economic cooperation organization training course on “drought and desertification” alanya...
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Economic Cooperation Organization Training Course on “Drought and Desertification”
Alanya Facilities, Antalya, TURKEY
presented by Ertan TURGU from Turkish State Meteorological Service
14 - 16 November 2006
Standardized Precipitation Index (SPI)
What is Standardized Precipitation Index (SPI)? A statistical method for assessing rainfall More representative than using the average rainfall as a representation of what is “normal”. it is designed to quantify the precipitation deficit for multiple time scales such as 1,3,6,9,12,24 months The SPI is a dimensionless index where negative values indicate drought, but positive values show wet conditions.
Advantages: minimal data requirements (only monthly precipitation data) simple and quick can be calculated for varying time scales can provide early warning of drought can help assess drought severity can answer such questions as; when, how long, and how severe a drought is.
Disadvantages (from practical applications): more suitable theoretical probability distribution can be found to model the raw precipitation data limitation from the standardization process of index itself. drought measured by the SPI can occur with the same frequency at all locations misleadingly large positive or negative SPI values may result when the index is applied at short time steps to regions of low seasonal precipitation.
A deficit of precipitation has different impacts on the ground water, reservoir storage, soil moisture, snowpack, and streamflow. Soil moisture conditions respond to precipitation anomalies on a relatively short scale, while ground water, streamflow, and reservoir storage reflect the longer-term precipitation anomalies. Thus, McKee et al (1993) originally calculated the SPI for 3,6,12,24 and 48 month time scales. This long term record is fitted to a gamma probability distribution, which is then transformed into a normal distribution. Positive SPI values indicate greater than median precipitation, while negative values indicate less than median precipitation.
Sequence of Drought Impacts: When drought begins, the agricultural sector is usually the first to be affected because of its heavy dependence on stored soil-water. Those who rely on surface water (i.e, reservoirs and lakes) and subsurface water (i.e,ground water) are usually the last to be affected.
,river,lake,reservoir
METHODOLOGY:
The SPI computation is based on the long term precipitation data for the desired time step. It is simply calculated by taking the difference of the precipitation from the mean for a particular time scale, and then dividing it by the standard deviation.
McKee et al.(1993) defined the criteria for a “drought event” for any of the time steps and classified the SPI to define various drought intensities.
SPI Values Drought Category 2.00 + extremely wet 1.50 to 1.99 very wet 1.00 to 1.49 moderately wet - 0.99 to 0.99 near normal - 1.00 to - 1.49 moderate drought - 1.50 to - 1.99 severe drought - 2.00 and less extreme drought
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Calculation of the SPI:Calculation of the SPI:Δ The SPI computation is based on the long term precipitation data for the desired time step. Thom (1958) found the gamma distribution to fit precipitation time series well. The gamma distribution is defined by its frequency or probability density function:
where α >0 is a shape parameter, β >0 is a scale parameter and x >0 is amount of precipitation.
Δ Fitting the distribution to data requires α and β to be estimated. They are estimated for each station, for each time step of interest (3,6,9,12,48 months, etc) and for each month of the year. Integrating the probability density function with respect to x and inserting the estimates of α and β yields an expression for the cumulative probability G(x) of an observed amount of precipitation occurring for a given month and time step:
Δ Since the gamma distribution is undefined for x=0 and q=P(x=0) >0 where P(x=0) is the probability of zero precipitation, the cumulative probability becomes as follow:
Δ The cumulative probability distribution is then transformed into the standard normal distribution to yield the SPI.
By using this software, spatial and temporal dimensions of meteorological droughts can be investigated from vulnerability concept based on frequency and severity of drought events at multiple time steps.
Drought Occurences and Analysis:▲ The index has been applied to long-term precipitation data at 101 stations for 1951-2001 period.▲ Here, our aim is to identify some areas vulnerable to drought at comparable time steps based on their occurence frequencies. The resulting SPI values at corresponding drought categories were mapped using Surfer which is a grid based contour software.
