IAS_SRF_Project_2015

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Impact of Climate Change on
Precipitation Characteristics in
Guwahati,
using ESM model (RCP 4.5)
Submitted by :
Swatah Snigdha Borkotoky
Under the guidance of :
Prof. Arup K. Sarma
Department of Civil Engineering,
Indian Institute of Technology, Guwahati,
781039
July, 2015

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ABSTRACT
The Brahmaputra valley in India is known to be among the highest
precipitated areas in the world. In addition to that it is also susceptible to
unexpected and long dry spells. The Guwahati region in NE India is no
different. However, in the past couple of decades the advent of rapid
industrialization has brought about conspicuous changes in the climate of
this region. As such, analysis of available data so as to project future
scenarios in as immaculate manner as possible is imperative.
In this study, dataset of ESM (Earth System Model) under RCP
(Representative Concentration Pathways) 4.5 has been used. Also, recorded
precipitation data from IMD (Indian Meteorological Department) in Guwahati
(1969-2011) is utilized. Then the data has been downscaled using statistical
downscaling (Multiple Linear Regression).
The data obtained from this study is used for frequency analysis using
Gumbel Distribution. Then the results are analyzed to project the no. of dry
days, maximum monthly, and total monthly precipitation with a given return
period.

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CONTENTS
Abstract
Contents
1. Introduction
 Climate Model - an Introduction
 GCM (Global Circulation Model)
 Objectives of this study
2. Downscaling from ESM
 ESM (Earth System Model)
 Downscaling - Theory & Procedure
3. Study of Impact of Climate Change on precipitation in Guwahati
 Multiple Linear Regression
 Selection of predictors
 Calibration & Validation
 Projection of Future data
 Results & Inference
4. Flood frequency analysis
 Total Monthly Precipitation
 Maximum Monthly Precipitation
 No. of Dry Days
 Inference
5. Conclusion & further study
 Conclusion
 Further Study
6. References

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1. INTRODUCTION
1.1 Climate Model:
Climate models are numerical models that use quantitative methods to
simulate the interactions of the atmosphere, oceans, land surface, and ice.
They are used for a variety of purposes from study of the dynamics of the
climate system to "projections of future climate".
All climate models take account of incoming energy from the sun as short
wave electromagnetic radiation, chiefly visible and short-wave (near) infrared,
as well as outgoing energy as long wave (far) infrared-electromagnetic radiation
from the earth. Any imbalance results in a change in temperature.
Models can range from relatively simple to quite complex:
 A simple radiant heat transfer model that treats the earth as a single point
and averages outgoing energy
 this can be expanded vertically (radiative-convective models), or horizontally
 finally, (coupled) atmosphere–ocean–sea ice global climate models and
solve the full equations for mass and energy transfer and radiant exchange.
1.2 GLOBAL CLIMATE MODEL
A Global Climate Model, commonly referred to as general circulation
model (GCM), a type of climate model, is a mathematical model of the
general circulation of a planetary atmosphere or ocean and based on
the Navier–Stokes equations on a rotating sphere with thermodynamic
terms for various energy sources (radiation, latent heat). These equations
are the basis for complex computer programs commonly used
for simulating the atmosphere or ocean of the Earth.

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Atmospheric and oceanic GCMs (AGCM and OGCM) are key components
of global climate models along with sea ice and land-surface components.
GCMs and global climate models are widely applied forweather forecasting,
understanding the climate, and projecting climate change. Versions
designed for decade to century time scale climate applications were
originally created by Syukuro Manabe and Kirk Bryan at the Geophysical
Fluid Dynamics Laboratory in Princeton, New Jersey. These computationally
intensive numerical models are based on the integration of a variety of fluid
dynamical, chemical, and sometimes biological equations.
1.3 OBJECTIVES of this STUDY:
 To develop a statistical downscaled model for projecting future
precipitation in the Guwahati.
 To analyze the precipitation pattern in Guwahati.
 To analyze the changes in the future so as to mitigate the
consequences of flood or drought.
 To develop frequency analysis using Gumbel Distribution, so as to
compliment the results of the downscaled model.

