Incorporation of SPEI in MSDP: Scopes and Possibilities

Hasan M Abdullah(PhD),Dept. of Agroforestry and Environment, BSMRAU, Gazipur

It is one of the main natural causes of agricultural, economic, and environmental damage (Burton et al.1978; Wilhite and Glantz 1985; Wilhite 1993).

Drought is an insidious natural hazard that results from lower levels of precipitations than usual.

Drought affects the whole water cycle, its triggering factor is a deficiency in precipitation(source:www.esipfed.org).

In the past drought has attracted less scientific attention than flood or cyclone, several authors found that the impact of drought can be more defenseless than flood and cyclone (e.g. Shahid and Behrawan, 2008; Shahid, 2008).

Water Security Crop Damage

Dust Strom Slum Fire

Impacts of drought

HydropowerDrainage System

Insect Outbreak (giant milibug) Navigation System

Forest fire Irrigation system

Livestock/Wildlife management

and so on……

Recently, a new drought index, the Standardized Precipitation–Evapotranspiration Index (SPEI), was proposed by Vicente-Serrano et al (2010).

The SPEI’s main advantage over other widely used drought indices lies in its ability to identify the role of evapotranspiration and temperature variability with regard to drought assessments in the context of global warming. SPEI can identify

drought prone area, severity, duration, onset, extent and end.

It is a simple multiscalar drought index (the SPEI) that combines precipitation and temperature data.

The SPEI uses the monthly (or weekly) difference between precipitation and PET (R-PET)

This represents a simple climatic water balance (Thornthwaite 1948) that is calculated at different time scales to obtain the SPEI.

The PET calculation is difficult because of the involvement of numerous parameters eg.

surface temperature, land use and land coverair humidity, soil incoming radiation, water vapor pressure, and ground–atmosphere latent and sensible heat fluxes (Allen et al. 1998).

Different methods have been proposed to indirectly estimate the PET from meteorological parameters measured at weather stations. According to data availability, such methods include physically based methods (e.g., the Penman–Monteith method; PM) and models based on empirical relationships, where PET is calculated with fewer data requirements.

The PM method has been adopted by International Commission on Irrigation and Drainage (ICID), Food and Agriculture Organization of the United Nations (FAO), andAmerican Society of Civil Engineers (ASCE) as the standard procedure for computing PET. The PM method requires large amounts of data because its calculation involves values for solar radiation, temperature, wind speed, and relative humidity. In the majority of regions of the world, these meteorological data are not available. Accordingly, alternative empirical equations have been proposed for PET calculation where data are scarce (Allen et al. 1998).

The purpose of including PET in the drought indexcalculation is to obtain a relative temporal estimation,and therefore the method used to calculate the PET isnot critical.

Mavromatis (2007) recently showed that the use of simple or complex methods to calculate the PET provides similar results when a drought index such as the PDSI, is calculated.

Therefore, Vincent followed the simplest approach to calculate PET (Thornthwaite1948), which has the advantage of only requiring data on monthly-mean temperature.

Calculation of SPEI can be done at different lag(period)

01 – month (Monthly SPEI)03 – month (Seasonal SPEI)06 – month (Short time SPEI)09 – month (Medium time SPEI)12 – month (Long time SPEI)24 – month (Long time SPEI)48 – month (Long time SPEI)

SPEI is calculated both temporally and spatially

Monthly SPEI

Seasonal SPEI

Long time SPEI

Period (lag)

B arisa lB h o la

B o g ra

C h an d p u r

C h ittag o n g (C ity )C h ittag o n g (A P )

C h u ad an g a

C o m illa

C o x 's B azar

D h ak a

D in a jp u r

F arid p u r

F en i

H a tiy a

Ish w ard i

Jesso re

K h ep u p ara

K h u ln a

K u tu b d ia

M ad arip u r

M aijd i C o u rt

M o n g la

M y m en sin g h

P a tu ak h a li

R a jsh ah i

R an g p u r

R an g am atiS an d w ip

S a tk h iraS itak u n d a

S rim o n g a l

S y ed p u r

S y lh e t

T an g a il

T ek n a f

89°E 90°E 91°E 92°E

21°N

22°N

23°N

24°N

25°N

26°N

32 BMD stationsOldest -1948With frequent missing data

Other than thoseNARSBWDBUniversities……..

Only 5 stations encircling the Mymensingh Was analyze

Range Condition

SPEI≤-2 Extreme dry

-2<SPEI≤-1.5 Severely dry

-1.5<SPEI≤-1 Moderately dry

-1<SPEI≤1 Near Normal

1<SPEI≤1.5 Moderately wet

1.5<SPEI≤2 Severely wet

SPEI≥2 Extremely wet

SPEI based Drought Severity Index

SPEI analysis based on BMD data with gap filling1970-2009

1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

year

spe

i

-20

00

-10

00

01

00

02

00

0

1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

year

spe

i

-20

00

-10

00

01

00

02

00

0

Yearly residual Rainfall of Mymensingh

1970 1971 1973 1975 1977 1978 1980 1982 1984 1985 1987 1989 1991 1992 1994 1996 1998 1999 2001 2003 2005 2006 2008

