applicability of drought indices to monitor multi-sector ... · pdf filesergio m....

Applicability of drought indices to monitor multi-sector impacts:

“The Standardized Precipitation Evapotranspiration Index – SPEI”

Sergio M. Vicente-Serrano, Santiago Beguería and Juan I. López-Moreno

Spanish National Research Council, CSIC, Zaragoza, SpainSpanish National Research Council, CSIC, Zaragoza, Spain

• Drought is a complex natural phenomena without a general and commonly accepted definition

• In contrast to other extreme events such as floods, which are typically restricted to small regions and well-defined temporal intervals, droughts are difficult to pinpoint in time and space, affecting wide areas over long periods of time.

• It is very difficult to isolate the beginning of a drought, as drought development is slow and very often the drought is not recognized until human activities, or the environment, are affected. Moreover, the effects of a drought can persist over many years after it has ended.

Challenges for drought analysis and monitoring

• It is very difficult to objectively quantify their characteristics in terms of intensity, magnitude, duration and spatial extent.

• We identify a drought by its effects at different levels, but there is not a physical variable we can measure to quantify droughts.

Given the large dificulties to objectively quantify their characteristics (duration, intensity, magnitude, spatial extent, onset, etc.) several drought indices have been developed in the last decades (Heim, 2002: Bulletin of the American Meteorological Society, 83: 1149-1165)

At present the two most widely used drought indices are:

Palmer Drought Severity Index Standardised Precipitation IndexPalmer (1965) McKee et al. (1993)

Based on a simplified water balance equation.

Incorporates prior precipitation, moisture supply, runoff and evaporation

demand at the surface level

Based on precipitation anomalies

sc-PDSI and 18-month SPI at Indore (India)

• A critical issue in the study of the drought impacts is the multi-scalar nature of drought, since the response of the hydrological (soil moisture, groundwater, river discharge, reservoir storage, etc) and biological (crops, natural vegetation, etc) systems to water shortage varies markedly and have different response times.

• This explains why severe drought conditions can be recorded in one system (e.g., low river flows) whereas other systems in the same region (e.g., crops) have normal or even humid conditions.

The importance of time-scales

• The time period from the arrival of water inputs to availability of a given usable resource differs considerably. Thus, the time scale over which water deficits accumulate becomes extremely important, and functionally separates hydrological, environmental, agricultural and other droughts. For this reason, drought indices must be associated with a specific timescale to be useful for monitoring and management of different usable water resources.

• This explains the wide acceptance of the SPI.

1-monthSP

I

-3-2-10123

3-month

SPI

-3-2-10123

12-month

SPI

-3-2-10123

48-month

1950 1960 1970 1980 1990 2000

SPI

-3-2-10123


1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

z-va

lues

-3

-2

-1

0

1

2

3

3-months SPEIInflows

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

z-va

lues

-3

-2

-1

0

1

2

3

40-months SPEIReservoir storages

Inflows

Time-scale0 5 10 15 20 25 30 35 40 45

R-P

ears

on

0.0

0.2

0.4

0.6

0.8

1.0

SPEISPI Storages

Time-scale0 5 10 15 20 25 30 35 40 45

R-P

ears

on

0.0

0.2

0.4

0.6

0.8

1.0

SPEISPI

Lorenzo-Lacruz, J., Vicente-Serrano, S.M., López-Moreno, J.I., Beguería, S., García-Ruiz, J.M., Cuadrat, J.M. (2010) The impact of droughts and water management on various hydrological systems in the headwaters of the Tagus River (central Spain). Journal of Hydrology, 386: 13-26.


