hydrologic impacts of climate change : scale issues and uncertainties p p mujumdar dept. of civil...
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1
Hydrologic Impacts of Climate Change : Scale Issues and Uncertainties
P P Mujumdar
Dept. of Civil Engineering &
Divecha Center for Climate Change, IISc.
221 July 2011
Organisation of the Talk Introduction – Hydrologic Processes
Climate Change Impacts : Scope of Research
Scale Issues & UncertaintiesMeteorologic droughtsRiver basins : water availabilityRiver Water QualityUrban flooding
Summary
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Source: http://hydrogeology.glg.msu.edu/research/active/modeling-and-monitoring-hydrologic-processes-in-large-watersheds
Hydrologic Processes in a Catchment
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Factors affecting Evapotranspiration
•Air Temperature
•Net Radiation
•Wind Speed
•Vapour Pressure
•Relative Humidity
•Soil Moisture
•Type of Vegetation/Crop
•Season of Vegetation/Crop Growth
Source for figure :
http://eoedu.belspo.be/en/applications/evap-contexte.asp?section=
4.1
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Physical characteristics affecting runoff
Land use Vegetation Soil type Drainage area Basin shape Elevation Topography Drainage network patterns Ponds, lakes, reservoirs etc. in the basin
Credit : USGS
Hydro - meteorological factors affecting runoff
Rainfall intensityRainfall amountRainfall durationDistribution of rainfall over the basinAntecedent moisture content
RU
NO
FF
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Climate Change – Hydrologic Implications
Increasing Temperatures Evapotranspiration Water Quality
Change in Precipitation Patterns Streamflow; Water availability Intensity, Frequency and
Magnitude of Floods and Droughts
Groundwater Recharge Rise in Sea Levels
Inundation of coastal areas Salinity Intrusion
Fig. S
ource: ww
.engr.uconn.edulanboG229Lect111S
WIntru.pdfAsian Climate Change
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Research Issues of Interest
•Water availability•How do water fluxes vary on catchment scale in response to global climate events?•Impacts on Water Quality
•Change in Frequency and Magnitude of extreme events •Design storm intensities - Urban Flooding •Delays in onset of monsoon
•Impact on agriculture•Over-year storage policies •Real-time adaptive decisions
•Water Demands • Evapotranspiration • Municipal and Industrial Demands
•Salinity Intrusions & Coastal flooding •Robust & Resilient water management policies to offset adverse impact due to climate change
Source for the map: www.mapsofIndia.com
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Hydrologic impact assessment
Spatio-temporal scale mismatch
Accuracy of tropospheric vs surface variables
Source: Xu C.Y., Water Resources Management 13: 369–382, 1999.Asian Climate Change
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Need for downscaling
A schematic diagram describing the statistical downscaling approach. GCMs provide useful predictions for large-scale atmospheric patterns (lower part). Details contained within a grid box (upper part) are influenced by local features beyond the resolution of current global climate models.[Source: http://www.bom.gov.au/info/GreenhouseEffectAndClimateChange.pdf ]
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Downscaling the GCM outputs to the river basin scales
Grid size of interest in hydrology (~0.20 – 0.50)
Global climate models (GCM: resolution - coarser than 20) ; Size of grid box: Tens of thousands of square kilometers.
GCM Grid(~2.50)
Dow
nscale
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Projecting Climate Change Impacts on Hydrology
Climate Change Projections (precipitation, temperature, radiation, humidity)
Topography, Land-use Patterns; soil characteristics;
Hydrologic Model
Possible Future Hydrologic Scenarios on Basin Scale
(Streamflow, Evapotranspiration, Soil Moisture, Infiltration, Groundwater Recharge etc.)
Downscaling
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DownscalingDownscaling: to model the hydrologic variables (e.g., precipitation) at a smaller scale based on large scale GCM outputs.
Dynamic Downscaling: uses complex algorithms at a fine grid-scale (typically of the order of 50 Km × 50 Km) describing atmospheric process nested within the GCM outputs; commonly known as Limited Area Models (LAM) or Regional Climate Models (RCM).
Statistical Downscaling: produces future scenarios based on statistical relationships between large scale climate features and hydrologic variables.Assumption- Statistical relationships hold good in future for changed climate scenario.Advantage- computationally simple.
