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Extreme Precipitation in a Changing Climate:
Ankara Case Study
Middle East Technical University
Graduate School of Natural and Applied Sciences
Earth System Science (ESS) Interdisciplinary Program
Sertaç Oruç1, İsmail Yücel 1, 2 and Ayşen Yılmaz1, 3
1 Earth System Science (ESS) Interdisciplinary Program, Middle East Technical University 2 Dept. of Civil Eng., Water Resources Division, Middle East Technical University 3 Institute of Marine Sciences, Middle East Technical University
2
Outline
Motivation
Objectives
Methodology
Observed Precipitation Data Analyses
Projected Precipitation Data Analyses
Summary and Conclusions
3
Motivation: Climate Change Global Precipitation Change (For the last 2 decades and Projected Periods)
Low (RCP 2.6) ensemble average (dark line) and
spread of ensemble members (shaded area). Values
are for the model grid cell containing: 39.912°N
32.84°E
High (RCP 8.5) ensemble average (dark line) and
spread of ensemble members (shaded area). Values
are for the model grid cell containing: 39.912°N
32.84°E.
https://gisclimatechange.ucar.edu/inspector RCP : Representative Concentration Pathways
4
Figure 1.1. Annual count of extreme events in Turkey in the period of 1940-2017 Figure 1.2. Distribution of extreme events and their types in
Turkey in 2017
Motivation: Climate Change and Extreme Events
Annual count of extreme events in Turkey shows an increasing trend in 1940-2017 period (Climate Assessment 2017
Report, February 2018 – State Meteorological Service).
During 2017 most hazardous extreme events were; heavy rain/floods (31%), wind storm (36%), hail (16%), heavy
snow (7%), and lightning (4%)
5
Problem Statement
• To analyze the rainfall extreme value frequencies for stationary and nonstationary conditions in Ankara region,
• To produce Return Levels in stationary and non-stationary conditions with observed data and future projections,.
• To figure out the superiority of nonstationary and stationary models to each other,
• Climate change in Turkey has been evaluated in many different studies with its different aspects. Majority of analysis
performed and the future estimation works were focused on temperature and precipitation changes which are the most
important climate parameters causing the extreme events.
• In the last decades, heavy rainfall and flash flooding caused various damages in Turkey; for example settlements were
damaged, road transportation and vehicles are disrupted, and life was negatively affected in Ankara
Objectives
The methodology of precipitation analysis in this study consists of;
(1) Trend analysis is carried out for observed (1950-2015) and projected data (2015-2098)
(2) Projected data is disaggregated into finer scales (5 min) and then it is aggregated to next analysis time scales (10, 15,
and 30 min, …)
(3) Stationary GEV (St) models are developed, return levels are derived for desired return periods considering single and
multi-time periods for observed and single period for projected data
(4) Non-stationary GEV (NSt) models are developed, return levels are derived for desired return periods for observed and
projected data
(5) Stationary and Non-stationary model results were compared
6
Methodology and Data
• Observed Data for Ankara - 1950-2015 (State Meteorological Services)
• Projected Data; Three global climate models (GCM) are used; namely HadGEM2-ES, MPI-ESM-MR and GFDL-