-3

-2

-1

0

1

2

3

1950 1954 1958 1962 1966 1970 1975 1979 1983 1987 1991 1995 2000 2004

SSI


López-Moreno, J.I., S.M., Vicente-Serrano, J. Zabalza, S. Beguería, J. Lorenzo-Lacruz, C. Azorin-Molina, E. Morán-Tejeda. Hydrological response to climate variability at different time scales: a study in the Ebro basin. Journal of Hydrology. Under review

PC 1

SPEI time-scale

5 10 15 20 25 30 35 40 45

Mon

th

1

2

34

5

6

78

9

1011

12

PC 1

SPEI time-scale

5 10 15 20 25 30 35 40 45

Mon

th

1

23

4

56

78

9

1011

12


March 1993 March 1998

March 2003 March 2006

Monthly 8 km. Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index dataset obtained from 25 years of daily NOAA-AVHRR satellite data.


Month

Jan Feb Mar Apr May Jun J ul Aug Sep Oct N ov Dec

SPE

I Tim

e-sc

ale

(mon

th)

2

4

6

8

10

12

14

16

18

20

22

24

-0.7 -0.5 -0.3 -0.1 0.1 0.3 0.5 0.7

Month

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

SP

EI T

ime-

scal

e (m

onth

)

2

4

6

8

10

12

14

16

18

20

22

24

-0.7 -0.5 -0.3 -0.1 0.1 0.3 0.5 0.7

Month


SPE

I Tim

e-sc

ale

(mon

th)

2

4

6

8

10

12

14

16

18

20

22

24

-0.7 -0.5 -0.3 -0.1 0.1 0.3 0.5 0.7

Month


SP

EI T

ime-

scal

e (m

onth

)

2

4

6

8

10

12

14

16

18

20

22

24

-0.7 -0.5 -0.3 -0.1 0.1 0.3 0.5 0.7

Month


SP

EI T

ime-

scal

e (m

onth

)

2

4

6

8

10

12

14

16

18

20

22

24

-0.7 -0.5 -0.3 -0.1 0.1 0.3 0.5 0.7

Month

Jan Feb Mar Apr May Jun J ul Aug Sep Oct Nov D ec

SPEI

Tim

e-sc

ale

(mon

th)

2

4

6

8

10

12

14

16

18

20

22

24

-0.7 -0.5 -0.3 -0.1 0.1 0.3 0.5 0.7

Month


SP

EI T

ime-

scal

e (m

onth

)

2

4

6

8

10

12

14

16

18

20

22

24

-0.7 -0.5 -0.3 -0.1 0.1 0.3 0.5 0.7

Canadian praires North Mexico

Month


SPE

I Tim

e-sc

ale

(mon

th)

2

4

6

8

10

12

14

16

18

20

22

24

-0.7 -0.5 -0.3 -0.1 0.1 0.3 0.5 0.7

N-East Brazil

Month


SPE

I Tim

e-sc

ale

(mon

th)

2

4

6

8

10

12

14

16

18

20

22

24

-0.7 -0.5 -0.3 -0.1 0.1 0.3 0.5 0.7

Sahel

Month


SP

EI T

ime-

scal

e (m

onth

)

2

4

6

8

10

12

14

16

18

20

22

24

-0.7 -0.5 -0.3 -0.1 0.1 0.3 0.5 0.7

Ib. Pen.-Morocco

N-West Australia South Africa East Africa Argentina Kazakhstan

Month


SPE

I Tim

e-sc

ale

(mon

th)

2

4

6

8

10

12

14

16

18

20

22

24

-0.7 -0.5 -0.3 -0.1 0.1 0.3 0.5 0.7

Month


SP

EI T

ime-

scal

e (m

onth

)

2

4

6

8

10

12

14

16

18

20

22

24

-0.7 -0.5 -0.3 -0.1 0.1 0.3 0.5 0.7

Month


SPE

I Tim

e-sc

ale

(mon

th)

2

4

6

8

10

12

14

16

18

20

22

24

-0.7 -0.5 -0.3 -0.1 0.1 0.3 0.5 0.7

Month


SP

EI T

ime-

scal

e (m

onth

)

2

4

6

8

10

12

14

16

18

20

22

24

-0.7 -0.5 -0.3 -0.1 0.1 0.3 0.5 0.7

Month


SP

EI T

ime-

scal

e (m

onth

)

