in relation to water supply.
As mentioned earlier, the SPI was designed to quantify the precipitation deficit for multiple
timescales, or moving averaging windows. These timescales reflect the impacts of drought on
different water resources needed by various decision-makers. Meteorological and soil moisture
conditions (agriculture) respond to precipitation anomalies on relatively short timescales, forexample 1-6 months, whereas streamflow, reservoirs, and groundwater respond to longer-term
precipitation anomalies of the order of 6 months up to 24 months or longer. So, for example,
one may want to look at a 1- or 2-month SPI for meteorological drought, anywhere from 1-month
to 6-month SPI for agricultural drought, and something like 6-month up to 24-month SPI or more
for hydrological drought analyses and applications.
The SPI can be calculated from 1 month up to 72 months. Statistically, 1–24 months is the bestpractical range of application (Guttman, 1994, 1999). This 24-month cutoff is based on

STANDARDIZED PRECIPITATION INDEX USER GUIDE

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Guttman’s recommendation of having around 50–60 years of data available. Unless one has
80–100 years of data, the sample size is too small and the statistical confidence of the probability
estimates on the tails (both wet and dry extremes) becomes weak beyond 24 months. In addition,
having only the minimum 30 years of data (or less) shortens the sample size and weakens theconfidence. Technically, one could run the SPI on less than 30 years of data bearing in mind,
however, the statistical limitations and weaker confidence pointed out above.

5.1

Short- versus long-term Standardized Precipitation Index values

5.1.1

1-month SPI

A 1-month SPI map is very similar to a map displaying the percentage of normal precipitation for
a 30-day period. In fact, the derived SPI is a more accurate representation of monthly
precipitation because the distribution has been normalized. For example, a 1-month SPI at theend of November compares the 1-month precipitation total for November in that particular yearwith the November precipitation totals of all the years on record. Because the 1-month SPI
reflects short-term conditions, its application can be related closely to meteorological types ofdrought along with short-term soil moisture and crop stress, especially during the growing
season. The 1-month SPI may approximate conditions represented by the Crop Moisture Index,
which is part of the Palmer Drought Severity Index suite of indices.
Interpretation of the 1-month SPI may be misleading unless climatology is understood. In regions
where rainfall is normally low during a month, large negative or positive SPIs may result eventhough the departure from the mean is relatively small. The 1-month SPI can also be misleadingwith precipitation values less than the normal in regions with a small normal precipitation total fora month. As with a percent of normal precipitation map, useful information is contained in the
1-month SPI maps, but caution must be observed when analysing them.
NOTE: In theory, the SPI can be calculated on a sub-monthly basis, but in practice this is notrecommended. It is highly recommended that the user look at a minimum averaging window of 4
weeks. One could compute a 1-week SPI, but the reality is that one will likely encounter many dry
day events (0.00 rainfall even in non-arid climates), which makes the SPI behave erratically (Wuand others, 2006), therefore this approach is not recommended. However, updating the SPI every
day or every week for a 1-month up to a 24-month time frame is acceptable. This “moving
window” approach does not compromise the program as it is still looking at a minimum of 4
weeks of data each day it moves.

5.1.2

3-month SPI

The 3-month SPI provides a comparison of the precipitation over a specific 3-month period withthe precipitation totals from the same 3-month period for all the years included in the historical
record. In other words, a 3-month SPI at the end of February compares the December–January–
February precipitation total in that particular year with the December–February precipitation
totals of all the years on record for that location. Each year data is added, another year is added tothe period of record, thus the values from all years are used again. The values can and will change
as the current year is compared historically and statistically to all prior years in the record ofobservation.
A 3-month SPI reflects short- and medium-term moisture conditions and provides a seasonal
estimation of precipitation. In primary agricultural regions, a 3-month SPI might be more effective
in highlighting available moisture conditions than the slow-responding Palmer Index or othercurrently available hydrological indices. A 3-month SPI at the end of August in the United States
Corn Belt would capture precipitation trends during the important reproductive and early grainfilling stages for both corn and soybeans. Meanwhile, the 3-month SPI at the end of May gives anindication of soil moisture conditions as the growing season begins.