ESM2M. These models are operated with the RCP 4.5 and RCP 8.5 emission scenarios - 2015-2098 (State
Meteorological Services)
7
Rainfall Data
Observed
Trend Analysis
GEV Models
Stationary
Return Levels for Desired Storm
Durations and Periods
Nonstationary
Mean/Median of Return Levels for Desired Storm Duration
Define Covariates
Obatining Blocks
Projected
Disaggregation
Aggregation Obatining Blocks
GEV Models
Stationary
Return Levels for Desired Storm
Durations and Periods
Nonstationary
Mean/Median of Return Levels for Desired Storm Duration
Define Covariates
Figure 1.3. Rainfall Data Analyses Framework
Methodology
Trends & Change Point
8
Observed Data
Figure 1.4. Sub-Hourly Time Series Trend Figure 1.5. Hourly Time Series Trend
Figure 1.6. Average annual maximum rainfall intensities (mm) for sub-hourly and hourly storm durations
0
5
10
15
20
25
30
35
40
45
501
95
0
195
2
195
4
195
6
195
8
196
0
196
2
196
4
196
6
196
8
197
0
197
2
197
4
197
6
197
8
198
0
198
2
198
4
198
6
198
8
199
0
199
2
199
4
199
6
199
8
200
0
200
2
200
4
200
6
200
8
201
0
201
2
201
4
Pre
cip
ita
tio
n H
eig
ht
(mm
)
Years
5 min 10 min 15 min 30 min
0
10
20
30
40
50
60
70
80
90
195
0
195
2
195
4
195
6
195
8
196
0
196
2
196
4
196
6
196
8
197
0
197
2
197
4
197
6
197
8
198
0
198
2
198
4
198
6
198
8
199
0
199
2
199
4
199
6
199
8
200
0
200
2
200
4
200
6
200
8
201
0
201
2
201
4
Pre
cip
ita
tio
n H
eig
ht
(mm
)
Years
1 hour 2 hours 3 hours 6 hours
0
10
20
30
40
50
60
70
80
90
5 min 10 min 15 min 30 min 1 hour 2 hours 3 hours 6 hours
Pre
cip
ita
tio
n H
eig
ht
mm
Storm Duration
Average 1950-1975
Maximum 1950-1975
Average 1976-2015
Maximum 1976-2015
Average 1950-2015
Maximum 1950-2015
9
Model Location Scale Shape
NStGEV1 𝜇t =𝛽0 +𝛽1t 𝜎 (constant) 𝜉 (constant)
NStGEV2 𝜇t =𝛽0 +𝛽1t 𝜎t =𝛽0 +𝛽1t 𝜉 (constant)
NStGEV3 𝜇 (constant) 𝜎t =𝛽0 +𝛽1t 𝜉 (constant)
NStGEV4 𝜇t =𝛽0 +𝛽1temperature 𝜎 (constant) 𝜉 (constant)
NStGEV5 𝜇t =𝛽0 +𝛽1t 𝜎t =𝛽0 +𝛽1exp(temperature) 𝜉 (constant)
NStGEV6 𝜇t =𝛽0 +𝛽1exp(temperature) 𝜎t =𝛽0 +𝛽1exp(temperature) 𝜉 (constant)
NStGEV7 𝜇t =𝛽0 +𝛽1exp(temperature) 𝜎t = (constant) 𝜉 (constant)
NStGEV8 𝜇 (constant) 𝜎t =𝛽0 +𝛽1temperature 𝜉 (constant)
Table 1.1. Non-stationary models with time and covariate (temperature)
dependent location and scale parameters
Stationary Models (St)
5 Minutes 10
Minutes 15
Minutes 30
Minutes 1 Hour 2 Hours 3 Hours 6 Hours
NonStationary Models (NSt)
5 Minutes 10
Minutes 15
Minutes 30
Minutes 1 Hour 2 Hours 3 Hours 6 Hours
Figure 1.7. Storm Durations Used for Stationary Models
2-year 5-year 10-year 25-year 50-year 100-year 200-year
Mean Value Change
FiveMin -4% -4% -5% -9% -11% -15% -18%
TenMin -14% -13% -12% -9% -7% -5% -3%
FifteenMin -1% -4% -6% -9% -12% -14% -17%
ThirtyMin 0% -3% -6% -10% -13% -16% -19%
OneHour -7% -5% -3% 0% 3% 6% 10%
TwoHours 0% -3% -4% -5% -6% -7% -7%
ThreeHours 0% -3% -5% -8% -11% -13% -16%
SixHours 1% -1% -2% -3% -4% -5% -5%
Median Value Change
FiveMin -3% -2% -4% -7% -9% -12% -15%
TenMin -13% -12% -10% -7% -4% -2% 0%
FifteenMin -1% -2% -4% -7% -9% -12% -14%
ThirtyMin 1% -2% -5% -8% -10% -13% -16%
OneHour -8% -5% -2% 2% 5% 8% 12%
TwoHours -1% -2% -3% -4% -4% -5% -5%
ThreeHours -1% -2% -4% -7% -9% -11% -14%
SixHours 0% -1% -2% -2% -3% -3% -4%
Table 1.2. Nonstationary GEV Best Fit Model Return Levels (mm) - Mean and
Median Value Change with Respect to Stationary GEV Model
Observed Data
10
Observed Data
• The shorter the storm duration the larger the differences between the non-stationary and stationary extremes.