2

4

6

8

10

12

14

16

18

20

22

24

-0.7 -0.5 -0.3 -0.1 0.1 0.3 0.5 0.7

Month

Jan Feb Mar Apr May Jun J ul Aug Sep Oct Nov D ec

SPEI

Tim

e-sc

ale

(mon

th)

2

4

6

8

10

12

14

16

18

20

22

24

-0.7 -0.5 -0.3 -0.1 0.1 0.3 0.5 0.7

Month


SP

EI T

ime-

scal

e (m

onth

)

2

4

6

8

10

12

14

16

18

20

22

24

-0.7 -0.5 -0.3 -0.1 0.1 0.3 0.5 0.7

Canadian praires North Mexico

Month


SPE

I Tim

e-sc

ale

(mon

th)

2

4

6

8

10

12

14

16

18

20

22

24

-0.7 -0.5 -0.3 -0.1 0.1 0.3 0.5 0.7

N-East Brazil

Month


SPE

I Tim

e-sc

ale

(mon

th)

2

4

6

8

10

12

14

16

18

20

22

24

-0.7 -0.5 -0.3 -0.1 0.1 0.3 0.5 0.7

Sahel

Month


SP

EI T

ime-

scal

e (m

onth

)

2

4

6

8

10

12

14

16

18

20

22

24

-0.7 -0.5 -0.3 -0.1 0.1 0.3 0.5 0.7

Ib. Pen.-Morocco

N-West Australia South Africa East Africa Argentina Kazakhstan


P.halepensis

19501955 1960 1965 1970 1975 1980 1985 1990 1995200020050.0

0.5

1.0

1.5

2.0

2.5

3.0

P. pinea

1950 1955 1960 1965 1970 1975 1980198519901995 2000 2005

0.0

0.5

1.0

1.5

2.0

2.5

3.0

P.nigra

Year1950 1955196019651970 1975 1980 1985 1990 1995 2000 2005

0.0

0.5

1.0

1.5

2.0

2.5

3.0J.thurifera

Year195019551960 1965 19701975198019851990199520002005

0.0

0.5

1.0

1.5

2.0

2.5

3.0

P. sylvestris

1950 1955 1960 1965 1970 1975 1980198519901995 2000 20050.0

0.5

1.0

1.5

2.0

2.5

3.0

Q.ilex

Years1950 1955 1960 1965197019751980 1985 1990 1995 2000 2005

0.0

0.5

1.0

1.5

2.0

2.5

3.0

A.alba

1950 1955196019651970 1975 1980 1985 1990 1995 2000 20050.0

0.5

1.0

1.5

2.0

2.5

3.0

Q.faginea

Years1950 1955196019651970 1975 1980 1985 1990 1995 2000 2005

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Res

idua

l ind

ices

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

P. pinea P. halepensis

Cor

rela

tion

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7 Q. ilex P. nigra Q. faginea J. thurifera

Months

0 5 10 15 20 25 30 35 40 45-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7 P. sylvestris A. alba

Pasho, E., J. Julio Camarero, Martín de Luis and Sergio M. Vicente-Serrano: Drought impacts on forest growth in a semi arid region in north-eastern Spain. Agricultural and Forest meteorology. Under review.


Promedio por especies P. halepensis Quercus sp.

Pasho, E., J. Julio Camarero, Martín de Luis and Sergio M. Vicente-Serrano: Drought impacts on forest growth in a semi arid region in north-eastern Spain. IAgricultural and Forest meteorology. Under review.


The SPI calculation is based on two assumptions:

1. the variability of precipitation is much higher than that of other variables, such as temperature and potential evapotranspiration (PET)

2. the other variables (PET) are stationary (i.e. they have no temporal trend).

In this scenario droughts are controlled by the temporal variability in precipitation. However, some authors have warned against systematically neglecting the importance of the effect of temperature on drought conditions.

