Climate change impact assessment on hydrology on river basins

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CLIMATE CHANGE IMPACT ASSESSMENT ON HYDROLOGY OF

RIVER BASINS

CH.TIRUPATHI131856

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INTRODUCTION APPLICATION OF RS&GIS IN IMPACT OF

CLIMATE CHANGE CLIMATE CHANGE MODELS GCM,RCM DOWN SCALING TECHNICS LITERATURE RIEVIEW CASE STUDIES

CONTENTS

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CLIMATE means “ average weather “

Weather

General Definition: Any systematic change in the long-

term statistics of climate elements (such as

temperature, pressure, or winds) sustained over several

decades or longer.

IMPACTS OF CLIMATE CHANGE

INTRODUCTION

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Remote sensing has emerged as a powerful tool for cost effective data

acquisition in shorter time at periodic intervals (temporal), at different

wavelength bands (spectral) and covering large area (spatial)

The availability of GIS tools and more powerful computing facilities makes

it possible to overcome many difficulties and limitations and to develop

distributed continuous time models, based on available regional information

Application of a distributed hydrologic model Arc SWAT along with GIS

and remote sensing techniques

APPLICATION OF RS&GIS IN IMPACT OF CLIMATE CHANGE

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GLOBAL CLIMATE MODEL(GCM’S) are used to evaluate

the impact of increasing GHG concentrations on climate.

Planetary scale features, but their application to regional

studies is often limited due to its coarse spatial resolution.

REGIONAL CLIMATE MODELS(RCM’S) are developed to

dynamically downscale global model simulations to make

climate projections for a particular region after superimposing

the topographic details of specific regions of interest

CLIMATE CHANGE MODELS

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Poor performances of GCMs at local and regional scales have lead to the

development of Limited Area Models (LAMs) in which a fine

computational grid over a limited domain is nested within the coarse grid

of a GCM This procedure is also known as dynamic downscaling.

Complicated design and high computational cost.

Inflexible in the sense that expanding the region or moving to a slightly

different region requires redoing the entire experiment

DOWNSCALING TECHNIQUES

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Statistical downscaling, in which, regional or local

information about a hydrologic variable is derived by first

determining a statistical model which relates large scale

climate variables (or predictors) to regional or local scale

hydrologic variables .

Then the large scale output of a GCM simulation is fed into

this statistical model to estimate the corresponding local or

regional hydrologic characteristics .

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Figure1.Development of Limited Area Models (LAMs) ,(RCM’s) from GCM’

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The steps involved in assessing impacts of climate change

on river basin scale hydrology

Simulation of large scale climate variables by GCMs.

Downscaling large scale climate variables to local scale

hydro-meteorological variables (e.g., rainfall).

Hydrologic modelling

Analysis of hydrologic extremes

METHODOLOGY

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Nune et.al,. (2013) quantified the impacts climate change and WSD will have on

the hydrologic behavior of the Musi catchment Andhra Pradesh, Global Climate

Model (GCM) predictions and dynamic downscaling approach was used in this

research.

The hydrology of the catchment was modeled using the SWAT hydrologic model

An assessment of the impact of hydrological structures on stream flows shows

that stream flows have been declining due to the growth and impact of these

structures in the catchment.

The flow decline due to hydrological structures was significant during drought

years.

Kulakarni et.al,. (2012) described the usage of hydrological model, PRECIS, SWAT,

three simulations viz. Q0, Q1, Q14, to quantify the impacts of climate change on

the water resources of the Bhīma river basin.

The hydrological model calibration and validation indicates that SWAT model

simulates stream flow appreciably well for this study area.

LITERATURE REVIEW

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Xiyan et.al,. (2011) investigated impacts of climate change on stream flow

in the Yellow River Basin.

They use outputs from a global circulation model (HadCM3), a statistical

downscaling model (SDSM) and a combination of ‘bilinear-interpolation and

delta’ are applied to generate daily time-series of temperature and

precipitation.

The results modelled responding to SDSM fit natural or measured records

better than responding to the combination method.

Kenji et.al,. (2008) explored the potential impacts of climate change on the

hydrology and water resources of the Seyhan River Basin in Turkey.

