Climate change impact assessment on hydrology on river basins

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Climate change impact assessment on hydrology on river basins

  1. 1. CH.TIRUPATHI 131856 5/8/2014 1
  2. 2.  INTRODUCTION  APPLICATION OF RS&GIS IN IMPACT OF CLIMATE CHANGE  CLIMATE CHANGE MODELS  GCM,RCM  DOWN SCALING TECHNICS  LITERATURE RIEVIEW  CASE STUDIES 5/8/2014 2
  3. 3. 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 5/8/2014 3
  4. 4.  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 5/8/2014 4
  5. 5.  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 5/8/2014 5 CLIMATE CHANGE MODELS
  6. 6.  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 5/8/2014 6
  7. 7.  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 . 5/8/2014 7
  8. 8. 5/8/2014 8 Figure1.Development of Limited Area Models (LAMs) ,(RCM’s) from GCM’
  9. 9. 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 5/8/2014 9
  10. 10. 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. 5/8/2014 10
  11. 11. 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. 5/8/2014 11
  12. 12.  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. 5/8/2014 12
  13. 13. “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) 5/8/2014 13
  14. 14. 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. 5/8/2014 14
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  16. 16. 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 5/8/2014 16
  17. 17.  “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. 5/8/2014 17 Figure 3 Map of the study area.
  18. 18.  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. 5/8/2014 18
  19. 19. 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. 5/8/2014 19
  20. 20. 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. 5/8/2014 20
  21. 21. SWAT Model Calibration and Validation 5/8/2014 21 Figure 4 Plots of monthly observed and simulated flows for the calibration period at HS
  22. 22. 5/8/2014 22 Table 1Nash-Sutcliffe coefficient during calibration and validation phases (monthly flows)
  23. 23. 5/8/2014 23 Figure 5 Projected annual stream flow at different time periods-Q0 scenario
  24. 24. 5/8/2014 24 Table 2 Impact of hydrologic structures
  25. 25.  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. 5/8/2014 25
  26. 26.  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. 5/8/2014 26
  27. 27.  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 5/8/2014 27
  28. 28. THANK YOU 5/8/2014 28

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