Incorporation of Climate Change Effect on Indian Hydroclimatology

Dr. Rajib Maity

Assistant professor, IIT Bombay, India

Current Position: Honorary Visiting ScholarSchool of Chemistry, Physics and Earth Sciences

Flinders University, Adelaide, Australia

June 18,2008 2

Introduction

Overview of climate change impact

Indian hydroclimatology: Continental scale

Indian Summer Monsoon Rainfall (ISMR)

Large-scale coupled circulation pattern: ENSO and EQUINOO

Prediction of ISMR using ENSO and EQUINOO

Indian hydroclimatology: Basin scale

ENSO, EQUINOO and Basin-Scale Streamflow

Traditional modeling approaches vs. hydroclimatological approach

Conclusions

Outline

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Climate Change Overview

Web Source: http://geoscape.nrcan.gc.ca/h2o/bow/climate_e.php

How to model hydrologic time series under climate change scenario?

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AtmosphericScience

Hydrology and Water Resources

Overview

Hydroclimatic Teleconnection: The significant association between hydrologic events and large-scale atmospheric circulation patterns, which are widely separated (planetary scale) across the globe, is referred as ‘hydroclimatic teleconnection’.

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Natural variability of hydrologic variables and hydroclimatic teleconnection

Volu

me

(M A

cre-

Feet

)

Year

It is recently being established that temporal structure of hydrologic time series is significantly forced by large-scale atmospheric circulation patterns throughhydroclimatic teleconnection (Jain and Lall, 2001)

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Major Issues/Challenges

• Dynamic nature of cause-effect relationship between hydrologic time series and large-scale circulation is to be captured.

• Uncertainty associated with the prediction to be addressed

• Consideration of nonstationarity in time series, expected under climate change scenario.

• ‘Large-Scale’ to ‘Basin-Scale’: It is necessary to establish the link.

• Being most essential for Hydrology and Water Resources responses of ‘basin-scale’ hydrologic variables to ‘large-scale’ atmospheric circulation will be investigated

• Use of such relationship with a goal to improve the prediction performance of hydrologic time series.

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Source: NOAA

El Niño-Southern Oscillation (ENSO) is the coupled Ocean-atmosphere mode of tropical Pacific Ocean (Cane, 1992)

Attempts were made to forecast hydrologic variables, like rainfall, streamflow, etc., using ENSO information all over the world (Dracup and Kahya, 1994; Eltahir, 1996; Jain and Lall, 2001).

El Niño-Southern Oscillation(ENSO)

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El Niño condition La Niña condition

SST(°C)

AnomalousSST (°C)

Source: NOAA

El Niño-Southern Oscillation (ENSO)

AnomalousPressure

(mb)

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Source: NOAA

El Niño-Southern Oscillation(ENSO)

of 38

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Source: NOAA

El Niño conditionNormal conditionLa Niña condition

Change in Atmospheric Circulation Pattern

of 38

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Impact of El Niño

Rainfall defienciesAfrica, the Indian Subcontinent, northern China, Australia, northern South America and the Caribbean

Low river dischargeNile (Egypt), Senegal (Senegal), Orange (South Africa), Krishna (India), Murray-Darling (Australia) and Amazon (Brazil) river system

Surplus rainfallWestern Europe, eastern Africa, Western Cape of South Africa, Gulf regions of the united states and northern Mexico and central Chile.

Sources: NOAAAllan et al., 1996

Additional Slide #

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Impact of La Niña

Increased RainfallAfrica, the Indian Subcontinent, northern China, Australia, northeastern South America

High River DischargeNile, Senegal, Orange, Krishna, Murray-Darling and Amazon

Enhanced Monsoonal/ Tropical-linked summer rainfallSouthern Africa and India, and prominent cloud band influences lead to rainfall surplus regions over southern Australia and southern Africa

Sources: NOAAAllan et al., 1996

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Niño Regions

Niño 3.4 sea surface temperature anomaly (Niño 3.4 SSTA) is best correlated with Indian summer monsoon rainfall

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Equatorial Indian Ocean Oscillation (EQUINOO)

Atmospheric component of Indian Ocean Dipole (IOD) Mode is known as EQUINOO.

EQUINOO is defined as the oscillation between two opposite states of convection over EEIO and WEIO.

Equatorial zonal wind index (EQWIN) is considered as an measure of this which is the negative of the anomaly of zonal wind at Eq. I O Reg.

EEIO = 90° – 110° E, 10° S – 0°WEIO = 50° – 70° E, 10° S – 10° NEq I O Reg = 60° – 90° E, 2.5° S – 2.5° N

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Bayesian Dynamic Linear Model (BDLM)

Dynamic nature of the model helps to capture the dynamic relationship between climate information and hydrologic variables

Allowance incorporation of exogenous inputs

Relaxation of stationarity assumption

Allowance for on-line external intervention

Probabilistic Forecast

Important Features

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Model Calibration

Calibration Period: 1959 – 1985

Performance Statistics

• Correlation coefficient (CC) between observed and predicted rainfall anomaly

• CC between observed and predicted rainfall • Log-likelihood• Mean Square Error (MSE)

Testing Period: 1986 – 2003

Additional Slide #

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Model Performance

June 18,2008 18

Model Performance

Correlation coefficient between observed and predicted rainfall during monsoon months = 0.82

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Model Performance

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Comparative Performance

a) Only ENSO index,

b) Only EQUINOO index and

c) Both ENSO and EQUINOO indices

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Semiparametric approach using Copula

A copula is a function that joins or couples multiple distribution functions to their one-dimensional marginal distribution functions.Let X and Y be a pair of ‘Random Variables’ with cdf as FX(x) and GY(y). Also let their joint cdf as HX,Y(x,y).

