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UNDERSTANDING, DETECTING AND COMPARING EXTREME PRECIPITATION CHANGES OVER MEDITERRANEAN USING...
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Transcript of UNDERSTANDING, DETECTING AND COMPARING EXTREME PRECIPITATION CHANGES OVER MEDITERRANEAN USING...
UNDERSTANDING, DETECTING AND COMPARING EXTREME PRECIPITATION
CHANGES OVER MEDITERRANEAN USING CLIMATE MODELS
Dr. Christina Anagnostopoulou
Department of Meteorology-Climatology, School of GeologyDepartment of Meteorology-Climatology, School of Geology
Aristotle University of ThessalonikiAristotle University of Thessaloniki
GreeceGreece
• To assess the ability of RCMs datasets to simulate extreme daily
precipitation
• To produce estimates of predicted changes in return levels by
future time periods (2031-2050 and 2081-2100)
• Detection of extreme precipitation assuming that model
predictions are accurate
AimAim
• Data and methods
• Results for selected grid points
• Spatial distribution of the extreme precipitation indices
• Differences of the extreme precipitation indices between
future and reference time period
OutlineOutline
KNMI
DataData
C4IRCMs data for Mediterranean region
Window: 10oW – 35oE
31oN - 45oN
-10 -5 0 5 10 15 20 25 30 35
35
40
45
-10 -5 0 5 10 15 20 25 30 35
35
40
45
-10 -5 0 5 10 15 20 25 30 35
35
40
45
-10 -5 0 5 10 15 20 25 30 35
35
40
45
-10 -5 0 5 10 15 20 25 30 35
35
40
45
-10 -5 0 5 10 15 20 25 30 35
35
40
45
0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300
0 10 20 30 40 50 60 70 80 90 100
0 20 40 60 80 100 120 140 160 180 200
0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300
0 10 20 30 40 50 60 70 80 90 100
0 20 40 60 80 100 120 140 160 180 200
Description of RCMs used
• KNMI-RACMO2: Royal Netherlands Meteorological Institute (KNMI, Lenderink et al., 2003; van den Hurk et al., 2006)
• ‘Parent’ ECHAM5
• Time period 1950-2100
• SRES A1B
• Physical parameterizations of ΕCMWF (European Centre for Medium – Range Weather Forecasts) used also for ERA-40 (http://www.ecmwf.int/research/ifsdocs).
• Spatial Resolution 25x25km.
Description of RCMs used
• C4IRCA3 : Community Climate Change Consortium for Ireland (C4I).
• ‘Parent’ ECHAM5
• Time period 1950-2050
• SRES A2
• RCA3 the third version of the Rossby Centre Atmospheric model (Kjellström et al., 2005)
• Spatial Resolution 25x25km.
Methodology
Geveralized Extreme Value DistributionGeveralized Extreme Value Distribution
ξ1
σμz
ξ1expzG μ: location parameter
σ: scale parameter
ξ: shape parameter
Return level
p
ξp
p
logyσμ
y1ξ
σμ
z
ˆˆ
ˆ
ˆ ˆ
for ξ ≠ 0
for ξ = 0
)1log( py p
Estimation for GEV distribution
1
11
11log)11(log),,(
m
im
i zzm
01
iz
),,(,....;,,,...\,, 11 mm Lf
1. Maximum Likelihood Estimation-MLE1. Maximum Likelihood Estimation-MLE
2. Bayesian Method2. Bayesian Method
Methodology
Reference period:1951-2000
• 20year period: 2031-2050
• 20year period: 2081-2100
Indices
• Pm: median Pm(t)=X0.5(t)
• P20 : 20-year return value P20(t)=X0.95(t)
• P100: 100-year return value P100(t)=X0.99(t)
-10 -5 0 5 10 15 20 25 30 35
35
40
45
-10 -5 0 5 10 15 20 25 30 35
35
40
45
-10 -5 0 5 10 15 20 25 30 35
35
40
45
-10 -5 0 5 10 15 20 25 30 35
35
40
45
-10 -5 0 5 10 15 20 25 30 35
35
40
45
-10 -5 0 5 10 15 20 25 30 35
35
40
45
0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300
0 10 20 30 40 50 60 70 80 90 100
0 20 40 60 80 100 120 140 160 180 200
0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300
0 10 20 30 40 50 60 70 80 90 100
0 20 40 60 80 100 120 140 160 180 200