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STANDARDIZED PRECIPITATION INDEX USER GUIDE

It is important to compare the 3-month SPI with longer timescales. A relatively normal or even a
wet 3-month period could occur in the middle of a longer-term drought that would only bevisible over a long period. Looking at longer timescales can prevent misinterpretation believingthat a drought might be over when in fact it is just a temporary wet period. Continuous andpersistent drought monitoring is essential to determine when droughts begin and end. This helps
avoid “false alarms” when going into and coming out of drought. Having a set of “triggers” inplace, which are tied to actions within a drought plan, can help ensure this.
As with the 1-month SPI, the 3-month SPI may be misleading in regions where it is normally dry
during any given 3-month period. Large negative or positive SPIs may be associated with
precipitation totals not very different from the mean. This caution can be explained with theMediterranean climate of California and around northern Africa and southern Europe, where very
little rain falls or is expected over distinct periods of the year. Because these periods arecharacterized by little rain, the corresponding historical totals will be small, and relatively small
deviations on either side of the mean could result in large negative or positive SPIs. Conversely,
this time period can be a good indicator for some monsoon regions around the world.

5.1.3

6-month SPI

The 6-month SPI compares the precipitation for that period with the same 6-month period overthe historical record. For example, a 6-month SPI at the end of September compares theprecipitation total for the April–September period with all the past totals for that same period.
The 6-month SPI indicates seasonal to medium-term trends in precipitation and is still considered
to be more sensitive to conditions at this scale than the Palmer Index. A 6-month SPI can be very
effective in showing the precipitation over distinct seasons. For example, a 6-month SPI at theend of March would give a very good indication of the amount of precipitation that has fallen
during the very important wet season period from October through March for certain
Mediterranean locales. Information from a 6-month SPI may also begin to be associated withanomalous streamflows and reservoir levels, depending on the region and time of year.

5.1.4

9-month SPI

The 9-month SPI provides an indication of inter-seasonal precipitation patterns over a medium
timescale duration. Droughts usually take a season or more to develop. SPI values below -1.5 forthese timescales are usually a good indication that dryness is having a significant impact on
agriculture and may be affecting other sectors as well. Some regions may find that the patterndisplayed by the map of the Palmer Index is closely related the 9-month SPI maps. For otherareas, the Palmer Index is more closely related to the 12-month SPI. This time period begins tobridge a short-term seasonal drought to those longer-term droughts that may becomehydrological, or multi-year, in nature.

5.1.5

12-month up to 24-month SPIThe SPI at these timescales reflects long-term precipitation patterns. A 12-month SPI is acomparison of the precipitation for 12 consecutive months with that recorded in the same 12
consecutive months in all previous years of available data. Because these timescales are thecumulative result of shorter periods that may be above or below normal, the longer SPIs tend to
gravitate toward zero unless a distinctive wet or dry trend is taking place. SPIs of these timescales
are usually tied to streamflows, reservoir levels, and even groundwater levels at longer timescales.
In some locations, the 12-month SPI is most closely related with the Palmer Index, and the two
indices can reflect similar conditions.

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6.

9COMPUTATIONAL METHODOLOGYThe SPI is determined by normalizing the precipitation for a given station after it has been fitted
to a probability density function as described by McKee and others (1993, 1995), Edwards and
McKee (1997), and Guttman (1998). A full description of the SPI computational procedure can befound in McKee and others (1993, 1995) and Edwards and McKee (1997). The basics, as takenfrom Edwards (1997), are described below.

6.1

SPI methodology•

The SPI calculation for any location is based on the long-term precipitation record for adesired period. This long-term record is fitted to a probability distribution, which is thentransformed 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.

•

Drought, according to the SPI, starts when the SPI value is equal or below -1.0 and ends whenthe value becomes positive.