There has been a general temperature increase (0.5-2°C) during the last past 150 years (Jones and Moberg, 2003), and climate change models predict a marked increase during the 21st century (IPCC, 2007). It is expected that this will have dramatic consequences for drought conditions, with an increase in water demand due to evapotranspiration (Sheffield and Wood, 2008; Dubrovsky et al., 2008).

The use of drought indices which include temperature data in their formulation (such as the PDSI) is preferable, especially for applications involving future climate scenarios.

However, the PDSI lacks the multi-scalar character essential for both assessing drought in relation to different hydrological systems, and differentiating among different drought types.

We need a new drought index based on precipitation and PET and combining the sensitivity of PDSI to changes in evaporation demand and the multi-temporal nature of the SPI. The index must be suited to detecting, monitoring and exploring the consequences of global warming on drought conditions.

Syed et al. (2008): Water Resources Research, VOL. 44, W02433, doi:10.1029/2006WR005779, 2008

The importance of the evapotranspiration processes on droughts

Lobo, A. and Maisongrande, P., 2006. Hydrology and Earth System Sciences, 10: 151-164


1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

SP

I

-3-2-10123

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

SPE

I

-3-2-10123

2002 2003 2004 2005

SP

EI

-3

-2

-1

0

1

2

3

2002 2003 2004 2005

SPI

-3

-2

-1

0

1

2

3

47ºN – 2º E


Sc-PDSI-Original

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Sc-P

DS

I

-8-6-4-20246

Sc-PDSI-Precipitat ion change

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Sc-

PDSI

-8-6-4-20246

SPI (18 months)-Precipitat ion change

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

SPI

-3-2-10123

SPI (18 months)-Original

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

SPI

-3-2-10123

Difference

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Sc-

PDS

I

-6-4-2024

Difference

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

SP

I

-1.0

-0.5

0.0

0.5

1.0

Under precipitation changes both the PDSI and the SPI are equally sensitive

PDSI and 18-month SPI at the Albuquerque (New Mexico, USA) observatory (1910−2007). Both indices were calculated from precipitation series containing a linear reduction of 15% between 1910 and 2007. The difference between the indices is also

shown.

Sc-PDSI-Original

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Sc-

PD

SI

-8-6-4-20246

Sc-PDSI-Temperature change (2ºC)

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Sc-

PD

SI

-8-6-4-20246

Difference

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Sc-

PD

SI

-6-4-2024

Sc-PDSI-Temperature change (4ºC)

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Sc-

PD

SI

-8-6-4-20246

Difference

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Sc-

PD

SI

-6-4-2024

Evolution of the sc-PDSI at Albuquerque (New Mexico, USA) between 1910 and 2007, and under lineal temperature increase scenarios of 2ºC and 4ºC during the same period. The difference between the indices is also shown.

But under temperature changes only the PDSI is sensitive

We describe here a simple multi-scalar drought index (the Standardised Precipitation Evapotranspiration Index) that combines precipitation and temperature data.

The SPEI uses the monthly (or weekly) difference between precipitation and PET. This represents a simple climatic water balance (Thornthwaite, 1948) which is calculated at different time scales to obtain the SPEI. We followed the simplest approach to calculate PET (Thornthwaite, 1948), which has the advantage of only requiring data on monthly mean temperature.

With a value for PET, the difference between the precipitation (P) and PET for the month i is calculated according to:

Di = Pi-PETi,

The calculated D values are aggregated at different time scales:

where k (months) is the timescale of the aggregation and n is the calculation month.

∑−

=−− −=

1k

0iinin

kn PETPD

The Standardized Precipitation Evapotranspiration Index (SPEI)

The probability density function of a three parameter Log-logistic distributed variable is expressed as:

where a, β and γ are scale, shape and origin parameters, respectively, for D values in the range (γ > D <∞ ).

Parameters of the Log-logistic distribution can be obtained following different procedures. Among them, the L-moment procedure is the most robust and easy approach (Ahmad et al., 1988). When L-moments are calculated, the parameters of the Pearson III distribution can be obtained following Singh et al. (1993):

where Γ(β) is the gamma function of β.