Western Western MediterraneanMediterranean
Central Central MediterraneanMediterranean
Eastern Eastern MediterraneanMediterranean
0 50 100 150 200
0.0
00
.01
0.0
20
.03
0.0
40
.05
N = 50 Bandwidth = 4.455
De
nsi
ty
0 50 100 150 200
0.0
00
.01
0.0
20
.03
0.0
40
.05
0 10 20 30 40 50
05
01
00
15
02
00
0 10 20 30 40 50
05
01
00
15
02
00
year
pre
cip
itatio
n
0 10 20 30 40 50
05
01
00
15
02
00
0 10 20 30 40 50
05
01
00
15
02
00
year
pre
cip
itatio
n
0 50 100 150 200
0.0
00
.01
0.0
20
.03
0.0
40
.05
N = 50 Bandwidth = 7.187
De
nsi
ty
0 50 100 150 200
0.0
00
.01
0.0
20
.03
0.0
40
.05
0 10 20 30 40 50
05
01
00
15
02
00
0 10 20 30 40 50
05
01
00
15
02
00
year
pre
cip
itatio
n
0 50 100 150 200
0.0
00
.01
0.0
20
.03
0.0
40
.05
N = 50 Bandwidth = 4.438
De
nsi
ty
0 50 100 150 200
0.0
00
.01
0.0
20
.03
0.0
40
.05
Maximum Likelihood Estimation-MLE Maximum Likelihood Estimation-MLE
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
Probability Plot
Empirical
Mod
el
30 40 50 60
3040
5060
70
Quantile Plot
Model
Em
piric
al
1e-01 1e+00 1e+01 1e+02 1e+03
2030
4050
6070
Return Period
Ret
urn
Leve
l
Return Level Plot Density Plot
z
f(z)
20 30 40 50 60 70 80
0.00
00.
010
0.02
00.
030
KNMI C4I0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
Probability Plot
Empirical
Mod
el
20 30 40 50 60 70 80
3040
5060
7080
90
Quantile Plot
Model
Em
piric
al
1e-01 1e+00 1e+01 1e+02 1e+03
2040
6080
100
120
140
Return Period
Ret
urn
Leve
l
Return Level Plot Density Plot
z
f(z)
20 40 60 80 100
0.00
0.01
0.02
0.03
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
Probability Plot
Empirical
Mod
el
20 40 60 80 100
2040
6080
100
120
Quantile Plot
Model
Em
piric
al
1e-01 1e+00 1e+01 1e+02 1e+03
5010
015
020
0
Return Period
Ret
urn
Leve
l
Return Level Plot Density Plot
z
f(z)
0 20 40 60 80 100 120 140
0.00
00.
010
0.02
00.
030
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
Probability Plot
Empirical
Mod
el
10 20 30 40 50 60 70 80
2040
6080
Quantile Plot
Model
Em
piric
al
1e-01 1e+00 1e+01 1e+02 1e+03
5010
015
0
Return Period
Ret
urn
Leve
l
Return Level Plot Density Plot
z
f(z)
0 20 40 60 80
0.00
0.01
0.02
0.03
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
Probability Plot
Empirical
Mod
el
10 20 30 40 50 60 70
2040
6080
Quantile Plot
Model
Em
piric
al
1e-01 1e+00 1e+01 1e+02 1e+03
2040
6080
100
120
Return Period
Ret
urn
Leve
l
Return Level Plot Density Plot
z
f(z)
20 40 60 80
0.00
0.01
0.02
0.03
0.0 0.2 0.4 0.6 0.8 1.0
0.2
0.4
0.6
0.8
1.0
Probability Plot
Empirical
Mod
el
10 20 30 40 50 60 70
1020
3040
5060
Quantile Plot
Model
Em
piric
al
1e-01 1e+00 1e+01 1e+02 1e+03
2040
6080
120
Return Period
Ret
urn
Leve
l
Return Level Plot Density Plot
z
f(z)
10 20 30 40 50 60 70
0.00
0.01
0.02
0.03
0.04
0.05
Eastern
Mediterranean
Central
Mediterranean
Western
Mediterranean
Bayesian Method Bayesian Method
Eastern
Mediterranean
Central
Mediterranean
Western
Mediterranean
0 20 40 60 80 100
0.0
0.1
0.2
0.3
0.4
0.5
location parameter
De
nsi
ty
0 20 40 60 80 100
0.0
0.1
0.2
0.3
0.4
0.5
location parameter
De
nsi
ty
-20 0 20 40 60
0.0
0.1
0.2
0.3
0.4
0.5
scale parametre
De
nsi
ty
-20 0 20 40 60
0.0
0.1
0.2
0.3
0.4
0.5
scale parametre
De
nsi
ty
-4 -2 0 2 4
01
23
45
6
shape parameter
De
nsi
ty
-4 -2 0 2 4
01
23
45
6
shape parameter
De
nsi
ty
1 5 10 50 100 500 5000
05
01
00
15
02
00
return period
retu
rn le
vel
1 5 10 50 100 500 5000
05
01
00
15
02
00
return period
retu
rn le
vel
0 20 40 60 80 100
0.0
0.1
0.2
0.3
0.4
0.5
location parameter
De
nsi
ty
0 20 40 60 80 100
0.0
0.1
0.2
0.3
0.4
0.5