• Among the storm durations, only one hour time series exhibit larger values for its nonstationary model return level
values, however this is not valid for shorter return periods such as 5 years or 20 years
• Sub-hourly storm durations indicate larger difference than hourly storm durations and non-stationary estimates are
smaller than their corresponding stationary values
Figure 1.8. Stationary and Best Fit Nonstationary Model Return Level (mm) Comparison - Return Period vs. Return Level
0
5
10
15
20
2 5 10 20 25 50 100 200
Ret
urn
Lev
el m
m
Return Period (Years)
StFiveMin
NStFiveMin
0
5
10
15
20
25
30
2 5 10 20 25 50 100 200
Ret
un
Lev
el m
m
Return Period (Years)
StTenMin
NStTenMin
0
10
20
30
40
2 5 10 20 25 50 100 200
Ret
urn
Lev
el m
m
Return Period (Years)
StFifteenMin
NStFifteenMin
0
10
20
30
40
50
60
70
2 5 10 20 25 50 100 200
Ret
urn
Lev
el m
m
Return Period (Years)
StThirtyMin
NStThirtyMin
0
20
40
60
80
100
2 5 10 20 25 50 100 200
Ret
urn
Lev
el m
m
Return Period (Years)
StOneHour
NStOneHour
0
20
40
60
80
100
2 5 10 20 25 50 100 200
Ret
urn
Lev
el m
m
Return Period (Years)
StTwoHours
NStTwoHours
0
20
40
60
80
100
2 5 10 20 25 50 100 200
Ret
urn
Lev
el m
m
Return Period (Years)
StThreeHours
NStThreeHours
0
20
40
60
80
100
2 5 10 20 25 50 100 200
Ret
urn
Lev
el m
m
Return Period (Years)
StSixHours
NStSixHours
11
(Non) Stationary ((N)St)
10 Minutes
MPI-ESM-MR (MPI)
RCP 4.5
RCP 8.5
HADGEM2-ES (HG)
RCP 4.5
RCP 8.5
GFDL-ESM2M (GFDL)
RCP 4.5
RCP 8.5
15 Minutes
MPI
RCP 4.5
RCP 8.5
HG
RCP 4.5
RCP 8.5
GFDL
RCP 4.5
RCP 8.5
1 Hour
MPI
RCP 4.5
RCP 8.5
HG
RCP 4.5
RCP 8.5
GFDL
RCP 4.5
RCP 8.5
6 Hours
MPI
RCP 4.5
RCP 8.5
HG
RCP 4.5
RCP 8.5
GFDL
RCP 4.5
RCP 8.5
Figure 1.9. Projected Storm Durations Used for Stationary Models for 2015-2098 period
Projected Data
12
Projected Data: Trends
Figure 1.10. Projected 10-15 Minutes (a,b) and 1-6 Hours (c,d) Annual Maximum Time Series for 2015-2098
(a) (b)
(c) (d)
0
2
4
6
8
10
12
14
16
201
5
201
8
202
1
202
4
202
7
203
0
203
3
203
6
203
9
204
2
204
5
204
8
205
1
205
4
205
7
206
0
206
3
206
6
206
9
207
2
207
5
207
8
208
1
208
4
208
7
209
0
209
3
209
6
Hei
gh
t m
m
MPI 4.5 MPI 8.5 GFDL 4.5 GFDL 8.5 HG 4.5 HG 8.5
0
5
10
15
20
25
201
5
201
8
202
1
202
4
202
7
203
0
203
3
203
6
203
9
204
2
204
5
204
8
205
1
205
4
205
7
206
0
206
3
206
6
206
9
207
2
207
5
207
8
208
1
208
4
208
7
209
0
209
3
209
6
Hei
hg
t m
m
MPI 4.5 MPI 8.5 GFDL 4.5 GFDL 8.5 HG10 4.5 HG10 8.5
0
5
10
15
20
25
30
35
40
45
50
201
5
201
8
202
1
202
4
202
7
203
0
203
3
203
6
203
9
204
2
204
5
204
8
205
1
205
4
205
7
206
0
206
3
206
6
206
9
207
2
207
5
207
8
208
1
208
4
208
7
209
0
209
3
209
6
Hei
gh
t m
m
MPI 4.5 MPI 8.5 GFDL 4.5 GFDL 8.5 HG10 4.5 HG10 8.5