Climate Predictors : Must be reliably simulated by GCMs; readily available from archives of GCM outputs and strongly correlated with the surface variables of interest
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Climate change effects in the Colorado river basinN
aturalized: effects of w
ater
managem
ent removed
Source: Christensen et al. (2004), Climatic Change 62, 337–363
Colorado
River basin w
ith 1/8-deg
ree VIC
routing network
and m
ajor system of reservoirs
Drainage Area : 6,30,000 sq. km; Serves 7 states; 12 major reservoirs – water supply, hydropower and flood control ; 70% runoff from Snow pack; Average Annual Runoff : 18.6BCM
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Current demands in the basin are not much lower than the mean flow. A mere 10% reduction in mean annual flow has major implications for the reservoir system performance; Reliability of a reservoir system decreases rapidly as the demands approach the mean flow;
1421 July 2011
Climate change effects in the Colorado river basin
HIST : Historical (observed) : 1950-1999CTRL : Control Climate Simulation (1995 greenhouse gas levels)BAU: Business as usual scenario for periods 1–3: 2010–2039, 2040–2069 and 2070–2098
Sou
rce: C
hristense
n et al. (2004), Clim
atic C
hange 62,
337–363
Downscaled temperature and precipitation from Parallel Climate Model (PCM) – 105 year simulations
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Downscaling GCM Simulations to Precipitation : Orissa Meteorological Sub-division
GC
M G
rids S
urro
un
din
g
the C
ase Stu
dy A
rea
• Coastal Area
• Increase of hydrologic extremes in recent past
• Increase in temperature: 1.10C/century, whereas in average increase in India: 0.40C/century.
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NC
EP
grid
poi
nts
surr
ound
ing
the
met
eoro
logi
cal s
ub-d
ivis
ion
Oris
sa
Climate Predictors: MSLP and 500 hPa geopotential height
Statistical downscaling (Principal component analysis, fuzzy
clustering, transfer functions)
Data Used : Rainfall : 1950-2003 (source : IITM Pune)
Climate Predictors : from NCEP Reanalysis Project Asian Climate Change
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Projected Rainfall
Increase
decrease
CC
SR
/NIE
S G
CM
with
B2 S
cenario
CC
SR
/NIE
S G
CM
with
B2 S
cenario
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GCMs and Scenarios Used
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Projections of SPI (Drought Indicator)
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pdfs of Drought IndicatorW
ater Resources R
esearch , No. 43, (2007)
All scenarios are equally possible
Projections from all GCMs are equally likely to be realized.
Time series generated by a downscaled GCM simulation with one scenario is considered as one realization.
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Projections with A1B Scenario
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Weights for A1B ScenarioAssignment of weights :
Reliability Ensemble Averaging Algorithm
Two reliability criteria :
(a) Performance of the model in reproducing the present-day climate (“model performance”)
(b) Convergence of simulated changes across models (“model convergence”)
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Projected Rainfall(Weighted Mean CDF; A1B scenario)
Limitation Reducing the present knowledge about climate sensitivity to a single probability distribution would clearly mis-represent the scientific disagreement ( Hall et al., 2007).
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Imprecise Probability
Provides an envelope of probability distribution
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Bounds for Probability of DroughtJou
rnal of G
eoph
ysical Research
, 114 (2009)
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Mahanadi River Basin - Streamflow
Predictand:
Monsoon Streamflow of Mahanadi River at Hirakud Dam
Predictors
2m Surface TemperatureGeopotential Height at 500 hPaSpecific HumidityMean Sea Level Pressure
Hirakud Dam
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Selection of Predictors Streamflow: result of rainfall, evaporation and infiltration.
Monsoon: insignificant infiltration compared to streamflow.
Rainfall: consequence of Mean Sea Level Pressure (MSLP) (Bardossy and Plate, 1991; Bardossy et al., 1995; Hughes and Guttrop, 1994), Geopotential Height (Stehlik and Bardossy, 2002) and Specific Humidity (Crane and Hewitson, 1998).