6.2

How it works•

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 estimation of both dry and wet periods.

•

Accumulated values can be used to analyse drought severity (magnitude).

•At least 30 years of continuous monthly precipitation data are needed but longer-term
records would be preferable.

•

SPI timescale intervals shorter than 1 month and longer than 24 months may be unreliable.

•

It is spatially invariant in its interpretation.

•Its probability-based nature (probability of observed precipitation transformed into an index)
makes it well suited to risk management and triggers for decision-making.

7.

HOW TO OBTAIN THE PROGRAM

The program is available in a Windows/PC version and can be downloaded for free.
The latest SPI program (SPI_SL_6.exe), sample files such as those described below andinstructions for Windows/PC use can be found at http://drought.unl.edu/MonitoringTools/
DownloadableSPIProgram.aspx.
The program can calculate up to 6 SPI time windows at one time for any given location. It wascompiled in C++ for PC and all libraries are included.

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8.

STANDARDIZED PRECIPITATION INDEX USER GUIDEHOW TO RUN THE PROGRAM IN WINDOWS

To run the program in Windows, simply follow the steps indicated below:
1. Set up an input file as in the following sample containing precipitation data from Falls City,
Nebraska:

All input files must follow this format, which features three columns indicating respectively theyear, month and monthly precipitation value. A header, usually the name of the station, must be
included at the top of the input file otherwise the program will produce an empty output file. The
precipitation total must NOT include decimals and can be in inches or millimetres.
NOTE: Pay attention to column spacing and missing data in the input file. If the monthly
precipitation value is missing for a particular month or months, one must use -99 for the missing
data value. Do not use a blank in the precipitation column. Zero is a valid value for typically dry
months in arid regions or for those locations having distinct wet or dry seasons. Ideally, one
would want at least 30 years of monthly/weekly data in order to have some confidence in thestatistics, but that would be the case with most indices when assessing any drought climatology
for a given location or region.
The input files can be generated with Excel or any text editor, but must be renamed with a .cor
extension before executing the program.
2. Right click on the SPI_SL_6.exe file and save it. Then execute (double click) the program andfollow the instructions in the pop-up window.

STANDARDIZED PRECIPITATION INDEX USER GUIDE

3. Choose the number of SPI timescales to be computed:

4. Specify the SPI timescales to be computed. In the example below, the user will generate five
SPI timescales or windows: 1-month, 3-month, 6-month, 9-month and 12-month SPIs:

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5. Enter an input and an output filename. It is recommended to adopt a naming system thatreflects the SPI analyses to be carried out in order to keep the results of each analysis separate:

The output file can be given any name but must have a .dat extension. It will be placed in thesame folder where the executable file is located.
The results can be processed with Microsoft Notepad or any other text or word processing
software. These files are saved as MS_DOS ASCII text files. The output data can then be plotted,
graphed or mapped by any means.
A sample output file for Fall City, Nebraska, is shown and described below. The input file was set
up to analyse the 1-, 3-, 6-, 9- and 12-month SPIs. The corresponding values appear in the third,
fourth, fifth, sixth and seventh columns:

STANDARDIZED PRECIPITATION INDEX USER GUIDE13

NOTE: The -99.00 value does not indicate missing data in this case. It simply reflects that, forexample, in the fourth column, one cannot have a 3-month SPI value until one is 3 months into
the period of record. The same applies to the last column where one does not see a 12-month SPI
until December 1949, or the twelfth month available for calculation. This becomes the first
12-month SPI generated.

9.