location parameter
De
nsi
ty
-20 0 20 40 60
0.0
0.1
0.2
0.3
0.4
0.5
scale parametre
De
nsi
ty
-20 0 20 40 60
0.0
0.1
0.2
0.3
0.4
0.5
scale parametre
De
nsi
ty
-4 -2 0 2 4
01
23
45
6
shape parameter
De
nsi
ty
-4 -2 0 2 4
01
23
45
6
shape parameter
De
nsi
ty
1 5 10 50 100 500 5000
05
01
00
15
02
00
return period
retu
rn le
vel
1 5 10 50 100 500 5000
05
01
00
15
02
00
return period
retu
rn le
vel
0 20 40 60 80 100
0.0
0.1
0.2
0.3
0.4
0.5
location parameter
De
nsi
ty
0 20 40 60 80 100
0.0
0.1
0.2
0.3
0.4
0.5
location parameter
De
nsi
ty
-20 0 20 40 60
0.0
0.1
0.2
0.3
0.4
0.5
scale parametre
De
nsi
ty
-20 0 20 40 60
0.0
0.1
0.2
0.3
0.4
0.5
scale parametre
De
nsi
ty
-4 -2 0 2 4
01
23
45
6
shape parameter
De
nsi
ty
-4 -2 0 2 4
01
23
45
6
shape parameter
De
nsi
ty
1 5 10 50 100 500 5000
05
01
00
15
02
00
return period
retu
rn le
vel
1 5 10 50 100 500 5000
05
01
00
15
02
00
return period
retu
rn le
vel
location scale shape Return level
Spatial distribution of maximum annual precipitation Spatial distribution of maximum annual precipitation
-10 -5 0 5 10 15 20 25 30 35
35
40
45
-10 -5 0 5 10 15 20 25 30 35
35
40
45
-10 -5 0 5 10 15 20 25 30 35
35
40
45
-10 -5 0 5 10 15 20 25 30 35
35
40
45
-10 -5 0 5 10 15 20 25 30 35
35
40
45
-10 -5 0 5 10 15 20 25 30 35
35
40
45
0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300
0 10 20 30 40 50 60 70 80 90 100
0 20 40 60 80 100 120 140 160 180 200
0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300
0 10 20 30 40 50 60 70 80 90 100
0 20 40 60 80 100 120 140 160 180 200
Max
Min
Mean
Spatial distribution of the extreme precipitation indicesSpatial distribution of the extreme precipitation indices
KNMI-MLEKNMI-MLE
- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5
P m ( m m )
3 5
4 0
4 5
- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5
S c a l e ( σ )
3 5
4 0
4 5
- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5
l o c a t i o n ( μ )
3 5
4 0
4 5
- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5
P 2 0 ( m m )
3 5
4 0
4 5
0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 00 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0
0 2 4 6 8 1 0 1 2 1 4 1 6 1 8 2 0 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0
Spatial distribution of the extreme precipitation indicesSpatial distribution of the extreme precipitation indices
KNMI - MLEKNMI - MLE
- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5
P 1 0 0 ( m m )
3 5
4 0
4 5
0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0
Spatial distribution of the extreme precipitation indicesSpatial distribution of the extreme precipitation indices
C4I - MLEC4I - MLE
- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5
P m ( m m )
3 5
4 0
4 5
- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5
S c a l e ( σ )
3 5
4 0
4 5
- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5
l o c a t i o n ( μ )
3 5
4 0
4 5
- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5
P 2 0 ( m m )
3 5
4 0
4 5
0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0
0 2 4 6 8 1 0 1 2 1 4 1 6 1 8 2 0 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0
Spatial distribution of the extreme precipitation indicesSpatial distribution of the extreme precipitation indices
C4I - MLEC4I - MLE
- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5
P 1 0 0 ( m m )
3 5
4 0
4 5
0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0
Spatial distribution of the extreme precipitation indicesSpatial distribution of the extreme precipitation indices
KNMI-BayesKNMI-Bayes
- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5
P m
3 5
4 0
4 5
- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5
l o c a t i o n ( μ )
3 5
4 0
4 5
- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5
s c a l e ( σ )
3 5
4 0
4 5
- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5
P 2 0
3 5
4 0
4 5
0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0
0 2 4 6 8 1 0 1 2 1 4 1 6 1 8 2 0 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0
Spatial distribution of the extreme precipitation indicesSpatial distribution of the extreme precipitation indices
KNMI - BayesKNMI - Bayes
- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5
P 1 0 0
3 5
4 0
4 5
0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0
Spatial distribution of the extreme precipitation indicesSpatial distribution of the extreme precipitation indices
C4I - BayesC4I - Bayes
- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5
P m ( m m )
3 5
4 0
4 5
- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5
s c a l e ( σ )
3 5
4 0
4 5
- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5
l o c a t i o n ( μ )
3 5
4 0
4 5
- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5
P 2 0
3 5
4 0
4 5
0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 00 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0
0 2 4 6 8 1 0 1 2 1 4 1 6 1 8 2 0 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0
Spatial distribution of the extreme precipitation indicesSpatial distribution of the extreme precipitation indices
C4I - BayesC4I - Bayes
- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5
P 1 0 0
3 5
4 0
4 5
0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0
Differences of the extreme precipitation indicesDifferences of the extreme precipitation indicesbetween the two time period (2031-2050 & 2081-2100) between the two time period (2031-2050 & 2081-2100)
and the reference period (1951-2000)and the reference period (1951-2000) KNMI-MLE KNMI-MLE
-10 -5 0 5 10 15 20 25 30 35
ΔPm 2050-2000
35
40
45
-10 -5 0 5 10 15 20 25 30 35
ΔP20 2050-2000
35
40
45
-10 -5 0 5 10 15 20 25 30 35
ΔPm 2100-2000
35
40
45
-10 -5 0 5 10 15 20 25 30 35
ΔP20 2100-2000
35
40
45
-20 -15 -10 -5 0 5 10 15 20 -40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40
-40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40-20 -15 -10 -5 0 5 10 15 20
-10 -5 0 5 10 15 20 25 30 35
ΔPm 2050-2000
35
40
45
-10 -5 0 5 10 15 20 25 30 35
ΔP20 2050-2000
35
40
45
-10 -5 0 5 10 15 20 25 30 35
ΔPm 2100-2000
35
40
45
-10 -5 0 5 10 15 20 25 30 35
ΔP20 2100-2000
35
40
45
-20 -15 -10 -5 0 5 10 15 20 -40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40
-40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40-20 -15 -10 -5 0 5 10 15 20
ΔPm
-10 -5 0 5 10 15 20 25 30 35
35
40
45
-10 -5 0 5 10 15 20 25 30 35
ΔP20
35
40
45
-10 -5 0 5 10 15 20 25 30 35
ΔP100
35
40
45
-200 -160 -120 -80 -40 0 40 80 120 160 200 -100 -80 -60 -40 -20 0 20 40 60 80 100
-200 -160 -120 -80 -40 0 40 80 120 160 200
ΔPm
-10 -5 0 5 10 15 20 25 30 35
35
40
45
-10 -5 0 5 10 15 20 25 30 35
ΔP20
35
40
45
-10 -5 0 5 10 15 20 25 30 35
ΔP100
35
40
45
-200 -160 -120 -80 -40 0 40 80 120 160 200 -100 -80 -60 -40 -20 0 20 40 60 80 100
-200 -160 -120 -80 -40 0 40 80 120 160 200
-10 -5 0 5 10 15 20 25 30 35
ΔPm 2050-2000
35
40
45
-10 -5 0 5 10 15 20 25 30 35
ΔP20 2050-2000
35
40
45
-10 -5 0 5 10 15 20 25 30 35
ΔPm 2100-2000
35
40
45
-10 -5 0 5 10 15 20 25 30 35
ΔP20 2100-2000
35
40
45
-20 -15 -10 -5 0 5 10 15 20 -40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40