Evaporation: mainly guided by temperature and humidity (Wilby and Harris, 2006)
Predictors selected: 2m surface air temperature, MSLP, geopotential height at 500 hPa and surface specific humidity
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Observed and Predicted Streamflow (from NCEP/NCAR reanalysis data)
Observed Predicted
Mean 7332 Mm3 7384 Mm3
Std. Dev 5996 Mm3 4607 Mm3
Nash-Sutcliffe coefficient, E = 0.67
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Possibilistic Approach Assumptions of earlier models on GCM and scenario uncertainty:
equal possibility and equi-probability of all the scenarios.
With the passage of time, it is relevant to assess the effectiveness of the GCMs in best modeling climate change and also to judge which of the scenarios best represent the present situation under climate forcing.
Scope of the study: assignment of possibility distribution to different GCMs and scenarios, measured in terms of their ability in modeling climate change based on their performance in the recent past (years 1991-2005) under climate forcing
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Possibility Distribution of GCMs and ScenariosW
ate
r Reso
urce
s Rese
arch
, No. 4
4,
(2008), A
GU
Possibility assigned to GCM : possibility with which the future hydrologic scenario is modeled best by the downscaled output of the GCM ; Possibility assigned to a scenario : possibility with which the scenario best represents the climate forcing in the study area
Model U
ncertainty is greater than Scenario U
ncertainty
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Projected Streamflow CDF
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Projections for future monsoon inflows to Hirakud Reservoir
Range of projected future flow duration curves at Hirakud
Reduction in ‘normal’ (middle level) flows
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Projected Irrigation Water Demand : CGCM2; A2 ; Source : Asokan and Dutta (2009)
Projected Peak and Average Discharge; CGCM2; A2; Source : Asokan and Dutta (2009)
Flood Storage
Live Storage
HydropowerIrrigation
Dam
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Gross Storage : 5896 Mm3
Live Storage : 4823 Mm3
Installed Capacity : 347.5 MW
Firm Power : 134 MW
Hirakud Reservoir : Serves Flood Control, Hydropower and Irrigation
Adaptive Operating Policies : Derived with Stochastic Dynamic Programming, with tradeoffs between flood control, hydropower and irrigation, with an objective of maximising hydropower in future years.
2045-65
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Impacts: Performance measures Adaptive Policy using SDP (Policy 1)
Reliability-power Reliability-irrigation Reliability-Flood Control
Current (1959-2005) 0.604 0.834 0.907
A1B A2 B1 A1B A2 B1 A1B A2 B1 2045-65 MIROC3.2 0.5 0.484 0.462 MIROC3.2 0.799 0.798 0.795 MIROC3.2 0.906 0.921 0.939 CGCM2 0.453 0.523 0.471 CGCM2 0.796 0.802 0.801 CGCM2 0.961 0.95 0.955
GISS 0.502 0.515 0.514 GISS 0.802 0.801 0.801 GISS 0.899 0.905 0.897
A1B A2 B1 A1B A2 B1 A1B A2 B1 2075-95 MIROC3.2 0.366 0.286 0.382 MIROC3.2 0.592 0.497 0.601 MIROC3.2 0.944 0.93 0.916 CGCM2 0.276 0.146 0.294 CGCM2 0.544 0.447 0.535 CGCM2 0.95 0.988 0.984 GISS 0.403 0.423 0.458 GISS 0.599 0.614 0.634 GISS 0.916 0.902 0.894
Resiliency-power Vulnerability-power Deficit ratio-power
Current (1959-2005) 0.229 0.688 0.311
A1B A2 B1 A1B A2 B1 A1B A2 B1 2045-65 MIROC3.2 0.214 0.215 0.206 MIROC3.2 0.824 0.895 0.931 MIROC3.2 0.395 0.41 0.43 CGCM2 0.218 0.224 0.202 CGCM2 0.956 0.75 0.935 CGCM2 0.429 0.381 0.395 GISS 0.215 0.221 0.213 GISS 0.911 0.873 0.903 GISS 0.381 0.377 0.371
A1B A2 B1 A1B A2 B1 A1B A2 B1 2075-95 MIROC3.2 0.155 0.159 0.177 MIROC3.2 1 0.933 0.966 MIROC3.2 0.558 0.65 0.571 CGCM2 0.123 0.103 0.255 CGCM2 0.952 1 1 CGCM2 0.677 0.8 0.673 GISS 0.178 0.175 0.18 GISS 0.878 0.883 0.865 GISS 0.525 0.501 0.466
35Asian Climate Change
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Rule curve for adaptive policiesRule curve at Hirakud for adaptive policies
178
180
182
184
186
188
190
192
194
1-Jul 1-Aug 1-Sep 2-Oct
Date
Re
se
rvo
ir le
ve
l (m
)
Curr rule curve min