MAPPING CAPABILITIESMany countries are calculating and mapping the SPI and other drought indices or meteorologicalparameters on a regular basis. Below is an overview of the methods often employed in mapping
drought indicators.
There are multiple ways, such as the standard drought indicators and indices, to map
meteorological variables. Most drought-related data originate as point (“station-based” or “sitespecific”) data. These data serve their purposes, but it is often in map form that the data bestcommunicate a message based on a geographic context to the decision-maker trying tounderstand drought severity and spatial extent. The point data can be placed on a map, andderivative products or characteristics of that site can be provided for additional information. This
could include, for example, a time series plot of the indicator or index. The limitation of this levelof spatial detail is that information on what is taking place between points is not available.
A variety of techniques can be used to generate a continuous map of meteorological drought.
One such technique generates an interpolated surface of estimated values at locations betweensites based on mathematical relationships of the indicator or index between the original point
data. Often this produces a map that appears “natural”, but is still based on the data from specificpoints and is only as accurate as the original data and the interpolation technique. No single
interpolation method can be applied to all situations, and the most commonly used interpolation
techniques include Kriging, Spline, and Inverse Distance Weighting (IDW).
Each interpolation technique has its advantages and disadvantages. Some techniques are moreexact than others, but take longer to produce the desired output. The Kriging method, which has
its origins in geological applications and the mining industry, assumes that there is a relationshipbetween points that is non-random and changes over space. The Spline method is used whenminimizing the overall surface curvature is important. Inverse Distance Weighting (IDW) is used
when the data points are scattered but dense enough to represent local variations. The data, as
the name implies, are weighted to favour data closer in proximity to the point being processed.
Another technique that has been used for meteorological drought monitoring and mapping is tomap point data to grid cells. These data can also originate from airborne, radar, and satellite
sources. These gridded data products appear less “natural” than interpolated products, but they
are easier to use for comparative purposes because of the common grid cell sizes. These cells canvary in size from degrees down to meter(s) depending on the source and application needed.
They also vary in their temporal frequency, with return periods ranging from daily (or multiple
times a day) to weekly or longer. In the United States, gridded products for monitoring
meteorological drought are becoming much more common, while in other regions, particularly
Africa, there has been a long history of using gridded information to determine drought
conditions. The Famine Early Warning System (FEWS) and similar networks have utilized gridded
data in their analyses. Multiple examples of gridded meteorological drought products exist inAustralia, China, the United Kingdom and the United States.
To develop a gridded map product, the point data are aggregated to a grid cell resolution
selected for that product using a mathematical relationship. An interpolated surface is thencreated between the grid cells (not the point data). For example, the High Plains Regional Climate
Center, in partnership with the National Drought Mitigation Center, is mapping the Standardized
Precipitation Index on a daily basis at state, regional, and national scales across the United States.

STANDARDIZED PRECIPITATION INDEX USER GUIDE

14

–3

–2.5

–2

–1.5

–1

0

1

1.5

2

2.5

3

Source: United States High Plains Regional Climate Center

Example of 3-month SPI (1 May 2011 – 31 July 2011)

SPI maps are generated by using the Grid Analysis and Display System (GrADS). The discrete
station SPI data are interpolated using a Cressman objective analysis with radii of influence of
10,7,4,2,1. The grid resolution is 0.4 degrees. Gridded contour maps are generated at national,
regional and state levels for the High Plains region. For national maps, north polar stereographic(nps) projection is applied. Regional and state maps use a latitude/longitude (lat/lon) projection
with aspect ratio maintained. This interface and resultant products can be found at: http://www.
hprcc.unl.edu/maps/current/.
Successful mapping of meteorological drought depends on the quality of the data. Drought
indicator and index data quality is determined by several factors, including the availability of thedata, the timing of the recording, the quality of the historical data at a station, the transmission ofdata in near real time, the maintenance of the station network and the ability to measure
precipitation in cold temperatures, particularly in northern or alpine locations. Some of theseissues are related to the ability to provide the data in a timely fashion, which can be very
important with meteorological drought. Finally, the data density plays a huge role in the spatial
resolution that can be achieved in mapping drought.
One of the biggest challenges in mapping meteorological drought is to try and match the spatial
resolution that decision-makers need and demand with the information that is available today.
This limitation is related to the density of point data, which might not be available at theresolution desired by the decision-makers. Because of this challenge, the promise of potential
remote sensing-based products is encouraging. Some remote-sensing products can provide dataat spatial resolutions in regions where in situ point data are relatively sparse and unreliable. Most
satellite products are already incorporated into a grid cell (or “pixel”) as described above. In the
United States, some products are now being developed to utilize a combination of station-based
and remote-sensing data. The station data are used to help refine the remote-sensing data, andthe resulting “hybrid” maps have a higher level of accuracy.
Topographical issues, particularly those related to mountains and rapid terrain changes, representa real challenge when mapping meteorological drought. There are two reasons for this. First, data
density tends to be lower in mountainous regions. Second, because interpolation methodologies