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2. DOWNSCALING from ESM
2.1 ESM (earth system model)
GFDL, Princeton, has constructed NOAA’s (National Oceanic and Atmospheric
Administration) first Earth System Models (ESMs) to advance the
understanding of how the Earth's biogeochemical cycles, including human
actions, interact with the climate system. Like GFDL's physical climate models,
these simulation tools are based on an atmospheric circulation model coupled
with an oceanic circulation model, with representations of land, sea ice and
iceberg dynamics. ESMs incorporate interactive biogeochemistry, including the
carbon cycle. Building the ESMs has been a outcome of large collaborative
effort involving scientists from GFDL, Princeton University, Department of
Interior and other institutions, to study climate and ecosystem interactions and
their potential changes, from both natural and anthropogenic causes.
The atmospheric component of the ESMs includes physical features such as
aerosols (both natural and anthropogenic), cloud physics, and precipitation.
The terrestrial component includes precipitation and evaporation, streams,
lakes, rivers, and runoff as well as a terrestrial ecology component to simulate
dynamic reservoirs of carbon and other tracers. The oceanic component
includes features such as free surface to capture wave processes; water fluxes,
or flow; currents; sea ice dynamics; iceberg transport of freshwater; and a
state-of-the-art representation of ocean mixing as well as marine
biogeochemistry and ecology.
While carbon is necessarily included as the basic building block of ecosystems
undergoing terrestrial and oceanic chemistry, associated chemical and
ecological tracers which control nutrient limitation, plant biomass,
productivity, and functional composition are also included. Chemical tracers
are also tracked in the atmosphere.

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ESMs capture numerous types of emissions, variations of land surface
albedo {the fraction of solar energy, i.e. - shortwave radiation reflected from the
Earth back into space} due to both natural vegetation changes and land use
history such as agriculture and forestry, and aerosol chemistry. Adding these
different components to the ESM represents a major step forward in simulating
the Earth's ecological systems in a comprehensive and internally consistent
context.
2.1.1 ESM2M and ESM2G:
Our first prototype model, ESM2.1, evolved directly from GFDL’s successful
CM2.1 climate model. Building on this, we produced two new models
representing ocean physics with alternative numerical frameworks to explore the
implications of some of the fundamental assumptions embedded in these
models. The models differ mainly in the physical ocean component. In one model,
ESM2M, pressure-based vertical coordinates are used along the developmental
path of GFDL’s Modular Ocean Model version 4.1. In the other, ESM2G, an
independently developed isopycnal (an imaginary line or surface on a map or
chart) connecting points in the ocean where the water has the same density)
model using the Generalized Ocean Layer Dynamics (GOLD) code base was
used.
2.1.2 Comparison:
Comparison between these two models allows us to assess the sensitivity of the
coupled climate-carbon system to our assumptions about ocean formulation.
Both ESM2M and ESM2G utilize a more advanced land model, LM3, than was
available in ESM2.1 including a variety of enhancements. While the models
demonstrate similar overall scale fidelity, they have important differences in both
their thermocline (a steep temperature gradient in a body of water such as a
lake, marked by a layer above and below which the water is at different
temperatures) characteristics, deep circulation, ventilation patterns and El Nino
variability that suggest critical roles for details of ocean configuration in the
coupled carbon climate system.