A dynamical downscaling method, referred to as the pseudo global warming

method (PGWM), was used to connect the outputs of general circulation

models (GCMs) and river basin hydrologic models.

They concluded that PGWM combined with bias-correction is extremely

useful to produce input data for hydrologic simulations.

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Aleix et.al,. (2007) discussed the assessment of climate change impacts

in the water resources of a semi-arid basin using results from an ensemble

of 17 global circulation models (GCMs) and four different climate change

scenarios from the Intergovernmental Panel on Climate Change (IPCC).

The use of multiple climate model results provides a highest-likelihood

mean estimate as well as a measure of its uncertainty and a range of less

probable outcomes.

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“Assessing hydrological response to changing climate in the Krishna basin”

AUTHORS : B. D. Kulkanri & S. D. Bansod

Study Area : The central portion of the Indian Peninsula The drainage area of the entire basin is about 2,58,948 km2 of which 26.8% lies in

Maharashtra, 43.8% in Karnataka and 29.4 % in Andhra Pradesh

Data inputs for Hydrological modeling The SWAT model requires data on terrain, land use, soil, weather for the

assessment of water-resources availability at desired locations of the drainage basin.

Spatial Data

(1) Digital Elevation Model (DEM)

( 2) Soil Data Layer

(3) Land Use/ Land Cover layer Climatic data

Weather Data (Climate Model Data)

CASE STUDY -I

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Hydrological modeling of the basin

The ARCSWAT distributed hydrologic model has been used. The basin has been

sub-divided in to 23 sub-basins to account for the spatial variability. After mapping

the basin for terrain, land use and soil, simulated imposing the weather conditions

predicted for control and GHG climate

Control Climate Scenario

The Krishna basin has been simulated using ARCSWAT model firstly using

generated daily weather data by PRECIS control climate scenario (1960-1990)

PRECIS Climate Scenario

The model then had been run on using PRECIS climate scenarios for remaining 60

years (2011-2040) & (2041-2070) data but without changing the land use. The

outputs of these two scenarios have been made available at the sub-basins.

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Fig 2: Difference in mean monthly water balance components from control to GHG scenario

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Limitations of the Study

Future flow conditions cannot be projected exactly due to uncertainty in climate change

scenarios and GCM outputs

The uncertainties presented in each of the models and model outputs kept on cumulating

while progressing towards the final output. These Uncertainties include: Uncertainty

Linked to Data quality, General circulation Model (GCMs), Emission scenarios.

Summary

The SWAT model is well able to simulate the hydrology of the Krishna river Basin. The

future annual discharge, surface runoff and base flow in the basin show increases over

the present as a result of future climate change

General results of this analysis should be identified and incorporated into water

resources management plans in order to promote more sustainable water use in the study

area

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“An Assessment of Climate Change Impacts on Stream flows in the Musi Catchment, India “

Authors:R. Nune , B. George , H. Malano , B. Nawarathna , B. Davidson a, D. Ryu

STUDY AREA AND DATA: The Musi River, a principle tributary of the Krishna River in India has been selected for this study.

CASE STUDY II

Figure 3 Map of the study area. 

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The data required for the study were collated from various sources

including:

Climatic data were sourced from the Indian Meteorological

Department and the Indian Institute of Tropical Meteorology (IITM).

The Indian Institute for Tropical Meteorology (IITM) provided

PRECIS regional climate model outputs for the period 1960-2098

for A1B IPCC SRES scenarios (Q0, Q1 and Q14 QUMP ensemble).

Data on hydrological structures (percolation tanks, irrigation

tanks, check dams, bunds, farm ponds) collated from Rural

Development Department.

Stream flows at two locations were collated.

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METHODOLOGY

The water cycle in the Musi catchment, including surface and groundwater

resources, is driven by two main forcing variables: climate and watershed

development (land use and hydrological structures).

The objective of the hydrologic modelling is to assess the impacts of future

climate and watershed development changes on the catchment water cycle.