-40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40-20 -15 -10 -5 0 5 10 15 20
-10 -5 0 5 10 15 20 25 30 35
ΔPm 2050-2000
35
40
45
-10 -5 0 5 10 15 20 25 30 35
ΔP20 2050-2000
35
40
45
-10 -5 0 5 10 15 20 25 30 35
ΔPm 2100-2000
35
40
45
-10 -5 0 5 10 15 20 25 30 35
ΔP20 2100-2000
35
40
45
-20 -15 -10 -5 0 5 10 15 20 -40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40
-40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40-20 -15 -10 -5 0 5 10 15 20
Differences of the extreme precipitation indicesDifferences of the extreme precipitation indicesbetween the time period (2031-2050) and the reference period between the time period (2031-2050) and the reference period
C4I-MLEC4I-MLEΔPm
-10 -5 0 5 10 15 20 25 30 35
35
40
45
-10 -5 0 5 10 15 20 25 30 35
ΔP20
35
40
45
-10 -5 0 5 10 15 20 25 30 35
ΔP100
35
40
45
-200 -160 -120 -80 -40 0 40 80 120 160 200 -100 -80 -60 -40 -20 0 20 40 60 80 100
-200 -160 -120 -80 -40 0 40 80 120 160 200
Differences of the extreme precipitation indicesDifferences of the extreme precipitation indicesbetween the two time period (2031-2050 & 2081-2100) between the two time period (2031-2050 & 2081-2100)
and the reference period (1951-2000)and the reference period (1951-2000)KNMI-BayesKNMI-Bayes
-10 -5 0 5 10 15 20 25 30 35
ΔPm 2050-2000
35
40
45
-10 -5 0 5 10 15 20 25 30 35
ΔP20 2050-2000
35
40
45
-10 -5 0 5 10 15 20 25 30 35
ΔPm 2100-2000
35
40
45
-10 -5 0 5 10 15 20 25 30 35
ΔP20 2050-2000
35
40
45
-20 -15 -10 -5 0 5 10 15 20 -200 -160 -120 -80 -40 0 40 80 120 160 200
-20 -15 -10 -5 0 5 10 15 20 -200 -160 -120 -80 -40 0 40 80 120 160 200
-10 -5 0 5 10 15 20 25 30 35
ΔPm 2050-2000
35
40
45
-10 -5 0 5 10 15 20 25 30 35
ΔP20 2050-2000
35
40
45
-10 -5 0 5 10 15 20 25 30 35
ΔPm 2100-2000
35
40
45
-10 -5 0 5 10 15 20 25 30 35
ΔP20 2100-2000
35
40
45
-20 -15 -10 -5 0 5 10 15 20 -40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40
-40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40-20 -15 -10 -5 0 5 10 15 20
-10 -5 0 5 10 15 20 25 30 35
ΔPm 2050-2000
35
40
45
-10 -5 0 5 10 15 20 25 30 35
ΔP20 2050-2000
35
40
45
-10 -5 0 5 10 15 20 25 30 35
ΔPm 2100-2000
35
40
45
-10 -5 0 5 10 15 20 25 30 35
ΔP20 2050-2000
35
40
45
-20 -15 -10 -5 0 5 10 15 20 -200 -160 -120 -80 -40 0 40 80 120 160 200
-20 -15 -10 -5 0 5 10 15 20 -200 -160 -120 -80 -40 0 40 80 120 160 200
Differences of the extreme precipitation indicesDifferences of the extreme precipitation indicesbetween the time period (2031-2050) and the reference period between the time period (2031-2050) and the reference period
C4I-BayesC4I-Bayes
- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5
Δ P m
3 5
4 0
4 5
- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5
Ä P 2 0
3 5
4 0
4 5
- 1 0 - 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5
Ä P 1 0 0
3 5
4 0
4 5
- 2 0 0 - 1 6 0 - 1 2 0 - 8 0 - 4 0 0 4 0 8 0 1 2 0 1 6 0 2 0 0 - 1 0 0 - 8 0 - 6 0 - 4 0 - 2 0 0 2 0 4 0 6 0 8 0 1 0 0
- 2 0 0 - 1 6 0 - 1 2 0 - 8 0 - 4 0 0 4 0 8 0 1 2 0 1 6 0 2 0 0
Concluding remarks
• The two RCMs datasets simulate reasonably well the extreme annual daily precipitation
• Pm index presents no change or a slight decrease for the future time period, in Mediterranean region
• P20, an index that locates in the tail of the GEV distribution, present increase especially in central Mediterranean
• The two estimators (MLE and Bayesian) present similar results for the reference period but different for the future time-period. The Bayesian method present a practical advantage.