Curr rule curve max
SDP 2045-65
Adaptive policy 1
Adaptive policy 2
Adaptive policy 3
SDP 1959-2005
Advances in W
ater Resou
rces (2010)
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Non-point source of pollution
Impacts on River Water Quality
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Tunga
Bhadra
1 2
3
4
MPML VISL
Shimoga City Sewage
BhadravathiCity
1 2
3
4
55
6
6
7
Harihar City Sewage8
9 8 9 10
10
11
HP
12
14
13
14Dhavangere City Sewage
15
Kumudavathi
Tunga- Bhadra River
Head Water Flow
Point Load
Reach
Reach End point
Check point Haridra
12
13
11
7
Honnali City Sewage
16
MPM - Mysore Paper MillVISL - Vishveshwaraya Iron and Steel LimitedHPF - Harihara Poly Fibers
Shimoga
Lakavalli
Harlahalli
Kuppelur
Byladahalli
Honnali
Schematic Diagram of Tunga-Bhadra River
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Station Variable Period Annual Mean Change
Shimoga
Air Temperature
1988 – 1999 To
2000 - 2006Increase by 0.215 oC
Water Temperature
1988 – 1999 To
2000 - 2006Increase by 0.599 oC
Honnali
Air Temperature
1988 – 1999 To
2000 - 2006Increase by 0.315 oC
Water Temperature
1988 – 1999 To
2000 -2006Increase by 3.34 oC
Kuppelur
Air Temperature
1991 – 2001 To
2002 - 2006Increase by 1.39 oC
Water Temperature
1991 – 2000 To
2002 - 2006Increase by 1.79 oC
Station Period% Reduction
in Annual Mean Flow
Shimoga1971 – 1991 To 1992 - 2006
3.1
Honnali1980 – 1990 To 1991 - 2006
12.26
Kuppelur1991-1999 To 2000-2006
16.8
Byladahalli
1985 – 1995 To 1996 - 2005
24.16
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Temperature
Streamflow
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Climate Change Impact Assessment ; Adaptive Policies
Climate Change Projections
Statistical Downscaling
River Water Quality Simulation
Model
Water Quality Responses
Optimal Effluent Treatment Levels(Fuzzy Effluent Load Allocation
Model)40
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Observed and CCA Predicted Projections from MIROC 3.2 GCM (A1B) at Shimoga along Tunga River
41
Observed Predicted20
25
30
Mon
thly
Ave
rage
Air
Tem
pera
ture
in d
eg C
(A)
2010-2040 2040-2070 2070-210022
26
30
(B)
Observed Predicted25
30
35
40
Mon
thly
Max
imum
Air
Tem
pera
ture
in d
egre
e C
(A)
2010-2040 2040-2070 2070-2100
30
35
40
(B)
Observed Predicted
15
20
25
Mon
thly
Min
imum
Air
Tem
pera
ture
in d
egre
e C
(A)
2010-2040 2040-2070 2070-2100
18
20
22
24
(B)
Observed Predicted40
60
80
100
Mon
thly
Rel
ativ
e H
umid
ity
(A)
2010-2040 2040-2070 2070-2100
65
70
75
80
(B)
Average Air Temperature Maximum Air Temperature Minimum Air Temperature
Relative Humidity
Observed Predicted
2
4
6
8
Mon
thly
Win
d Sp
eed
in k
mph
(A)
2010-2040 2040-2070 2070-2100
2
4
6
(B)
Observed Predicted20
25
30
35
Wate
r Tem
pera
ture
in d
egre
e C
(A)
2010-2040 2040-2070 2070-2100
20
25
30
35
(B)
River Water Temperature Wind Speed
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6.72
6.06
5.55
4.89
3.50
4.00
4.50
5.00
5.50
6.00
6.50
7.00
present 2010-2040 2040-2070 2070-2100
Diss
olve
d O
xyge
n m
g/L
Check Point 1
5.82
5.244.96
4.70
3.50
4.00
4.50
5.00
5.50
6.00
6.50
7.00
present 2010-2040 2040-2070 2070-2100
Diss
olve
d O
xyge
n m
g/L
Check Point 11
5.41
4.854.44 4.30
3.50
4.00
4.50
5.00
5.50
6.00
6.50
present 2010-2040 2040-2070 2070-2100
Diss
olve
d O
xyge
n m
g/L
Check Point 14
Present and Future Estimates of DO Levels at Various Check Points along Tunga-Bhadra River
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0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1 2 3 4 5 6 7 8
Fra
ctio
nal
Rem
ov
al L
evel
s
Discharger
Current
2010-2040
2040-2070
2070-2100
Current and Projected Treatment Policy
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Q
After Urbanization
Before Urbanization
Urbanisation alters the hydrology of a region; rainfall – runoff relationships get affected; quicker and higher peak flows ; more runoff
Urban Flooding
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4621 July 2011
Urban Flooding
Bangalore Floods
Likely changes in IDF (Intensity-Duration-Frequency) relationships due to climate change
How do the short term intensities of rainfall respond to the climate change?