0
10
20
30
40
50
60
70
80
201
5
201
8
202
1
202
4
202
7
203
0
203
3
203
6
203
9
204
2
204
5
204
8
205
1
205
4
205
7
206
0
206
3
206
6
206
9
207
2
207
5
207
8
208
1
208
4
208
7
209
0
209
3
209
6
Hei
gh
t m
m
MPI 4.5 MPI 8.5 GFDL 4.5 GFDL 8.5 HG10 4.5 HG10 8.5
13
Return Period
-Years 2 5 10 25 50 100 200 2 5 10 25 50 100 200
Model Mean Value Change Median Value Change
MPI45 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%
MPI85 0% -1% -1% -2% -2% -2% -2% 1% 0% -1% -1% -2% -2% -2%
GFDL45 -1% -1% 0% 0% 1% 1% 2% 1% 1% 1% 1% 2% 2% 3%
GFDL85 1% 0% 0% -1% -1% -2% -2% 2% 2% 2% 2% 2% 2% 1%
HG45 0% -1% 0% 1% 2% 2% 4% 0% -1% 0% 1% 2% 2% 4%
HG85 0% 0% 0% 0% 0% 0% 1% -1% -1% -1% -1% 0% 0% 0%
Mean Value Change Median Value Change
MPI45 0% 0% 0% 0% 1% 2% 3% 0% 0% 0% 1% 1% 2% 3%
MPI85 0% -1% -1% -2% -2% -2% -3% 1% 0% -1% -1% -2% -2% -2%
GFDL45 -1% -1% -1% 0% 0% 0% 1% -1% -1% -1% 0% 0% 0% 1%
GFDL85 0% 0% -1% -1% -1% -1% -1% 0% 0% 0% 0% -1% -1% -1%
HG45 -1% -1% 0% 2% 3% 5% 6% -1% -1% 0% 2% 3% 5% 6%
HG85 0% 0% 0% 0% 0% 0% 1% -2% -1% -1% -1% -1% 0% 0%
Mean Value Change Median Value Change
MPI45 -1% -1% -1% -1% -2% -2% -2% -1% -1% -1% -1% -2% -2% -2%
MPI85 0% -2% -2% -1% -1% 0% 1% 1% -1% -2% -1% 0% 1% 2%
GFDL45 -1% 0% 0% 1% 2% 3% 4% -1% 0% 0% 1% 2% 3% 4%
GFDL85 0% 0% -1% -2% -3% -3% -4% 2% 2% 1% 1% 0% 0% -1%
HG45 0% -1% -1% 0% 0% 0% 1% 0% -1% -1% 0% 0% 0% 1%
HG85 0% -1% -1% -1% -1% -1% -1% -3% -3% -2% -2% -2% -2% -2%
Mean Value Change Median Value Change
MPI45 0% -1% -1% -2% -2% -3% -4% 0% -1% -1% -2% -2% -3% -4%
MPI85 2% 2% 1% 0% 0% -1% -2% 2% 3% 3% 3% 3% 2% 2%
GFDL45 1% 0% -2% -5% -7% -10% -13% 1% -1% -3% -6% -9% -12% -15%
GFDL85 0% -2% -3% -5% -7% -9% -12% 1% 0% -1% -3% -4% -5% -7%
HG45 0% -1% -3% -5% -7% -9% -11% 2% 2% 1% -1% -2% -4% -6%
HG85 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1%
Table 1.3. Nonstationary Mean and Median Value Change with Respect to Stationary Model - Projected Data
Ten
Minutes
Fifteen
Minutes
One
Hour Six Hours
2-year 1% -5% 0% 1%
5-year 0% -7% -1% 0%
10-year -1% -9% -1% -1%
25-year -2% -10% -1% -2%
50-year -3% -11% 0% -4%
100-year -3% -12% 0% -5%
200-year -4% -12% 1% -6%
Table 1.4. Nonstationary Model-Stationary
Comparison for Projected Data - Average
Values
Projected Data
14 Figure 1.11. Stationary Model Results for Projected Time Series
Projected Data
0
5
10
15
20
25
30
Ret
urn
Lev
el m
m
Return Period
StTenMinMPI45
StTenMinMPI85
StTenMinGFDL45
StTenMinGFDL85
StTenMinHG45
StTenMinHG85
Average
Max
0
5
10
15
20
25
30
35
Ret
urn
Lev
el m
m
Return Period
StFifteenMinMPI45
StFifteenMinMPI85
StFifteenMinGFDL45
StFifteenMinGFDL85
StFifteenMinHG45
StFifteenMinHG85
Average
Max
05
101520253035404550