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2.2 DOWNSCALING:
Global Climate Models (GCMs) used for climate studies and climate projections are run
at coarse spatial resolution (in 2012, typically of the order 50 kilometers (31 mi)) and are
unable to resolve important sub-grid scale features such as clouds and topography. As
a result GCM output cannot be used for local impact studies.
To overcome this problem downscaling methods are developed to obtain local-
scale weather and climate, particularly at the surface level, from regional-scale
atmospheric variables that are provided by GCMs. Two main forms of downscaling
technique exist:
 Dynamical downscaling, where output from the GCM is used to drive a
regional, numerical model in higher spatial resolution, which therefore is able
to simulate local conditions in greater detail.
 Statistical downscaling, where a statistical relationship is established from
observations between large scale variables, like atmospheric surface
pressure, and a local variable, like the wind speed at a particular site. The
relationship is then subsequently used on the GCM data to obtain the local
variables from the GCM output.
In 1997, Wilby and Wigley divided downscaling into four categories:
regression methods, weather pattern-based approaches, stochastic weather
generators, which are all statistical downscaling methods, and limited-area modeling.
Among these approaches regression methods are preferred because of its ease of
implementation and low computation requirements.

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2.2.1 STANDARDIZATION:
Before calibration, the large scale climate variables need to be processed. In
this study, pre-processing of data has been done by the method of
standardization. Here
standardization has been used to reduce the biases in the mean and variance
of the ESM predictors relative to the observed data and to each other. In the
process of standardization the mean (μ) is
subtracted from the ith predictor/predictant and then it is divided by the
standard deviation.
X‫ݐݏ‬݀ (݊) =
X݅(݊) − μ (݊)
σ (݊)
Where, Xstd is the standardized data of nth predictor
Xi is the ith variable of the nth predictor, μ is the mean of all the variables of nth
predictor, and σ is the standard deviation.
The primary steps in downscaling are
I. Specification: In this stage, model and predictors are selected.
II. Calibration: Here MLR (Multiple Linear Regression), using is carried
out against the standardized predictants and the recorded predictor of
different combinations. The coefficients of respective predictors and
their residuals are noted down.
III. Validation: Here the accuracy of the model is put to test in this stage.
The R2
value of both models (with and without Residuals) are checked.
The one with the highest R2
is chosen for future projection.
IV. Projection: The selected model (having the highest R2
value) is utilized
for future projection of the predictants.

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3. Study of Impact of Climate Change
on Precipitation in
Guwahati
In this study, statistical downscaling has been carried out using Multiple
Linear Regression. The predictors (LSAVs) were shortlisted using Pearson
Correlation. The local values of these predictors in Guwahati have been
calculated using linear interpolation from 4 nearest ESM grid points, {namely
A (58,37); B(58,38); C(59,38) & D(59,37)}.
From the data obtained, analysis was carried out upon the results.
Although these four grid points are not exactly in the heartland of Assam, they
are the nearest ESM Grid points to Guwahati and therefore should represent
the parameters to the nearest extent possible.

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3.1 Multiple Linear Regression:
Multiple linear regression attempts to model the relationship between two or
more explanatory variables and a response variable by fitting a linear equation
to observed data. Every value of the independent variable x is associated with a
value of the dependent variable y.
The population regression line for p explanatory variables x1, x2, ... , xp is
defined to be y = 0 + 1x1 + 2x2 + ... + pxp. This line describes how the
mean response y changes with the explanatory variables. The observed
values for y vary about their means y and are assumed to have the same
standard deviation . The fitted values b0, b1, ..., bp estimate the
parameters 0, 1, ..., p of the population regression line.Since the observed
values for y vary about their means y, the multiple regression model
includes a term for this variation. In words, the model is expressed as DATA =
FIT + RESIDUAL, where the "FIT" term represents the expression 0 +
1x1 + 2x2 + ... pxp. The "RESIDUAL" term represents the deviations of the
observed values y from their means y, which are normally distributed with
mean 0 and variance . The notation for the model deviations is .
Formally, the model for multiple linear regression, given n observations,
is
yi = 0 + 1xi1 + 2xi2 + ... pxip + i for i = 1,2, ... n.
Assumptions: The following assumptions were taken into consideration while
using the multiple linear regressions:
1. The relation between Y and X1, X2,…, Xn are linear.
2. The residuals have a constant variance σ and are normally
distributed.
3. There is no autocorrelation
4. The X variables are fixed.