AD AP AZAR I
AD IYA M A N
AFYO N
AG RI
AK SA R A Y
AM AS YA
AN TA KY A
AN TA LYA
AR D AH A NAR TVIN
AY D IN
BA LIK ES IR
BA R TIN
BA TM AN
BA YB U R TBILEC IK
BIN G O L
BITLIS
BO LU
BU R D U R
BU R S A
C A N K IR I
D EN IZLID IY AR BA KIR
ELA ZIG
ER ZIN C A N
ER ZU R U M
ES KIS EH IR
G AZIA N TEP
G IR ES U N
G U M U SH AN E
H AK KA R I
IG D IR
KA H R AM AN M A R A S
KA R A M A N
KA R S
KA STA M O N U
KA YS ER I
K ILIS
K IR IKK ALE
KIR KLAR ELI
K IR SE H IR
KO N Y A
KU TA H YA
M A LATYA
M A NIS A
M A R D IN
M E R S IN
M U G LA
M U S
N E VS EH IR
N IG DE
O R D UR IZE
SA M S U N
SA N LIU R FA
SIIR T
SIN O P
SIV AS
TEK IR D A G
TO KA T
TR AB ZO N
TU N C E LI
U S AKVA N
Y O ZG AT
ZO N G U LD A K
SIR NA K
C O R U M
O SM A NIY E
KA R AB U K
28° 32° 36° 40° 44°
40°
36°
44°40°36°32°28°
0 50 100
150
200
250
km
36°
40°
BLAC K SEA
M ED ITER R AN EAN SEA
GR
EE
CE
BU LG AR IA
G EO RG IA
AR M EN IA
IRAN
IR AQ
SYR IA
DMİ
İSTAN BU L
AN KAR A
ISP AR TA
ED İR N E
YA LO VA
İZM İT
Ç AN AK KA LE
AD AN A
İZM İR
5 7 9 11 13
3 - M O N TH M O D ER ATE D R O U G H T O C C U R R EN CES (% )
ADAPAZAR I
AD IYAM AN
AFYO N
AG R I
AKSAR AY
AM ASYA
AN TAKYA
ANTALYA
AR D AHANAR TVIN
AYD IN
BALIKESIR
BAR TIN
BATM AN
BAYBU R TBILEC IK
B ING O L
BITLIS
BO LU
BU R D U R
BUR SA
C AN KIR I
D EN IZLID IYARBAKIR
ELAZIG
ER ZIN C AN
ER ZU R U M
ESKISEH IR
G AZIANTEP
G IR ESU N
G UM USH AN E
H AKKAR I
IG D IR
KAH R AM AN M AR AS
KAR AM AN
KAR S
KASTAM O N U
KAYSER I
K ILIS
K IR IKKALE
KIR KLAR ELI
K IRSEH IR
KO N YA
KU TAH YA
M ALATYA
M AN ISA
M AR D IN
M ER SIN
M U G LA
M U S
N EVSEH IR
N IG D E
O R D UR IZE
SAM SUN
SAN LIU R FA
SIIR T
S IN O P
SIVAS
TEKIR DAG
TO KAT
TR ABZO N
TUN C ELI
U SAKVAN
YO ZG AT
ZO N G U LD AK
SIR N AK
C O R U M
O SM AN IYE
KAR ABU K
28° 32° 36° 40° 44°
40°
36°
44°40°36°32°28°
0 50 100
15
0
200
25
0
km
36°
40°
BLACK SEA
M ED ITER R ANEAN SEA
GR
EE
CE
BU LG AR IA
G EO RG IA
ARM EN IA
IR AN
IRAQ
SYRIA
DMİ
İSTAN BUL
AN KAR A
ISPAR TA
ED İR NE
YALO VA
İZM İT
Ç AN AKKALE
ADAN A
İZM İR
5 7 9 11 13
6 - M O N TH M O D ER ATE D R O U G H T O C C U R R EN C ES (% )
Drought Occurences and Analysis:
AD APAZAR I