21

1)(−−

⎟⎟⎠

⎞⎜⎜⎝

⎛⎟⎠⎞

⎜⎝⎛ −

+⎟⎠⎞

⎜⎝⎛ −

=ββ

αγ

αγ

αβ xxxf

201

01

662

wwwww−−

−=β

)11()11()2( 10

βββα−Γ+Γ

−=

ww)11()11(0 ββαγ −Γ+Γ−= w

The probability distribution function of the D series according to the Log-logistic distribution is given by:

F(x) values for the D series at different time scales adapt very well to the empirical F(x) values at the different observatories, independently of the climate characteristics and the time scale of the analysis.

With F(x) the SPEI can easily be obtained as the standardized values of F(x). For example, following the classical approximation of Abramowitz and Stegun (1965).

1

1)(−

⎥⎥⎦

⎤

⎢⎢⎣

⎡⎟⎟⎠

⎞⎜⎜⎝

⎛−

+=β

γα

xxF

Albuquerque (3 months)

P-PET

-300 -200 -100 0

F(x)

0.0

0.2

0.4

0.6

0.8

1.0Albuquerque (6 months)

P-PET

-400 -300 -200 -100 0

F(x)

0.0

0.2

0.4

0.6

0.8


P-PET

-700 -600 -500 -400 -300 -200 -100

F(x)

0.0

0.2

0.4

0.6

0.8


P-PET

-1200 -1000 -800 -600 -400 -200

F(x)

0.0

0.2

0.4

0.6

0.8

1.0

Sao Paulo (3 months)

P-PET

0 200 400 600 800 1000

F(x)

0.0

0.2

0.4

0.6

0.8

1.0Sao Paulo(6 months)

P-PET

-200 0 200 400 600 800 1000 1200 1400

F(x)

0.0

0.2

0.4

0.6

0.8


P-PET

-200 0 200 400 600 800 1000 1200 1400 1600

F(x)

0.0

0.2

0.4

0.6

0.8


P-PET

0 500 1000 1500 2000

F(x)

0.0

0.2

0.4

0.6

0.8

1.0

Helsinki

(3 months)

P-PET

-100 0 100 200 300

F(x)

0.0

0.2

0.4

0.6

0.8

1.0Helsinki

(6 months)

P-PET

-200 0 200 400

F(x)

0.0

0.2

0.4

0.6

0.8

1.0Helsinki

(12 months)

P-PET

0 200 400 600 800

F(x)

0.0

0.2

0.4

0.6

0.8

1.0Helsinki

(24 months)

P-PET

0 200 400 600 800 1000 1200 1400

F(x)

0.0

0.2

0.4

0.6

0.8

1.0

Theoretical according the Log-logistic distribution (black line) vs. empirical (dots) F(x) values for D series at time scales of 3, 6, 12 and 24 months for the observatories at Albuquerque, Sao Paulo and Helsinki.

SPEI (3 months)

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

SPEI

-3-2-10123

SPEI (12 months)

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

SPEI

-3-2-10123

SPEI (24 months)

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

SPEI

-3-2-10123

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

SPI

-3-2-10123

SPI (12 months)

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

SPI

-3-2-10123

SPI (24 months)

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

SPI

-3-2-10123

Sc-PDSI

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Sc-P

DSI

-6-4-20246

SPI (3 months)

sc-PDSI, 3-, 12- and 24-month SPI and SPEI at Sao Paulo (1910−2007).