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3.2 SELECTION of PREDICTORS:
The predictors were selected on the basis of their Pearson-Correlation values,
which range from -1 to 1. The ones having Pearson-Correlation values close to
1 or -1 were used for downscaling. The Pearson values of Predictors are -
The above table clearly indicates that for total monthly precipitation, HUSS,
PR, PRC, PSL, RLDS, RLUS, TAS, TAS_MIN show the highest correlation (if the
cut-off is taken as 0.65). As such they are used in different combinations for
calibration. From this table, it is evident that for maximum monthly
precipitation, the predictors of HUSS,PRC, RLDS & TAS_min have the highest
correlation (with the cut-off of 0.65). They are used further for calibration
studies. From the above correlation table, we can see that for no. of dry days,
the Predictors HUSS, PRC, PSL,RLDS, TAS, and TAS_min have the highest
Pearson correlation values. Ergo they are selected for Calibration.
PREDICTORS ACRONYMS Total Monthly Maximum Monthly No. of Dry Days
Total Cloud Cover clt 0.626067543 0.590463676 -0.635614323
Surface Upward Latent Heat Flux hfls 0.619883106 0.589532101 -0.664498572
Surface Upward Sensibel Heat Flux hfss 0.133916035 0.163787149 -0.209035878
Near Surface Specific Humidity huss 0.750626327 0.686517905 -0.78183408
Precipitation pr 0.706359648 0.620076951 -0.72843451
Convective Precipitation prc 0.746606006 0.664364991 -0.789208405
Sea Level Pressure psl -0.775422561 -0.639109546 0.854623035
Near Surface Relative Humidity rhs 0.375297203 0.377804912 -0.335291216
Maximum RHS rhs_max 0.306378609 0.312222249 -0.257424904
Minimum RHS rhs_min 0.446886417 0.443416566 -0.418292069
Surface Downwelling Longwave Radiation rlds 0.770631456 0.699496125 -0.820019852
Surface Upwelling Longwave Radiation rlus 0.652303896 0.565929296 -0.763650329
Total Outgoing Longwave Radiation rlut -0.566402451 -0.502464842 0.553437016
Surface Downwelling Shortwave Radiation rsds -0.123411574 -0.150081356 0.045829587
Surface Upwelling Shortwave Radiation rsus -0.204355747 -0.2100199 0.135590734
Daily Mean Near Surface Wind Speed sfc wind -0.018637918 -0.087102006 -0.056845773
Daily Maximum Near Surface Wind Speed sfc wind_max -0.205484345 -0.244574204 0.146294227
Near Surface Air Temperature tas 0.694211972 0.614658281 -0.800381645
Maximum TAS tas_max 0.532217695 0.457905966 -0.647423326
Minimum TAS tas_min 0.784629826 0.704064199 -0.866503562
Eastward Near Surface Temperature uas -0.200343148 -0.233023918 0.136195995
Westward Near Surface Temperature vas 0.286850691 0.191115343 -0.379470347

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3.3 Calibration & Validation:
3.3.1 Total monthly precipitation:
Sl.
No. Combination
Calibration
(R2
)
Validation
(R2
)
Validation with
Residual
1 HUSS,PR,PRC,PSL,RLDS,RLUS,TAS,TAS_min 0.996 0.777 0.763
2 HUSS,PR,PRC,PSL,RLDS,TAS,TAS_min 0.986 0.914 0.918
3 HUSS,PR,PRC,PSL,RLDS,TAS_min 0.986 0.824 0.812
4 HUSS,PRC,PSL,RLDS,TAS_min 0.968 0.905 0.864
5 HUSS,PSL,RLDS,TAS_min 0.964 0.921 0.878
6 PSL,RLDS,TAS_min 0.962 0.931 0.878
After, carrying out MLR with different combinations of the predictors, the one
with the highest R2 value was selected (3 predictors (PSL, RLDS, & TAS_min).
From the validation chart, we can see that MLR without residual gives better
results. As such, it is used for future projection.
y = 0.962x + 52.79
R² = 0.962
0
1000
2000
3000
4000
0 1000 2000 3000 4000
MLR
Observed
Calibration
MLR
MLR_with_R
Linear (MLR)
y = 0.922x + 252.9
R² = 0.931
y = 0.916x + 260.9
R² = 0.878
0
1000
2000
3000
4000
0 1000 2000 3000 4000
MLR
Observed
Validation
MLR
MLR_with_R
Linear (MLR)
Linear (MLR_with_R)