AD IYAM AN
AFYO N
AG RI
AKSAR AY
AM ASYA
AN TAKYA
AN TALYA
AR D AH ANAR TVIN
AYD IN
BALIKESIR
BARTIN
BATM AN
BAYBU R TBILEC IK
BIN G O L
BITLIS
BO LU
BU R D U R
BU R SA
C AN KIR I
D EN IZLID IYAR BAKIR
ELAZIG
ER ZIN C AN
ER ZU R U M
ESKISEH IR
G AZIAN TEP
G IRESU N
G U M U SH AN E
H AKKAR I
IG DIR
KAH R AM ANM AR AS
KARAM AN
KARS
KASTAM O N U
KAYSER I
K ILIS
KIR IKKALE
KIR KLAR ELI
K IR SEH IR
KO N YA
KU TAH YA
M ALATYA
M ANISA
M ARD IN
M ER SIN
M U G LA
M U S
N EVSEH IR
N IG D E
O R DUR IZE
SAM SU N
SANLIU R FA
SIIRT
S IN O P
SIVAS
TEKIR D AG
TO KAT
TR ABZO N
TU NC ELI
U SAKVAN
YO ZG AT
ZO N G U LD AK
SIR N AK
C O R U M
O SM AN IYE
KAR ABU K
28° 32° 36° 40° 44°
40°
36°
44°40°36°32°28°
0 50 10
0
15
0
20
0
25
0
km
36°
40°
BLAC K SEA
M ED ITER RAN EAN SEA
GR
EE
CE
BULG ARIA
G EO RG IA
ARM ENIA
IRAN
IRAQ
SYRIA
DMİ
İSTANBUL
ANKARA
ISPAR TA
ED İR N E
YALO VA
İZM İT
Ç AN AKKALE
AD AN A
İZM İR
2 3 4 5 6 7
3 - M O NTH SEVERE DRO UG HT O CCURR ENCES (% )
AD APAZAR I
AD IYAM AN
AFYO N
AG RI
AKSAR AY
AM ASYA
AN TAKYA
AN TALYA
AR D AH ANAR TVIN
AYD IN
BALIKESIR
BARTIN
BATM AN
BAYBU R TBILEC IK
BIN G O L
BITLIS
BO LU
BU R D U R
BU R SA
C AN KIR I
D EN IZLID IYAR BAKIR
ELAZIG
ER ZIN C AN
ER ZU R U M
ESKISEH IR
G AZIAN TEP
G IRESU N
G U M U SH AN E
H AKKAR I
IG DIR
KAH R AM ANM AR AS
KARAM AN
KARS
KASTAM O N U
KAYSER I
K ILIS
KIR IKKALE
KIR KLAR ELI
K IR SEH IR
KO N YA
KU TAH YA
M ALATYA
M ANISA
M ARD IN
M ER SIN
M U G LA
M U S
N EVSEH IR
N IG D E
O R DUR IZE
SAM SU N
SANLIU R FA
SIIRT
S IN O P
SIVAS
TEKIR D AG
TO KAT
TR ABZO N
TU NC ELI
U SAKVAN
YO ZG AT
ZO N G U LD AK
SIR N AK
C O R U M
O SM AN IYE
KAR ABU K
28° 32° 36° 40° 44°
40°
36°
44°40°36°32°28°
0 50 10
0
15
0
20
0
25
0
km
36°
40°
BLAC K SEA
M ED ITER RAN EAN SEA
GR
EE
CE
BULG ARIA
G EO RG IA
ARM ENIA
IRAN
IRAQ
SYRIA
DMİ
İSTANBUL
ANKARA
ISPAR TA
ED İR N E
YALO VA
İZM İT
Ç AN AKKALE
AD AN A
İZM İR
2 3 4 5 6 7
6 - M O N TH SEVER E D RO UG H T O C C U RR EN CES (% )
AD APAZAR I
AD IYAM AN
AFYO N
AG RI
AKSAR AY
AM ASYA
AN TAKYA
AN TALYA
AR D AH ANAR TVIN
AYD IN
BALIKESIR
BARTIN
BATM AN
BAYBU R TBILEC IK
BIN G O L
BITLIS
BO LU
BU R D U R
