Download + Daily Weather Generation

Application of Multi-site
stochastic daily Climate Generation
to assess the Impact of Climate Change
in the eastern Seaboard of Thailand
Aug 2005-manager.co.th
Werapol Bejranonda and Manfred Koch
Geohydraulics and Engineering Hydrology, University of Kassel
Table of Contents
1.Introduction
Motivation/ Study region/ Objectives/ Scope of work
2.Model development
Methodology/ Model structure
3.Evaluation & Application
Climate schemes/ Application in downscaling
4.Impacts of climate change
Climate of the 21th century/ Impact on water resources
5.Conclusions
2
Introduction
Development
Eval. & App.
Impacts
Conclusions
Motivation
Rainfall / Climate
Water Planning
outdated climate pattern
traditional management
Drought crisis
in Eastern Seaboard
Water storage in reservoir (DK)
traditional rule
Reservoir storage
2005
Jan
Aug 2005-manager.co.th
no storage
Dec
monsoon storms
Consequences
http://www.oknation.net/blog/print.php?id=222747
source: eastwater.com
Industrial
shutdown
Crop loss
Abruption of
Thai economy
(ICIS, 2005)
3
Introduction
Development
Eval. & App.
Impacts
Conclusions
Study area
Eastern Seaboard of Thailand (EST)
Eastern
coastline
Khlong Yai basin
Chonburi
NPL
KY
DK
1560 km2
Rayong
Major industrial zone of Thailand
Thai Gulf
4
Introduction
Development
Eval. & App.
Impacts
Conclusions
Objectives
1. Development of daily weather generation
- Using statistical/stochastical techniques -
2. Application in climate projection
- Integrating with climate downscaling -
3. Investigation of climate pattern in 21st century
- Assessing the impact of climate change Ultimate goal
Pattern of climate change
and effects on water resources
5
Introduction
Development
Eval. & App.
Impacts
Conclusions
Scope of work
Climate models
Parameters
1. Stochastic generation
of daily climate
Historic
monthly & daily
climate
rescaling
monthly  daily climate
2. Climate downscaling
projecting
monthly climate in 21st century
Performance
Tmax, Tmin, PCP
Future monthly
climate
Tmax, Tmin, PCP
Climate sites in EST
● 24 precipitation
● 4 temperature
6
Introduction
Development
Existing
predicting
tools
vs
New. tools
developed
here
Eval. & App.
Impacts
Impact
assessment in
EST
Conclusions
Methodology (1)
Monthly climate
Stochastic
climate
generator
Daily attributes
• Data distribution
• Extreme values
• Spatial pattern
• etc.
Daily Moran’s I
multi-realization
daily climate
30rlz
Extreme daily
rainfall
7
Introduction
Development
Eval. & App.
Impacts
Conclusions
Methodology (2)
1.Today wet or dry ?
two-state Markov chain
2.Rainfall amount
3.Temperature
Exponential distribution
Normal distribution
Rainfall
generation
Tmax & Tmin
generation
Daily Moran’s I of Tmax
Multi-site
generation
Climate pattern
Spatial
Autocorrelation
dataurbanist.com
dataurbanist.com
Moran’s I
(Khalili et al., 2007)
Positive Moran’s I
8
Introduction
Development
Eval. & App.
Impacts
Negative Moran’s I
Conclusions
Model structure
Historic
record
Daily climate
Parameter estimation
• Moran’s I relationship
γk,i=1
γk,i=12
m = 30 points
…
Ik,i=1
...
Ik,i=12
• Extreme value relationship
Textr/Tmean
Tmean
Rmean
• Critical rainfall probability (Pc)
• etc.
Daily weather generation
monthly MLR model
9
Introduction
Development
Monthly
data
Rainfall
Probability
of wet day Monthly
rainfall
Rain. occurrence
generation
Rainfall amount
generation
Tmax & Tmin
30rlz
series
dry
wet
Rain on
wet day
Tmax & Tmin
generation
30rlz
Daily Tmax & Tmin on wet/dry
MLR + weather generation
New tool ! monthly GCMs  daily climate
Eval. & App.
Impacts
Conclusions
Climate schemes
Daily weather
generation
Using local
climate data
calibration
1971
GCM-baseline
1971-1985
1986-2000
verification
2000
1985 1986
Long-term
projection
calibration verification
Using GCM
climate data
calibration
verification
projection
1971-1985
1986-1999
2000-2096
Future scenarios
20c3m
(SRES)
calibration
1971
10
Introduction
verification
1985 1986
Development
projection
1999 2000
Eval. & App.
2096
Impacts
Conclusions
Application in climate projection
Monthly GCMs
Multi-domain & High-Res GCMs
Multi-linear regression
(MLR)
● 2.5° x 2.5° GCMs (5 domains)
ECHO-G, BCCR,
ECHAM5, GISS, PCM
75,000 km2
● 0.5° x 0.5° High-Res. GCM
3,000 km2
CRU/TYN
Daily weather
generation
Climate projection
Projected monthly
climate
Projected daily
climate
2000-2096
30rlz
Scenarios
A2
A1B
B1
11
Introduction
Development
Eval. & App.