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3.3.2 Maximum Monthly Precipitation:
With only four Predictors having Pearson-Correlation Value greater than
0.65, namely HUSS, RLDS, PRC, and TAS_min, all of them are selected for
testing the model.
From the above graphs, it is evident that MLR without residual will give
better results as it has higher R2 value. Ergo, it is used for future projection
for maximum daily precipitation in a month.
y = 0.959x + 15.60
R² = 0.959
0
100
200
300
400
500
600
700
800
900
0 200 400 600 800 1000
MLR
Observed
Calibration
MLR
MLR_with_R
Linear (MLR)
y = 0.921x + 34.82
R² = 0.927
y = 0.927x + 32.34
R² = 0.889
0
100
200
300
400
500
600
700
800
900
0 200 400 600 800
MLR
Observed
Validation
MLR
MLR_with_R
Linear (MLR)
Linear (MLR_with_R)

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3.3.3 No. of Dry Days:
As the R2 value of the model with combination of all the selected Predictors is
(of 0.985), ergo it is used for validation and projection.
It is evident from the validation graph that MLR with Residual gives better R2
value. Therefore, that model is used for future projection of the no. of dry
days.
y = 0.985x + 0.279
R² = 0.985
0
5
10
15
20
25
30
35
0 10 20 30 40
MLR
Observed
Calibration
MLR
MLR_with_R
Linear (MLR)
y = 0.929x + 1.093
R² = 0.916
y = 0.942x + 0.833
R² = 0.942
0
5
10
15
20
25
30
35
0 10 20 30 40
MLR
Observed
Validation
MLR
MLR_with_R
Linear (MLR)
Linear (MLR_with_R)

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3.4 Projection OF FUTURE DATA:
Using the best fit model (one having the highest R2 value), MLR was operated
on the future predictors to determine the value of future predictants.
3.4.1 Total Monthly Precipitation:
Column1 1969_2011 2012_2040 2041_2070 2071_2100
without_R with_R without_R with_R without_R with_R
MONSOON
total 13499.0263 11790.44451 11641.116 11839.7778 11690.4493 11855.3279 11705.9994
change -708.581768 -1857.9103 -1659.2485 -1808.577 -1643.6983 -1793.0268
%change -2.65707416 -13.763291 -12.291616 -13.397833 -12.176422 -13.282638
NON_MONSOON
total 4161.1803 5230.882951 5380.21145 5181.54967 5330.87817 5165.99953 5315.32802
change 1069.702653 1219.03115 1020.36937 1169.69787 1004.81923 1154.14772
%change 25.70671242 29.2953216 24.5211527 28.1097618 24.1474571 27.7360662
0
1000
2000
3000
4000
jan feb mar apr may jun jul aug sep oct nov dec
Precipitation(inmm)
Without Residual
2012_2040
2041_2070
2071_2100
1969_2011
0
500
1000
1500
2000
2500
3000
3500
4000
Precipitation(inmm)
With Residual
1969_2011
2012_2040
2041_2070
2071_2100