A)Albuquerque (3 months)

P-PET

-100 -50 0 50 100

F(x)

0.0

0.2

0.4

0.6

0.8


P-PET

-300 -200 -100 0

F(x)

0.0

0.2

0.4

0.6

0.8


P-PET

-1400 -1200 -1000 -800 -600 -400 -200

F(x

)

0.0

0.2

0.4

0.6

0.8


P-PET

-800 -600 -400 -200 0

F(x

)

0.0

0.2

0.4

0.6

0.8

1.0


P-PET

-200 0 200 400 600

F(x

)

0.0

0.2

0.4

0.6

0.8


P-PET

-200 0 200 400 600 800 1000 1200

F(x

)

0.0

0.2

0.4

0.6

0.8


P-PET

-500 0 500 1000

F(x

)

0.0

0.2

0.4

0.6

0.8


P-PET

-500 0 500 1000 1500 2000

F(x

)

0.0

0.2

0.4

0.6

0.8

1.0

Helsinki (3 months)

P-PET

-100 0 100 200 300F

(x)

0.0

0.2

0.4

0.6

0.8

1.0Helsinki (6 months)

P-PET

-200 -100 0 100 200 300 400

F(x

)

0.0

0.2

0.4

0.6

0.8


P-PET

0 200 400 600 800

F(x

)

0.0

0.2

0.4

0.6

0.8

1.0Helsinki(24 months)

P-PET

0 200 400 600 800 1000 1200 1400

F(x

)

0.0

0.2

0.4

0.6

0.8

1.0

B)Albuquerque (3 months)

P-PET

-250 -200 -150 -100 -50 0 50

F(x)

0.0

0.2

0.4

0.6

0.8


P-PET

-600 -400 -200 0

F(x)

0.0

0.2

0.4

0.6

0.8


P-PET

-1600 -1400 -1200 -1000 -800 -600 -400 -200

F(x

)

0.0

0.2

0.4

0.6

0.8


P-PET

-800 -600 -400 -200 0

F(x

)

0.0

0.2

0.4

0.6

0.8

1.0


P-PET

-400 -200 0 200 400 600

F(x

)

0.0

0.2

0.4

0.6

0.8


P-PET

-400 -200 0 200 400 600 800 1000

F(x

)

0.0

0.2

0.4

0.6

0.8


P-PET

-400 -200 0 200 400 600 800 1000 1200 1400F

(x)

0.0

0.2

0.4

0.6

0.8


P-PET

-500 0 500 1000 1500 2000

F(x

)

0.0

0.2

0.4

0.6

0.8

1.0

Helsinki (3 months)

P-PET

-100 0 100 200 300

F(x

)

0.0

0.2

0.4

0.6

0.8


P-PET

-200 -100 0 100 200 300 400

F(x

)

0.0

0.2

0.4

0.6

0.8


P-PET

0 200 400 600 800

F(x

)

0.0

0.2

0.4

0.6

0.8

1.0Helsinki(24 months)

P-PET

0 200 400 600 800 1000 1200 1400

F(x

)

0.0

0.2

0.4

0.6

0.8

1.0

Theoretical according the log-logistic distribution (black line) vs. empirical (dots) F(x) values for D series at time scales of 3, 6, 12 and 24 months for the observatories at Albuquerque, Sao Paulo and Helsinki. A) Temperature increase of 2ºC.

B) Temperature increase of 4 ºC.

Under warming conditions…

SPI (18 months)

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

SPI

-3-2-10123

SPEI (18 months)

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

SPE

I

-3-2-10123

SPEI (18 months) + 2ºC

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

SPE

I

-3-2-10123

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Sc-

PDS

I

-8-6-4-202468

Sc-PDSI

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Sc-P

DSI

-8-6-4-202468

Sc-PDSI + 2ºC

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Sc-P

DSI

-8-6-4-202468

SPEI (18 months) + 4ºC

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

SPE

I

-3-2-10123

Sc-PDSI + 4ºC

Evolution of the sc-PDSI, and 18-month SPI and SPEI in Abashiri (Japan). The original series (1910−2007) and the sc-PDSI and SPEI were calculated for a temperature series with a lineal increase

of 2ºC and 4ºC throughout the analyzed period.