year

spe

i

-3-2

-10

12

3

1970 1971 1973 1975 1977 1978 1980 1982 1984 1985 1987 1989 1991 1992 1994 1996 1998 1999 2001 2003 2005 2006 2008

year

spe

i

-3-2

-10

12

3SPEI of BMD data 1970-2009

1970 1971 1973 1975 1977 1978 1980 1982 1984 1985 1987 1989 1991 1992 1994 1996 1998 1999 2001 2003 2005 2006 2008

year

spe

i

-3-2

-10

12

3

1970 1971 1973 1975 1977 1978 1980 1982 1984 1985 1987 1989 1991 1992 1994 1996 1998 1999 2001 2003 2005 2006 2008

year

spe

i

-3-2

-10

12

3

24 month lagMymensingh

48 month lagMymensingh

SPEI analysis based on SPEIbase

Climatic Research Unit of the University of East Anglia

spatial resolution of 0.5ºlat x 0.5ºlon1901-2011

1901 1905 1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1964 1969 1974 1979 1984 1989 1994 1999 2004 2009

year

spe

i

-3-2

-10

12

3

1901 1905 1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1964 1969 1974 1979 1984 1989 1994 1999 2004 2009

year

spe

i

-3-2

-10

12

3

1901 1905 1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1964 1969 1974 1979 1984 1989 1994 1999 2004 2009

year

spe

i

-3-2

-10

12

3

1901 1905 1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1964 1969 1974 1979 1984 1989 1994 1999 2004 2009

year

spe

i

-3-2

-10

12

3 6 month lagMymensingh

12 month lagMymensingh

WET

Dry

Rice/ Wheat

Sugarcane

1904 1909 1914 1919 1923 1928 1933 1938 1942 1947 1952 1957 1961 1966 1971 1976 1980 1985 1990 1995 1999 2004 2009

year

spe

i

-3-2

-10

12

3

1904 1909 1914 1919 1923 1928 1933 1938 1942 1947 1952 1957 1961 1966 1971 1976 1980 1985 1990 1995 1999 2004 2009

year

spe

i

-3-2

-10

12

3

1902 1907 1912 1917 1922 1927 1932 1937 1942 1947 1952 1957 1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011

year

spe

i

-3-2

-10

12

3

1902 1907 1912 1917 1922 1927 1932 1937 1942 1947 1952 1957 1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011

year

spe

i

-3-2

-10

12

324 month lagMymensingh

48 month lagMymensingh

Famine in 1974

Decades long drought

1901 1908 1915 1922 1929 1936 1943 1950 1957 1964 1971 1978 1986 1993 2000 2007

year

sp

ei

-3-2

-10

12

3

1901 1908 1915 1922 1929 1936 1943 1950 1957 1964 1971 1978 1986 1993 2000 2007

year

sp

ei

-3-2

-10

12

3

1901 1909 1917 1925 1933 1941 1949 1957 1965 1973 1981 1989 1998 2006

year

sp

ei

-3-2

-10

12

3

1901 1909 1917 1925 1933 1941 1949 1957 1965 1973 1981 1989 1998 2006

year

sp

ei

-3-2

-10

12

3

Bogra (SPEI48)

Dhaka (SPEI48)

1901 1908 1916 1923 1931 1938 1946 1954 1961 1969 1976 1984 1992 1999 2007

year

sp

ei

-3-2

-10

12

3

1901 1908 1916 1923 1931 1938 1946 1954 1961 1969 1976 1984 1992 1999 2007

year

sp

ei

-3-2

-10

12

3

1901 1909 1918 1927 1936 1945 1954 1962 1971 1980 1989 1998 2007

year

sp

ei

-3-2

-10

12

3

1901 1909 1918 1927 1936 1945 1954 1962 1971 1980 1989 1998 2007

year

sp

ei

-3-2

-10

12

3

Sylhet (SPEI48)

Tangail (SPEI48)

Last 50 yearsDrought event increasedDrought move from W to E

Drought Scenario in Bangladesh (n=5)

April May

June July

Regional drought scenario 1994

August Sept

October November

Example from Czech Republic (Vera Potop )

There are scopes

1.Drought monitoring in agricultural land of MSDP

2.Understanding urban comfort/ Urban heat island

3. Relevant authority might arrange and share SPEI data with water, fire, drainage, insect management …dept.

4.SPEI has potential to integrate with other layers of information for spatial planning, NRM, modeling etc.

Thank you….

Dhaka Rainfall SeriesDhaka Rainfall Series

Dhaka Monthly Rainfall(mm)1970-2009

0

100

200

300

400

500

600

700

800

900

1970 1975 1980 1985 1990 1995 2000 2005

Dhaka annual rainfall residuals (1970-2009)

-1000

-800

-600

-400

-200

0

200

400

600

800

1000

1970 1975 1980 1985 1990 1995 2000 2005

resi

dual

rain

fall

(mm

)

Searching for trends in rainfall dataSearching for trends in rainfall data

Increased variance