Ret
urn
Lev
el m
m
Return Period
StOneHourMPI45
StOneHourMPI85
StOneHourGFDL45
StOneHourGFDL85
StOneHourHG45
StOneHourHG85
Average
Max
0102030405060708090
100
Ret
urn
Lev
el m
m
Return Period
StSixHoursMPI45
StSixHoursMPI85
StSixHoursGFDL45
StSixHoursGFDL85
StSixHoursHG45
StSixHoursHG85
Average
Max
15
On average nonstationary models produce mostly lower return levels for mid and longer return periods for all durations and
similar results for short (2 and 5 years) return periods except one hour storm duration.
Projected Data
Figure 1.12. 10-15 Minutes and 1-6 Hours Ensemble Model Comparison for Projected Data
0
5
10
15
20
25
30
2-year 5-year 10-year 20-year 25-year 50-year 100-year200-year
Ret
urn
Lev
el m
m
NStTenMinAvrg
NStTenMinMax
StTenMinAvrg
StTenMinMax
0
5
10
15
20
25
30
35
40
Ret
urn
Lev
el m
m
NStFifteenMinAvrg
NStFifteenMinMax
StFifteenMinAvrg
StFifteenMinMax
0
5
10
15
20
25
30
35
40
45
50
Ret
urn
Lev
el m
m
NStOneHourAvrg
NStOneHourMax
StOneHourAvrg
StOneHourMax
0
20
40
60
80
100
120
Ret
urn
Lev
el m
m
NStSixHoursAvrg
NStSixHoursMax
StSixHoursAvrg
StSixHoursMax
16
St
St
St
St
St
St
St
SMS
SMS
SMS
SMS
SMS
SMS
SMS
NStMean
NStMean
NStMean
NStMean
NStMean
NStMean
NStMean
NstMedian
NstMedian
NstMedian
NstMedian
NstMedian
NstMedian
NstMedian
-
5,00
10,00
15,00
20,00
25,00
30,00
2-year 5-year 10-year 25-year 50-year 100-year 200-year
St SMS NStMean NstMedian StMPI45 StMPI85 StGFDL45 StGFDL85
StHG45 StHG85 NStMPI45Mean NStMPI45Median NStMPI85Mean NStMPI85Median NStGFDL45Mean NStGFDL45Median
NStGFDL85Mean NStGFDL85Median NStHG45Mean NStHG45Median NStHG85Mean NStHG85Median StProjectedAvg NStProjectedAvg
Projected Data
Figure 1.15. Ten Minutes Data Model Comparison - Best Fit Nst and St for Observed and Projected Data and SMS (State Meteorological Service) Data
17
Projected Data
Figure 1.16. Fifteen Minutes Data Model Comparison - Best Fit Nst and St for Observed and Projected Data and SMS (State Meteorological Service) Data
St
St
St
St
St
St
St
SMS
SMS
SMS
SMS
SMS
SMS
SMS
NStMean
NStMean
NStMean
NStMean
NStMean
NStMean
NStMean
NstMedian
NstMedian
NstMedian
NstMedian
NstMedian
NstMedian
NstMedian
-
5,00
10,00
15,00
20,00
25,00
30,00
35,00
40,00
2-year 5-year 10-year 25-year 50-year 100-year 200-year
St SMS NStMean NstMedian StMPI45 StMPI85 StGFDL45 StGFDL85
StHG45 StHG85 NStMPI45Mean NStMPI45Median NStMPI85Mean NStMPI85Median NStGFDL45445Mean NStGFDL454Median
NStGFDL854Mean NStGFDL854Median NStHG45Mean NStHG45Median NStHG85Mean NStHG85Median StProjectedAvg NStProjectedAvg
18
St
St
St
St
St
St
St
SMS
SMS
SMS
SMS
SMS
SMS
SMS
NStMean
NStMean
NStMean
NStMean
NStMean
NStMean
NStMean
NstMedian
NstMedian
NstMedian
NstMedian
NstMedian