SWAT model:

Arc SWAT was used as the hydrological modelling tool for the Musi catchment

The SWAT model is a process-based continuous hydrological model that can be used to

assess the impacts of land use and hydrological structures on stream flows.

Data pre-processing in Arc SWAT involves three steps: watershed delineation, a

hydrological response unit (HRU) and a weather data definition.

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Assessing Impact of Climate Change

The model was calibrated and validated using historical forcing

data (daily rainfall, maximum and minimum air temperature).

These model outputs were then analysed and comparisons

were made for the periods 1980-2010, 2011-2040, 2041-2070

and 2071-2098.

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SWAT Model Calibration and Validation

RESULTS & DISCUSSION

Figure 4 Plots of monthly observed and simulated flows for the calibration period at HS

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Table 1Nash-Sutcliffe coefficient during calibration and validation phases (monthly flows)

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Assessment of Climate Change Impact on Water Resources

Figure 5 Projected annual stream flow at different time periods-Q0 scenario

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Impact of hydrologic structures

Table 2 Impact of hydrologic structures

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Results revealed that SWAT model can be used efficiently in hydrological modeling.

SWAT model works well in large mountainous watersheds and in semi-arid regions.

The hydrology of the catchment was modelled using the SWAT hydrologic model. The output from

these RCM’s was used as input for Arc SWAT hydrological model, The model then had been run on

using PRECIS climate scenarios daily weather data.

GIS based hydrological modelling has been utilized for the purpose of assessment of the total

amount of water available in the study area, as well as prediction of the impact of changes in the

land management practices on the water availability in the study area.

The utility of GIS to create combine and generate the necessary data to set up and run the

hydrological models especially for those distributed and continuous.

It also had demonstrated that the SWAT model works well in large mountainous watersheds and in

semi-arid regions.The hydrological model calibration and validation indicates that SWAT model

simulates stream flow appreciably well for the study area.

SUMMARY

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Aleix S.C, Juan B. V, Javier G.P, Kate B, Luis J.M, Thomas.M (2007), Modelling climate

change impacts and uncertainty on the hydrology of a riparian system: The San Pedro Basin

(Arizona/Sonora), Journal of Hydrology 2007, Pages 48-66

Fowler H.J, S. Blenkinsopa and C. Tebaldib (2007), Linking climate change modeling to

impacts studies recent advances in downscaling techniques for hydrological modeling Int. J.

Climatol. 27: 1547–1578.

Gupta P.K, S. Panigrahy and J.S. Parihar (2007), Impact of climate change on runoff of the

major river basins of India using Global Circulation Model (HADCM3) projected data ISPRS,

Archives XXXVIII-8/W3 Workshop Proceedings.

Kenji T, Yoichi.F, Tsugihiro.W, Takanori. N, Toshiharu.K (2008), assessing the impacts of

climate change on the water resources of the Seyhan River Basin in Turkey: Use of

dynamically downscaled data for hydrologic simulations, Journal of Hydrology, 2008, Pages,

33-48.

REFERENCES

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Kulkanri& S. D. Bansod (2012), Assessing hydrological response to changing climate in the

Krishna basin, International conference on "Opportunities and Challenges in Monsoon

Prediction in a Changing Climate" (OCHAMP-2012), Pune, India, 2012

Kulakarni B.D, N.R.Deshpande (2011), Assessing the impact climate change scenarios’ on water

recourses in bhima river basin in India,IITM

Nune.R , B. George , H. Malano , B. Nawarathna , B. Davidson , D. Ryua(2013), An Assessment

of Climate Change Impacts on Stream flows in the Musi Catchment, India 20th International

Congress on Modeling and Simulation, Adelaide, Australia, (2013),

www.mssanz.org.au/modsim2013.

Subimal.G, Misra.C (2010), Assessing Hydrological Impacts of Climate Change: Modeling

Techniques and Challenges, the Open Hydrology Journal, 2010, 4, 115-121

Xiyan.R,Luliu.L,Zhaofei.L, Thomas.F, Ying Xu (2011), Hydrological impacts of climate change

in the Yellow River Basin for the 21st century using hydrological model and statistical

downscaling model, Quaternary International, 2011, Pages 211-220

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THANK YOU