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Column1 1969_2011 2012_2040 2041_2070 2071_2100
without_R with_R without_R with_R without_R withR
MONSOON
Max 774.4474 735.185 812.42 723.9195 801.156 762.417 839.65
change -39.2624 37.974 -50.5278 26.7091 -12.0303 65.206
%change -5.06973 4.9034 -6.52437 3.44879 -1.55341 8.4197
NON_MONSOON
Max 562.0789 568.4725 643.95 554.3231 629.808 553.1619 628.64
change 6.393531 81.878 -7.75583 67.7294 -8.91707 66.568
%change 1.137479 14.567 -1.37985 12.0491 -1.58644 11.843
0
100
200
300
400
500
600
700
800
900
Precipitation(inmm)
Without Residual
1969_2011
2012_2040
2041_2070
2071_2100
0
100
200
300
400
500
600
700
800
900
Precipitation(mm)
with Residual
1969_2011
2012_2040
2041_2070
2071_2100

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Column1 1969_2011 2012_2040 2041_2070 2071_2100
without_
R with_R without_R with_R without_R with_R
MONSOON
MAX 29.12 28.98 29.7860 30.55972 29.9943 30.68155 30.1162
change 0.14 -0.6660 -1.4397 -0.8743 -1.56155 -0.9962
%change 0.480769 -2.2873 -4.9440 -3.0027 -5.3624 -3.421
NON_MONSOO
N
MIN 7.08 6.027882 6.58923 7.036984 6.61402 6.393978 7.30946
change 1.052118 0.49076 0.043016 0.46597 0.686022 -0.2294
%change 14.86042 6.93169 0.60757 6.58151 9.68957 -3.241
0
5
10
15
20
25
30
35
No.ofDryDays
1969_2011
2012_2040
2041_2070
2071_2100
WITHOUT_RESIDUAL
0
5
10
15
20
25
30
35
No.ofDryDays
With Residual
1969_2011
2012_2040
2041_2070
2071_2100

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3.5 Results & Inference:
3.5.1 Total Monthly Precipitation:
 The total monthly precipitation in the Monsoon Period has decreased by an
average of 12.38% (without Residual), and by an average of 13.48% (with
Residual).
 However, the total monthly precipitation in the Non-Monsoon period has
increased by an average of 24.79% (without Residual), and by an average of
28.38% (with Residual).
 Nevertheless, the total annual precipitation has reduced by an average of
3.62% (both with and without Residual).
 The no. of dry days in the Monsoon Period has increased by an average of
3.28% (without Residual), and by an average of 2.90% (with Residual).
 But, the same in the Non-Monsoon Period has decreased by an average of
8.39% (without Residual), and by an average of 3.42% (with residual).
 However, the total no. of dry days over an entire year has reduced on an
average from 225.29 days (from 1969-2011) to 223.93 days (i.e. by 0.61%).
 The maximum monthly precipitation in the Monsoon Period has reduced by
an average of 4.38% (without Residual), but increased by an average of 5.59%
(with Residual).
 In the Non-Monsoon Period the same has reduced by an average of 0.61%
(without Residual), but has increased by an average of 12.82% (with
Residual).
3.5.4 Overall Inference:
 In the Monsoon period, we can expect less rainfall, with greater no. of dry
days.
 In the Non-Monsoon period, more rainfall is to be expected along with more
no. of days with rainfall.

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4. FREQUENCY ANALYSIS
Using the recorded IMD data from 1969 to 2011, Gumbel Distribution was
plotted for Total Monthly Precipitation, Maximum Monthly Precipitation &
No. of Dry Days. The following results were observed :-
4.1 Total Monthly Precipitation:
4.2 Maximum Monthly Precipitation:
0
2000
4000
6000
8000
10000
12000
0 100 200 300 400 500 600
No.ofDryDays
Return Period
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
0
500
1000
1500
2000
2500
3000
0 100 200 300 400 500 600
Precipitation(inmm)
retrun period
Jan
Feb
Mar
Apr
May
Jun
Jul
Jul
Aug
Sep
Oct
Nov