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Toronto
50
55
60
65
70
75
80
85
90
95
Extreme precipitation recurrence time (Years)
Pre
cipi
tatio
n In
tens
ity (
mm
/hou
r)
10 20 40 8030 50 60 70
1985
2050
2090
Source : Simonovic, 2005
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4821 July 2011
Comparison of IDF for return period of 10 years
76.789
53.898
26.968
15.25
59.653
43.471
17.45
9.5709
90.174
62.672
33.651
19.124
0
10
20
30
40
50
60
70
80
90
100
1 2 6 12 24
Duration (hours)
Ra
infa
ll In
ten
sit
y (
mm
/h)
1969-2003
1969-1986
1987-2003
Bangalore City – Change in the IDF Relationships
Results not conclusive, because of the small sample of data availableAsian Climate Change
4921 July 2011
Summary Climate change is likely to impact most hydrologic
processes Impacts need to be assessed at regional/riverbasin and
smaller scales GCMs are the most credible tools available today for
impact assessment Scale issues and uncertainties are addressed in recent
studies Results from the studies are useful in developing
adaptive responses (e.g., long term reservoir operating policies; modifications in hydrologic designs; change in cropping patterns; water use adjustments etc.)
Similar results may be used in developing Intensty-Duration-Frequency (IDF) relationships and Flow-Duration curves, accounting for Climate Change.
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THANK YOU
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5121 July 2011
• Basic Equation
n
ll hXxKnhxf
1
1ˆ
- kernel density estimator of a pdf at x
n - number of observations h - smoothing parameter known as bandwidth
xf̂
Kernel Density EstimationKernel Density Estimation
Selection of bandwidth - an important step in kernel estimation method.
31
0 587.1
nh
349.1,min
IQRS
Conventional Method
(Silverman, 1986):
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5221 July 2011
Kernel Density Estimation: Drawbacks A large sample can give a better estimate of kernel density
estimator. In the present analysis, the sample size is small only having the downscaled SPI of the available GCM output, which may not lead to accurate results
The bandwidth is estimated by assuming the actual density as normal, which may not be the actual case. In such cases the estimate may be inaccurate.
Asian Climate Change
5321 July 2011
Orthonormal Series Method Orthonormal series: series of functions of an orthonormal
system
Properties:
Used to determine the nonparametric PDF of a small sample (Effromovich, 1999).
Series Used: Cosine System
sdxx
jsdxxx
s
js
1
0
2
0 0
J
J j j J j jj j
f x x or f x x
0cos2
10
jjxx
x
j Asian Climate Change
5421 July 2011
Determination of CoefficientsEstimation of j
( )j jf x x dx
From probability methods:
dxxxfxE jj
It can be said
n
lljj
jj
xn
or
xE
1
1
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5521 July 2011
Algorithm for pdf Estimation with Orthonormal SeriesAlgorithm for pdf Estimation with Orthonormal Series
Determination of bounds/support of the data set
Scaling of data set
Determination of functions of
orthonormal series
Determination ofFourier
coefficientsDetermination
of cut-off
Determinationof smoothing
parameter
Modifications for smoothness, area,
negative values
Determination of pdf of scaled
data
Determination of final pdf of
unscaled data
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5621 July 2011
Objectives of the Possibilistic Approach Assumptions of earlier models on GCM and scenario
uncertainty: equal possibility and equi-probability to all the scenarios.