BU R SA
C AN KIR I
D EN IZLID IYAR BAKIR
ELAZIG
ER ZIN C AN
ER ZU R U M
ESKISEH IR
G AZIAN TEP
G IRESU N
G U M U SH AN E
H AKKAR I
IG DIR
KAH R AM ANM AR AS
KARAM AN
KARS
KASTAM O N U
KAYSER I
K ILIS
KIR IKKALE
KIR KLAR ELI
K IR SEH IR
KO N YA
KU TAH YA
M ALATYA
M ANISA
M ARD IN
M ER SIN
M U G LA
M U S
N EVSEH IR
N IG D E
O R DUR IZE
SAM SU N
SANLIU R FA
SIIRT
S IN O P
SIVAS
TEKIR D AG
TO KAT
TR ABZO N
TU NC ELI
U SAKVAN
YO ZG AT
ZO N G U LD AK
SIR N AK
C O R U M
O SM AN IYE
KAR ABU K
28° 32° 36° 40° 44°
40°
36°
44°40°36°32°28°
0 50 10
0
15
0
20
0
25
0
km
36°
40°
BLAC K SEA
M ED ITER RAN EAN SEA
GR
EE
CE
BULG ARIA
G EO RG IA
ARM ENIA
IRAN
IRAQ
SYRIA
DMİ
İSTANBUL
ANKARA
ISPAR TA
ED İR N E
YALO VA
İZM İT
Ç AN AKKALE
AD AN A
İZM İR
1 2 3 4
3 - M O NTH VERY SEVERE DR O UG H T O CCURR ENCES (% )
AD APAZAR I
AD IYAM AN
AFYO N
AG RI
AKSAR AY
AM ASYA
AN TAKYA
AN TALYA
AR D AH ANAR TVIN
AYD IN
BALIKESIR
BARTIN
BATM AN
BAYBU R TBILEC IK
BIN G O L
BITLIS
BO LU
BU R D U R
BU R SA
C AN KIR I
D EN IZLID IYAR BAKIR
ELAZIG
ER ZIN C AN
ER ZU R U M
ESKISEH IR
G AZIAN TEP
G IRESU N
G U M U SH AN E
H AKKAR I
IG DIR
KAH R AM ANM AR AS
KARAM AN
KARS
KASTAM O N U
KAYSER I
K ILIS
KIR IKKALE
KIR KLAR ELI
K IR SEH IR
KO N YA
KU TAH YA
M ALATYA
M ANISA
M ARD IN
M ER SIN
M U G LA
M U S
N EVSEH IR
N IG D E
O R DUR IZE
SAM SU N
SANLIU R FA
SIIRT
S IN O P
SIVAS
TEKIR D AG
TO KAT
TR ABZO N
TU NC ELI
U SAKVAN
YO ZG AT
ZO N G U LD AK
SIR N AK
C O R U M
O SM AN IYE
KAR ABU K
28° 32° 36° 40° 44°
40°
36°
44°40°36°32°28°
0 50 10
0
15
0
20
0
25
0
km
36°
40°
BLAC K SEA
M ED ITER RAN EAN SEA
GR
EE
CE
BULG ARIA
G EO RG IA
ARM ENIA
IRAN
IRAQ
SYRIA
DMİ
İSTANBUL
ANKARA
ISPAR TA
ED İR N E
YALO VA
İZM İT
Ç AN AKKALE
AD AN A
İZM İR
1 2 3 4
6 - M O N TH VERY SEVERE D RO UG HT O CCUR RENCES (% )
Drought Occurences and Analysis:
Very Severe Drought Occurences (%) at 3 Month Time Scales:
Very Severe Drought Occurences (%) at 6 Month Time Scales:
Critical Treshold Rainfall Analysis:
▲ In general, rainfall amounts required for non-drought conditions decrease from the coastal parts toward the interiors with increasing time steps.
