Hydrologic Impacts of Climate Change : Scale Issues and Uncertainties P P Mujumdar Dept. of Civil...

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

Asian Climate Change

321 July 2011

Source: http://hydrogeology.glg.msu.edu/research/active/modeling-and-monitoring-hydrologic-processes-in-large-watersheds

Hydrologic Processes in a Catchment


421 July 2011

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


521 July 2011

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


621 July 2011

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

721 July 2011

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


821 July 2011

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

921 July 2011

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 ]


1021 July 2011

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


1121 July 2011

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


1221 July 2011

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


1321 July 2011

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


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


1521 July 2011

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.


1621 July 2011

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

1721 July 2011

Projected Rainfall

Increase

decrease

CC

SR

/NIE

S G

CM

with

B2 S

cenario

CC

SR

/NIE

S G

CM

with

B2 S

cenario


1821 July 2011

GCMs and Scenarios Used


1921 July 2011

Projections of SPI (Drought Indicator)


2021 July 2011

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.


2121 July 2011

Projections with A1B Scenario


2221 July 2011

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”)


2321 July 2011

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).


2421 July 2011

Imprecise Probability

Provides an envelope of probability distribution


2521 July 2011

Bounds for Probability of DroughtJou

rnal of G

eoph

ysical Research

, 114 (2009)


2621 July 2011

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


2721 July 2011

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


2821 July 2011

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


2921 July 2011

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


3021 July 2011

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


3121 July 2011

Projected Streamflow CDF


3221 July 2011

Projections for future monsoon inflows to Hirakud Reservoir

Range of projected future flow duration curves at Hirakud

Reduction in ‘normal’ (middle level) flows


3321 July 2011

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


3421 July 2011

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


3521 July 2011

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

3621 July 2011

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)


3721 July 2011

Non-point source of pollution

Impacts on River Water Quality


3821 July 2011

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


3921 July 2011

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


Temperature

Streamflow

4021 July 2011

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


4121 July 2011

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)


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)


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)


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


4221 July 2011 Asian Climate Change

4321 July 2011

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


4421 July 2011

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


4521 July 2011 t

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


User

Here I deleted two pictures as they had appeared before in slide no. 7

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?


4721 July 2011

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


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.


5021 July 2011

THANK YOU


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):


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.


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


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


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


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


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


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


6021 July 2011

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.)


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


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


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

6421 July 2011

IPCC Scenarios


6521 July 2011

Climate change projections

Source:Meehl et al., Climate Change 2007: The Physical Science Basis, WG I, AR4, IPCC


Hydrologic Impacts of Climate Change : Scale Issues and Uncertainties P P Mujumdar Dept. of Civil...

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Transcript of Hydrologic Impacts of Climate Change : Scale Issues and Uncertainties P P Mujumdar Dept. of Civil...