SPEI (1 month)

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

SPE

I

-3-2-10123

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

SPEI

-3-2-10123

Sc-PDSI

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Sc-

PD

SI

-8-6-4-202468

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

SPEI

-3-2-10123

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

SPE

I

-3-2-10123

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

SPEI

-3-2-10123

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

SPE

I

-3-2-10123

SPEI (3 month)

SPEI (6 month)

SPEI (12 month)

SPEI (18 month)

SPEI (24 month)

Evolution of the sc-PDSI, and 1-, 3-, 6-, 12-, 18- and 24-month SPEI at Tampa (Florida, USA) under a 4ºC temperature increase scenario relative to the origin

• According to the sc-PDSI, under global warming temperature will play a more important role than precipitation in explaining drought conditions. This phenomenon can also be assessed using the SPEI, which was very similar to the sc-PDSI under the two temperature increase scenarios tested. This suggests that the SPEI should be used in preference to the sc-PDSI, given the former index’s simplicity, lower data requirements and multi-scalar properties.

• The SPEI can account for the possible effects of temperature variability and temperature extremes beyond the context of global warming. Therefore, given the minor additional data requirements of the SPEI relative to the SPI, use of the former is preferable for the identification, analysis and monitoring of droughts in any climate region of the world.

• The SPEI fulfils the requirements of a drought index since its multi-scalar character enables it to be used by different scientific disciplines/sectors to detect, monitor and analyze droughts. Like the sc-PDSI and the SPI, the SPEI can measure drought severity according to its intensity and duration, and can identify the onset and end of drought episodes. The SPEI allows comparison of drought severity through time and space, since it can be calculated over a wide range of climates, as can the SPI. The SPEI is statistically robust and easily calculated, and has a clear and comprehensible calculation procedure.

• A crucial advantage of the SPEI over the most widely used drought indices that consider the effect of PET on drought severity is that its multi-scalar characteristics enable identification of different drought types and impacts in the context of global warming.

1960 1970 1980 1990 2000 2010

SP

EI

-3-2-10123

1960 1970 1980 1990 2000 2010S

PI

-3-2-10123

Moscow (Russia)

2000 2010

2000 2010

Advantages of the SPEI

SSI

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

z-un

its

-3-2-10123

(sc)PDSI

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

PDS

I

-6-4-20246

4-month SPEI

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

z-un

its

-3-2-10123

17-months SPEI

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

z-un

its

-3-2-10123

SSI

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

z-un

its

-3-2-10123

(sc)PDSI

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

PDSI

-6-4-20246

31-month SPEI

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

z-un

its

-3-2-10123

14-months SPEI

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

z-un

its

-3-2-10123

Mississippi

St. Lawrence


Tools and datasets

http://sac.csic.es/spei/

http://sac.csic.es/spei/

Tools and datasets

Tools and datasets

Updates and improvements:

Method of calculation of the log-logistic parameters: unbiased estimator, maximum likelihood.

Including different options to obtain the PET (Hargreaves, Penmann).

Dataset updated to 2009.

Real time monitoring from global products.

Vicente-Serrano S.M., Santiago Beguería, Juan I. López-Moreno, (2010) A Multi-scalar drought index sensitive to global warming: The Standardized Precipitation Evapotranspiration Index – SPEI. Journal of Climate 23: 1696-1718.

Vicente-Serrano, S.M., Beguería, S., López-Moreno, J.I., Angulo, M., El Kenawy, A. (2010): A new global 0.5° gridded dataset (1901-2006) of a multiscalar drought index: comparison with current drought index datasets based on the Palmer Drought Severity Index. Journal of Hydrometeorology. 11: 1033–1043

Beguería, S., Vicente-Serrano, S.M. Angulo, M., (2010): A multi-scalar global drought data set: the SPEIbase: A new gridded product for the analysis of drought variability and impacts. Bulletin of the American Meteorological Society. 91, 1351-1354

Vicente-Serrano, S.M., Juan I. López-Moreno, Santiago Beguería, Jorge Lorenzo-Lacruz, Cesar Azorin-Molina and Enrique Morán-Tejeda (2011): Accurate computation of a streamflow index. Journal of Hydrologic Engineering doi:10.1061/(ASCE)HE.1943-5584.0000433

References:

applicability of drought indices to monitor multi-sector ... · pdf filesergio m....

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