AD APAZAR I
AD IYAM AN
AFYO N
AG RI
AKSAR AY
AM ASYA
AN TAKYA
AN TALYA
AR D AH ANAR TVIN
AYD IN
BALIKESIR
BARTIN
BATM AN
BAYBU R TBILEC IK
BIN G O L
BITLIS
BO LU
BU R D U R
BU R SA
C AN KIR I
D EN IZLID IYAR BAKIR
ELAZIG
ER ZIN C AN
ER ZU R U MESKISEH IR
G AZIAN TEP
G IRESU N
G U M U SH AN E
H AKKAR I
IG DIR
KAH R AM ANM AR AS
KARAM AN
KARS
KASTAM O N U
KAYSER I
K ILIS
KIR IKKALE
KIR KLAR ELI
K IR SEH IR
KO N YA
KU TAH YA
M ALATYA
M ANISA
M ARD IN
M ER SIN
M U G LA
M U S
N EVSEH IR
N IG D E
O R DUR IZE
SAM SU N
SANLIU R FA
SIIRT
S IN O P
SIVAS
TEKIR D AG
TO KAT
TR ABZO N
TU NC ELI
U SAKVAN
YO ZG AT
ZO N G U LD AK
SIR N AK
C O R U M
O SM AN IYE
KAR ABU K
28° 32° 36° 40° 44°
40°
36°
44°40°36°32°28°
0 50 10
0
150 20
0
25
0
km
36°
40°
BLACK SEA
M EDITERRANEAN SEA
GR
EE
CE
BULG ARIA
G EORGIA
ARM ENIA
IRAN
IRAQ
SYRIA
DMİ
İSTANBUL
ANKARA
ISPAR TA
ED İR N E
YALO VA
İZM İT
Ç AN AKKALE
AD AN A
İZM İR
80 130 180 230 280 330 380
3 - M O NTH CRITICAL RAINFALL VALUESDERIVED FRO M THE SPI (m m )M ARCH - APRIL - M AY
AD APAZAR I
AD IYAM AN
AFYO N
AG RI
AKSAR AY
AM ASYA
AN TAKYA
AN TALYA
AR D AH ANAR TVIN
AYD IN
BALIKESIR
BARTIN
BATM AN
BAYBU R TBILEC IK
BIN G O L
BITLIS
BO LU
BU R D U R
BU R SA
C AN KIR I
D EN IZLID IYAR BAKIR
ELAZIG
ER ZIN C AN
ER ZU R U MESKISEH IR
G AZIAN TEP
G IRESU N
G U M U SH AN E
H AKKAR I
IG DIR
KAH R AM ANM AR AS
KARAM AN
KARS
KASTAM O N U
KAYSER I
K ILIS
KIR IKKALE
KIR KLAR ELI
K IR SEH IR
KO N YA
KU TAH YA
M ALATYA
M ANISA
M ARD IN
M ER SIN
M U G LA
M U S
N EVSEH IR
N IG D E
O R DUR IZE
SAM SU N
SANLIU R FA
SIIRT
S IN O P
SIVAS
TEKIR D AG
TO KAT
TR ABZO N
TU NC ELI
U SAKVAN
YO ZG AT
ZO N G U LD AK
SIR N AK
C O R U M
O SM AN IYE
KAR ABU K
28° 32° 36° 40° 44°
40°
36°
44°40°36°32°28°
0 50 10
0
150 20
0
25
0
km
36°
40°
BLACK SEA
M EDITERRANEAN SEA
GR
EE
CE
BULG ARIA
G EORGIA
ARM ENIA
IRAN
IRAQ
SYRIA
DMİ
İSTANBUL
ANKARA
ISPAR TA
ED İR N E
YALO VA
İZM İT
Ç AN AKKALE
AD AN A
İZM İR
0 200 400 600 800 1000
6 - M O NTH CRITICAL RAINFALL VALUESDERIVED FRO M THE SPI (m m )DECEM BER THRO UG H M AY
Critical Treshold Rainfall Values for Severe Drought at 6 Month (March-April-May) Time Scales:
Results:▲ In this study, frequency and severity of meteorological droughts in Turkey have been investigated from a hazard concept and a detailed analysis of geographical variations in the drought vulnerability using the Standardized Precipitation Index (SPI) is presented. Frequency of drought events at different severity categories and critical (threshold) rainfall data are computed at different time scales to identify drought vulnerability. ▲ Information on regional drought vulnerability could aid decision makers in identifying appropriate mitigation actions for future drought events and minimize its impacts. With a map of drought vulnerability, decision makers can conceptually visualize the hazard risk and convey the vulnerability information to other sectors to make sure that they will act timely and effectively to tackle with drought conditions. ▲ While the South-eastern and Eastern parts of the country are more vulnerable to moderate droughts at short time scales, the impact would be expected less at the coastal parts where the drought is only effective at longer durations and occur at moderate drought levels.
▲ At longer time scales hydrologic drought is likely to occur at the coastal parts while the interior parts will suffer from agricultural drought under severe drought conditions.
Thank you ...