STANDARDIZED PRECIPITATION INDEX USER GUIDE

15

are often based on correlations, the relationship between adjoining regions with regard to
precipitation tends to be particularly discontinuous in areas where the terrain changes rapidly andsignificantly. As a result, smoothed interpolated surfaces on a finished map product may notrealistically match natural variability, especially the indicators and indices related to precipitation.
Because of all the complexities involved in meteorological drought data and the characteristics ofmapping techniques, it is important that a decision-maker understands these factors wheninterpreting maps of drought severity and spatial extent.

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STANDARDIZED PRECIPITATION INDEX USER GUIDEREFERENCES
Edwards, D. C. and T. B. McKee, 1997: Characteristics of 20th century drought in the United States atmultiple time scales. Climatology Report 97-2, Department of Atmospheric Science, Colorado State
University, Fort Collins, Colorado.
Guttman, N.B., 1994: On the sensitivity of sample L moments to sample size. Journal of Climate,7(6):1026–1029.
Guttman, N.B., 1998: Comparing the Palmer drought index and the Standardized Precipitation Index.
Journal of the American Water Resources Association, 34(1):113–121.
——— , 1999: Accepting the Standardized Precipitation Index: a calculation algorithm. Journal of theAmerican Water Resources Association, 35(2):311–322.
McKee, T.B., N.J. Doesken and J. Kleist, 1993: The relationship of drought frequency and duration to timescale. In: Proceedings of the Eighth Conference on Applied Climatology, Anaheim, California,17–22 January
1993. Boston, American Meteorological Society, 179–184.
——— , 1995: Drought monitoring with multiple timescales. In: Proceedings of the Ninth Conference on
Applied Climatology, Dallas, Texas, 15–20 January 1995. Boston American Meteorological Society,233–236.
Vicente-Serrano, S.M., S. Beguería and J.I. López-Moreno, 2010: A multi-scalar drought index sensitive toglobal warming: the Standardized Precipitation Evapotranspiration Index – SPEI. Journal of Climate,23(7):1696–1718, doi: 10.1175/2009JCLI2909.1.
Wilhite, D.A. and M.H. Glantz, 1985: Understanding the drought phenomenon: the role of definitions.
Water International, 10:111–120.
Wu, H., M.D. Svoboda, M.J. Hayes, D.A. Wilhite and F. Wen, 2007: Appropriate application of theStandardized Precipitation Index in arid locations and dry seasons. International Journal of Climatology,27(1):65–79.

OTHER ONLINE RESOURCES
http://drought.unl.edu/MonitoringTools/DownloadableSPIProgram.aspx
http://drought.mssl.ucl.ac.uk/spi.html
http://www.wrcc.dri.edu/spi/spi.html
http://ccc.atmos.colostate.edu/standardizedprecipitation.php
http://www.wmo.int/drought

For more information, please contact:

World Meteorological Organization
7 bis, avenue de la Paix – P.O. Box 2300 – CH 1211 Geneva 2 – Switzerland
Communications and Public Affairs OfficeTel.: +41 (0) 22 730 83 14 – Fax: +41 (0) 22 730 80 27
E-mail: cpa@wmo.int

Tel.: +41 (0) 22 730 83 05 – Fax: +41 (0) 22 730 80 42
E-mail: agm@wmo.int
www.wmo.int/agm

P-CLW_12625

Climate and Water Department