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4.3 No. of Dry days:
4.4 INFERENCE:
 For the total monthly precipitation and maximum monthly
precipitation, July had the highest value for the same return period,
while January had the least value for the same.
 In no. of dry days, for the same return period, December and April
had highest output, while October and July had the least output, in
Non-Monsoon & Monsoon periods.
20
22
24
26
28
30
32
34
0 2 4 6 8 10 12
No.ofDryDays
Return Period (in years)
Non-Monsoon period
Jan
Feb
Mar
Oct
Nov
Dec
5
10
15
20
25
30
35
0 20 40 60 80 100 120
No.ofDryDays
return period (in years)
Monsoon period
Apr
May
Jun
Jul
Aug
Sep

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5. CONCLUSIONS & FUTURE STUDIES
5.1 CONCLUSION:
This study was done on Guwahati, the financial hub of entire NE India. It
also houses the administrative capital of Assam, and the premier judicial
institution of NE, the NE high Court. As such the smooth functioning of this
city is of utmost significance. In order for that to happen, incorporation of
the effects of climate change cannot be overlooked.
The following have been achieved from this study:-
 A downscaled model has been prepared for projecting large scale
variables to atmospheric variables.
 From the projected data, it can be stated that for total monthly
precipitation, the model with Residual gives better results (safer), with
a decrease of 13.48% in Monsoon period, and an increase of 28.38%
in Non-Monsoon period.
 For the no. of dry days, the model without Residual should be
preferred so as to be on the safer side. It gives an increase of 3.28% in
no. of dry days in Monsoon period, and a decrease of 8.39% in Non-
Monsoon period.
 For the Maximum monthly precipitation, safest option would to be
adopt the model with Residual, which gives an increase of 5.59% (in
Monsoon Period), and an increase of 12.82% (in Non-Monsoon Period).

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5.2 Further Study:
From this study it has been observed that climate change has drastically
affected the precipitation in Guwahati. However, this study was done on only
one Concentration Pathways (i.e. RCP 4.5). The predictors which were used
also indicates the parameters which affect the precipitation in Guwahati the
most. To get more comprehensive and viable results, the same work can be
done on the rest of the Concentration Pathways (i.e. RCP 2.6, RCP 6.5 & RCP
8.5). As such further work has to be carried out with different climate
models, so as to juxtapose their results and selecting the suitable one of all.
In order to get to the root of the cause of Climate Change and to acquire
viable mitigation strategies, further probe needs to be done on the subject,
not only in this region but all over the world.

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6. REFERENCES
 Subramanya, K, 2009. "Engineering Hydrology", 3rd Edition,
Publisher: Tata McGraw Hill, ISBN (10): 0-07-064855-7
 Khatua, S.K., Panigrahi B., and Panigrahi, K, 2014. "Probability
Analysis of Maximum Daily Rainfall for Hydrological Design of Soil and
Water Conservation Structures". Journal of Indian Water Resources
Society, Vol. 34, No. 4.
 Vijay P. Singh, “Elementary Hydrology,” publisher Prentice-Hall of
India pvt. Ltd. pp. 800-851, 1994.
 Clement Tisseuil , Mathieu Vrac , Sovan Lek , Andrew J. Wade,
“Statistical downscaling of river flows” Journal of Hydrology vol 385,
pp. 279–291.
 R. Vinnarasi, “Impact of Climate change on Rain fall and stream flow
of Dhansiri basin”, M.tech thesis, IIT Guwahati, may 2012.
 Stehlik, J., Bardossy, A., 2002, "Multivariate Stochastic downscaling
model for generating daily precipitation series based on atmospheric
circulation", Journal of Hydrology 256 (2002) 120-141.
 http://www.gfdl.noaa.gov
 http://www.gfdl.noaa.gov/earth-system-model
 https://en.wikipedia.org/wiki/Climate_model

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