Climate change impact assessment on hydrology on river basins
APPLICATION OF RS&GIS IN IMPACT OF
CLIMATE CHANGE MODELS
DOWN SCALING TECHNICS
CLIMATE means “ average 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
IMPACTS OF CLIMATE CHANGE
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
Application of a distributed hydrologic model Arc SWAT along with GIS
and remote sensing techniques
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
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
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 .
Figure1.Development of Limited Area Models (LAMs) ,(RCM’s)
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).
Analysis of hydrologic extremes
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 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.
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.
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
“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
(1) Digital Elevation Model (DEM)
( 2) Soil Data Layer
(3) Land Use/ Land Cover layer
Weather Data (Climate Model Data)
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.
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.
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
“An Assessment of Climate Change Impacts on Stream flows in the Musi Catchment,
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.
Figure 3 Map of the study area.
The data required for the study were collated from various sources
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
Stream flows at two locations were collated.
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.
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.
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
SWAT Model Calibration and Validation
Figure 4 Plots of monthly observed and simulated flows for the
calibration period at HS
Table 1Nash-Sutcliffe coefficient during calibration and validation
phases (monthly flows)
Figure 5 Projected annual stream flow at different time periods-Q0
Table 2 Impact of hydrologic structures
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.
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,
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,
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