NstMedian
NstMedian
-
10,00
20,00
30,00
40,00
50,00
60,00
70,00
80,00
90,00
2-year 5-year 10-year 25-year 50-year 100-year 200-year
St SMS NStMean NstMedian StMPI45 StMPI85 StGFDL45 StGFDL85
StHG45 StHG85 NStMPI45Mean NStMPI45Median NStMPI85Mean NStMPI85Median NStGFDL45Mean NStGFDL45Median
NStGFDL85Mean NStGFDL85Median NStHG45Mean NStHG45Median NStHG85Mean NStHG85Median StProjectedAvg NStProjectedAvg
Projected Data
Figure 1.17. One Hour Data Model Comparison - Best Fit Nst and St for Observed and Projected Data and SMS (State Meteorological Service) Data
19
St
St
St
St
St
St
St
SMS
SMS
SMS
SMS
SMS
SMS
SMS
StMPI45
StMPI45
StMPI45
StMPI45
StMPI45
StMPI45
StMPI45
NStMPI45Mean
NStMPI45Mean
NStMPI45Mean
NStMPI45Mean
NStMPI45Mean
NStMPI45Mean
NStMPI45Mean
NStMPI45Median
NStMPI45Median
NStMPI45Median
NStMPI45Median
NStMPI45Median
NStMPI45Median
NStMPI45Median
-
10,00
20,00
30,00
40,00
50,00
60,00
70,00
80,00
90,00
100,00
2-year 5-year 10-year 25-year 50-year 100-year 200-year
St SMS NStMean NstMedian StMPI45 StMPI85 StGFDL45 StGFDL85
StHG45 StHG85 NStMPI45Mean NStMPI45Median NStMPI85Mean NStMPI85Median NStGFDL45Mean NStGFDL45Median
NStGFDL85Mean NStGFDL85Median NStHG45Mean NStHG45Median NStHG85Mean NStHG85Median StProjectedAvg NStProjectedAvg
Projected Data
Figure 1.18. Six Hours Data Model Comparison - Best Fit Nst and St for Observed and Projected Data and SMS (State Meteorological Service) Data
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Summary and Conclusions:
• Stationary GEV models were capable of fitting extreme rainfall data for all durations but the developed non-stationary
GEV models showed advantage over the stationary models
• The differences in design rainfall estimates between two time slice, entire period and nonstationary assumption models
support the need to update the current information, with the most recent data and approaches.
• The differences also reveal the need to conduct analysis using future climate data.
• Nonstationary model results are in general exhibited smaller return level values with respect to stationary model results of
each storm duration for the observed data driven model results.
• On average nonstationary models produce mostly lower return levels for mid and longer return periods for all durations
and similar results for short (2 and 5 years) return periods except one hour storm duration for the projected data.
• Almost all the nonstationary model maximum return level results are significantly higher than stationary model maximum
return level results for all storm durations and return periods for the projected data driven model results.
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