With the passage of time, it is relevant to assess the effectiveness of the GCMs in best modeling climate change and also to judge which of the scenarios best represent the present situation under climate forcing.
Scope of the study: assignment of possibility distribution to different GCMs and scenarios, measured in terms of their ability in modeling climate change based on their performance in the recent past (years 1991-2005) under climate forcing
Asian Climate Change
5721 July 2011
Theory of Possibility DistributionX: variable in the universe , and is not possible to
measure precisely
Possibility that X can take a value x:
X(x): [0,1]
X(x)=0: Denotes X=x is impossible
X(x)=1: Denotes X=x is possible without any restriction
: Interpreted as complete ignorance about X
1,X x x
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5821 July 2011
Modeling GCM and Scenario Uncertainty with Possibility Theory
Assignment of possibility to GCMs and scenarios: based on system performance in recent past (1991-2005) when climate forcings are visible.
System performance measure: Deviation of the predicted CDF from the observed CDF.
System performance measure (C): similar to Nash-Sutcliffe coefficient.
Normalization of C values to obtain possibility distribution
2
21 OF PFF
OF OF
Q QC
Q Q
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5921 July 2011
Graphical models Combine probability theory and graph theory Family of probability distributions that factorize according to an
underlying graph G=(V,E). Vertices ~ random variables, edges ~ statistical dependencies
Directed graph (Bayesian Network) Variable is conditionally independent of all other variables given its
parents
Undirected graph (Markov Random Field / Markov network) Variable is conditionally independent of all other variables given its
neighbors Do not impose acyclicity constraint / constraints on causality
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Every undirected model can be represented as a factor graph Distribution over a large number of random variables represented as
product of local functions
Where is a global normalizing constant.
are clique potentials, functions from sets of nodes to nonnegative reals.
Factor graph representation
Graphical models (contd.)
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6121 July 2011
Generative model : Based on joint distribution p(y,x) Simplifying independence assumptions, else has to
account for correlated features of input Eg: Naïve Bayes classifier
Task: Predict class variable y (say, whether an email is spam / not spam) given a vector of features x = (x1,x2,...xk) (say, from address in predetermined list, contents more than certain size, subject contains word from predetermined list, etc). Model of joint distribution is
Discriminative model : Based on conditional distribution p(y|x) Does not need model for p(x) Sufficient for classification tasks Model does not need to account for complex dependencies
among input variables. Better suited to include rich, overlapping features Eg: Logistic regression (maximum entropy)
K
jjjyy x
xZyp
1, }exp{
)(
1)|( x
K
kk yxpypyp
1
)|()(),(
x
Generative vs discriminative models
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6221 July 2011
HMMs vs linear chain CRFs
HMMs Linear chain CRFs
Type of Bayesian Network Markov random field globally conditioned on input (observation) variables x
Generative model Discriminative model
Assigns a joint probability to paired observation and label sequences
Assigns a conditional probability to label sequences given observation sequence
Parameters trained to maximize joint likelihood Parameters trained to maximize conditional log likelihood
Conditional distribution is a linear chain CRF which includes features only for the current input variable xt
(at time t)
Joint distribution may have many forms, one of which is an HMM
Graphical model representation of an HMM One of the many graphical model representations of a linear chain CRF
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6321 July 2011
IPCC SRES (2001)
A1 A2 B1 B2
A1TA1BA1F
Scenarios(40)
(family)
(group)
Non fossil fuelBalanced
Fossil fuel
Scenario family
A1 A2 B1 B2
World order Integrated Divided Integrated Divided
Ecologically friendly
No No Yes Yes
Population Increases till 2050 and then declines
Continuously increasing
Same as A1 Increasing but lower than A2
Economic growth
Rapid Regionally oriented
Rapid (service-oriented)
Intermediate
Technology growth
Rapid Slower fragmented
Rapid Slower fragmentedAsian Climate Change
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IPCC Scenarios
Asian Climate Change
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Climate change projections
Source:Meehl et al., Climate Change 2007: The Physical Science Basis, WG I, AR4, IPCC
Asian Climate Change