Impacts
Conclusions
Evaluation: Daily climate generation
Scatterplots of obs. and sim.
monthly average climate
PCP
Max temperature
Predictor
Wet rate (% wet day)
Rainfall amount (mm/day)
Tmax (°C)
Tmin (°C)
12
Introduction
Validation scheme
calibration 1971-1985
verification 1986-1999
Calibration: 1971-1985
residual error
NS
ME
RMSE
0.36
3.32
0.71
-0.15
0.24
0.99
-0.04
0.07
0.99
-0.01
0.08
0.99
Development
Eval. & App.
Min temperature
Verification: 1986-2000
residual error
NS
ME
RMSE
0.70
2.89
0.80
0.19
0.34
0.99
0.20
0.24
0.95
0.08
0.21
0.99
Impacts
Conclusions
Evaluation: Application in downscaling
Goal  Describing climate behaviour
Multi-linear regression downscaling (MLR)
+
Daily Weather Generation (DWG)
Best in describing climate series
(correlation & distribution)
Cross-correlation
Density distribution
Predicted vs observed Tmax
Predicted vs observed series
b) LARS-WG
a) SDSM
Temperature (°C)
MLR-daily
a)c)
SDSM
Temperature (°C)
d) MLR+DWG
b) LARS-WG
Temperature (°C)
13
Introduction
Development
c) MLR-daily
Eval. & App.
Temperature (°C)
d) MLR+climate generator
Impacts
Conclusions
Impact assessment
Physical properties
MLR + DWG
Land & Soil maps
New tool !
monthly GCMs
Impact
assessment
daily climate
Projected daily climate
30rlz
Precipitation
SWAT model
(Arnold et al, 1998)
Hydrol. consequences
2000
9
30rlz
2
SRES
1600
1400
1
6
amount of water (mm/year)
Tmax & Tmax
20c3m
1800
8
Soil+Surface
PERC
ET.obs.sim
precipitation
PCP
1200
5
7
4
3
10
ET
PCP.obs.sim
PERC.obs.sim
1000
30rlz
800
Evaporation
evapotranspiration
600
400
200
11
12
percolation
Percolation
0
year
14
Introduction
Development
Eval. & App.
Impacts
Conclusions
Climate over 21st century
20th century simulation
1971 – 1999
Temperature
20th
vs
Tmax
21st century projection
2000 – 2096
Prob. of rain occurrence
(% of wet day)
21st
longer droughts
Tmin
% of
wet day
Extreme daily
rainfall
Precipitation
SRES A2
20th
21st
slight increase
15
Introduction
Development
Eval. & App.
more extreme
Impacts
Conclusions
Avg. monthly discharge at z4,z15 and z38 (m3/s)
Impact on water resources
Streamflow
Effects at reservoirs
NPL
reservoir
KY
NPL
Z15
DK
20th
Z4
21st
Aug 2005-manager.co.th
Z38
20th increase  21st decrease
Stream gauge
May 2014-manager.co.th
B1
A2
A1B
21st
Density distribution
of runoff
more low-flow
change of monthly flow-in (cms/year)
Change of inflow in 21st century
NPL
NPL reservoir
Compared to 20th
Wet season
change of pattern
16
Introduction
Development
Eval. & App.
Impacts
SRES A2
Conclusions
Conclusions
Daily weather generation (DWG)
 DWG can be applied for :
• Generating daily weather data from known monthly
• Downscaling monthly GCMs into daily climate series
(in application of monthly downscaling)
 DWG Model performance
• DWG can describe climate fluctuation and distribution
• Better performance than daily GCM downscaling (e.g.
SDSM and LARS-WG)
Impact of climate change
 Climate in 21st century in study region
• Higher temperature / extreme wet spells / longer droughts
• Change in mean and distribution
 Impact on water resources
• Less reservoir inflow / pattern change (distribution / season)
17
Introduction
Development
Eval. & App.
Impacts
Conclusions
Further developments
Hydrological simulation at
ungagged basin
Hydrologic
model
Daily weather
generation
Known monthly
regional climate
Generating daily weather
for short-term climate prediction
Daily weather
generation
18
Introduction
Development
MLR model
Eval. & App.
Teleconnection
• SSTs
• Ocean Indices
Impacts
Conclusions
Questions & Answers
Thanks to
• Water Resources System Research Unit,
Chulalongkorn University, Thailand (WRSRU_CU)
• Royal Irrigation Department, Thailand (RID)
• Thai meteorological department, Thailand (TMD)
References
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19
Arnold JG, Srinivasan R, Muttiah RS, Williams JR (1998) Large area hydrologic modeling and assessment part i: model development. J
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Chantanusornsiri W (2012) 2011 GDP growth sinks to 0.1% on flood crisis. Bounceback of about 6% expected this year. Bangkok Post
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University Press.
ICIS (2005) How severe is drought in Thailand? http://www.icis.com/Articles/2005/07/25/2003310/how-severe-is-drought-inthailand.html
Khalili M, Leconte R, Brissette F (2007) Stochastic Multisite Generation of Daily Precipitation Data Using Spatial Autocorrelation. J.
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for diverse climates. Clim. Res. 10(2):95–107.
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Environmental Modelling & Software 17(2):145–157.
Introduction
Development
Eval. & App.
Impacts
Conclusions