( )xFx

( )yGy

( )0,1

( )1,0

( )0,0

( )yxH yx ,,

0 1( )yx,

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Observations (1958 – 1985) Observations (1986 – 2003)

Simulation with different copulas

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Comparison between observed and predicted rainfall anomaly

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Spatial Variation

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At monthly time-scale, the hydroclimatic teleconnection between basin-scale streamflow and large-scale atmospheric circulation pattern is more complex.

Genetic Algorithm based Evolutionary Optimizer is opted to obtained optimized Artificial Neural Network (ANN), which is used to captured the complex relationship

Sub-basin Map of Mahanadi River

Monthly streamflow are measured at Basantpur

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Stepsa) Parameters initialization: All the parameters like population size (N), number of

maximum generations, probability of crossover (Pc), and probability of mutation (Pm) are set to specific values. N = 50, Pc = 0.2, Pm = 0.04

b) Generation of initial population: At initial generation, the evolutionary optimizer randomly creates N networks for the initial population

c) Training of the network and fitness evaluation: Networks are trained by back propagation algorithm and their fitness values are determined according to the goals to be achieved. These goals are (i) maximum number of neurons used in the network (Model Parsimony), (ii) the mean square deviation and (iii) maximum square deviation for the training set. Weighted average (expressed as percentage) of the goal parameters is used as the fitness of the individual network. Based on the fitness values, ranking of the network is done

Genetic Algorithm based Evolutionary Optimizer

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d) Evolution of new generation:

i) Two “parent” networks will be chosen out of the old generation. The selection algorithm will choose networks with a high fitness by a higher probability.

ii) Two “children” networks will be created from the two “parent” networks. Using the cross over probability (Pc), the two “parent” networks will be crossed over, i.e., they will swap a portion of the network with each other.

iii) The “children” will be mutated with a mutation probability (Pm)

iv) A few elitist members of the population in current generation are also carried to the next generation in addition to the “children”. The selection continues until the new generation also has N members. After completion, the new generation will be evaluated.

e) Checking for Termination Criteria: Evolution is stopped, if the target goals are achieved at least for one network in the population or maximum number of generations is reached. Otherwise, generation counter is increased by one and steps (c) and (d) are repeated.

Genetic Algorithm based Evolutionary Optimizer

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Model Performance for Testing Period (1992 – 2003)

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Predicted Category of StreamflowObserved Category of Streamflow Low Normal High

Low 12 6 3

Normal 3 20 4

High 0 3 9

( ) ( )randrand CFNCFCFHSS −−=Heidke skill score, = 0.525(Wilks, 2006)

Model Performance for Testing Period (1992 – 2003)

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Considering only streamflow information of previous month(s)

Considering only large-scale atmospheric circulation

information

Considering both streamflow (if necessary) and large-scale

atmospheric circulation information

Inputs* Best Network

Model Perfor-mance#

Inputs* Best Network

Model Perfor-mance#

Inputs* Best Network

Model Perfor-mance#

Jun May SF 1-2-2-1-0.030833.01500.3

Mar EN Mar EQ 2-4-5-3-1

0.5421209.41926.4

Mar EN Mar EQ 2-4-5-3-1

0.5421209.41926.4

Jul Jun SF 1-2-2-20.7314312.85025.6

Jun EN Jun EQ 2-3-2-1

0.2985643.66059.4

Jun SF Jun EN Jun EQ

3-3-5-10.8133570.94425.2

Aug Jul SF 1-2-3-1-10.4122929.23575.7

Jul EN Jul EQ 2-2-1

0.5112721.53225.6

Jul SF Jul EN Jul EQ

3-6-5-10.8361486.02008.5

Sep Jul SF Aug SF 2-2-3-1-1

0.3212331.93260.2

Aug EN Aug EQ 2-3-1-2-1

0.4021877.12719.7

Jul SF Aug SF Aug EN Aug EQ

4-3-2-10.4932101.02635.6

Oct Sep SF 1-2-10.838611.6761.4

Sep EN Sep EQ 2-4-3-1

0.372898.8

1228.1

Sep SF Sep EN Sep EQ

3-5-10.765742.2926.5

Month of Strea-

mflow

Performance for three different cases

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Conclusions

Bayesian dynamic linear model (BDLM) is introduced to the field of hydroclimatology, which is able to capture the time varying dynamic relationship and quantify the uncertainty associated with the predicted values.

A Copula-based method is able to capture the scale-free dependence pattern and provides a nonparametric way to predict the response variable using the information from causal force.

These methods, being general, can be applied to any other similar analysis. Copula-based method is applicable irrespective of the nature of dependence, type of relationship and distributional properties of data sets.

June 18,2008 32

Conclusions

Mahanadi River basin is selected to study the basin-scale hydroclimatic teleconnection. Hydroclimatic association at seasonal scale between monsoonal inflow into Hirakud reservoir on Mahanadi River and large-scale circulation phenomena is established.

At monthly scale, variation of streamflow at Basantpur site on Mahanadi River is investigated. The information of streamflow from previous month(s) alone may not be sufficient.

Consideration of both the information of previous streamflow and the two large-scale atmospheric circulations is important for basin-scale monthly streamflow prediction improves the prediction performance .

June 18,2008 33