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Case Studies

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Impact of Climate Change on Water Resources

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Abstract

This chapter presents various real-world global case studies in AR3 and AR5 perspective that are related to the evaluation of GCMs for maximum and minimum temperatures for India, intercomparison of statistical downscaling methods for projection of extreme precipitation in Europe, downscaling of climate variables using Support Vector Machine, Multiple Linear Regression for Malaprabha and Lower Godavari Basins, India and applicability of large-scale climate Teleconnections and Artificial Neural Networks for Regional Rainfall Forecasting for Orissa, India. In addition, the impact of climate change on semi-arid catchment water balance using an ensemble of GCMs for Malaprabha catchment, India; streamflow in four large African river basins; projection of rainfall–runoff for Murray–Hotham catchment of Western Australia; future changes in Mekong River hydrology are also parts of the chapter. The reader is expected to understand the impact studies through various case studies by studying this chapter.

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References

  • Aich V, Liersch S, Vetter T, Huang S, Tecklenburg J, Hoffmann P, Koch H, Fournet S, Krysanova V, Müller EN, Hattermann FF (2014) Comparing impacts of climate change on streamflow in four large African river basins. Hydrol Earth Syst Sci 18: 1305–1321

    Google Scholar 

  • Akshara G (2015) downscaling of climate variables using multiple linear regression-a case study lower godavari basin, India, M.E. dissertation, BITS Pilani

    Google Scholar 

  • Akshara G, Raju KS, Singh AP, Vasan A (2017) Application of multiple linear regression as downscaling methodology for lower godavari basin.In: Proceedings of International conference on water, environment, energy and society (ICWEES-2016), Springer

    Google Scholar 

  • Anandhi, A (2007) Impact assessment of climate change on hydrometeorology of Indian River Basin for IPCC SRES scenaries. Ph.D thesis, Indian Institute of Science, Bangalore.

    Google Scholar 

  • Anandhi A, Srinivas VV, Nanjundiah RS, Nagesh Kumar D (2008) downscaling precipitation to river basin in India for IPCC SRES scenarios using support vector machine. Int J Climatol Springer 28(3):401–420

    Article  Google Scholar 

  • Anandhi A, Srinivas VV, Nagesh Kumar D, Nanjundiah RS (2012) Daily relative humidity projections in an Indian river basin for IPCC SRES scenarios. Theor Appl Climatol Springer 108(1–2):85–104

    Article  Google Scholar 

  • Anandhi A, Srinivas VV, Nagesh Kumar (2013) Impact of climate change on hydrometeorological variables in a river basin in India for IPCC SRES scenarios. In: Rao YS, Zhang TC, Ojha CSP, Gurjar BR, Tyagi RD, Kao CM (eds) Chapter 12 in climate change modeling, mitigation, and adaptation. American Society of Civil Engineers, pp 327–356

    Google Scholar 

  • Armstrong R, Raup B, Khalsa SJS, Barry R, Kargel J, Helm C, Kieffer H (2005) GLIMS glacier database. National Snow and Ice Data Center, Digital Media, Boulder, Colorado USA

    Google Scholar 

  • Bari MA, Smettem KRJ (2003) Development of a salt and water balance model for a large partially cleared catchment. Aust J Water Resour 7:83–99

    Google Scholar 

  • Bari MA, Amirthanathan GE, Timbal B (2010) Climate change and long term water availability in south-western Australia—an experimental projection. In: Practical responses to climate change national conference 2010, Hilton on the Park, Melbourne, Australia, 29 September–1 October 2010

    Google Scholar 

  • Bartholomé E, Belward AS (2005) GLC2000: a new approach to global land cover mapping from earth observation data. Int J Remote Sens 29:1959–1977

    Article  Google Scholar 

  • Burn DH (1989) Cluster analysis as applied to regional flood frequency. J Water Resour Plan Manag ASCE 115:567–582

    Article  Google Scholar 

  • Carter TR, Jones PD, Hulme M, New M (2004) A comprehensive set of high521 resolution grids of monthly climate for Europe and the globe: the observed record (1901– 2000) and 16 scenarios (2001–2100). Tyndall Working Paper 55, Tyndall Centre, University of East Anglia, Norwich, United Kingdom

    Google Scholar 

  • Changnon SA Jr (1987) Climate fluctuations and record-high levels of lake Michigan. Bull Am Meteoro Soc 68(11):1394–1402

    Article  Google Scholar 

  • Chaturvedi RJ, Joshi J, Jayaraman M, Bala G, Ravindranath NH (2012) multi-model climate change projections for India under representative concentration pathways. Curr Sci 103:7–10

    Google Scholar 

  • Christensen NS, Lettenmaier DP (2007) A multimodel ensemble approach to assessment of climate change impacts on the hydrology and water resources of the Colorado River Basin. Hydrol Earth Syst Sci 11:1417–1434. doi:10.5194/hess-11-1417-2007

    Article  Google Scholar 

  • Commonwealth Scientific and Industrial Research Organisation (CSIRO) (2009) Surface water yields in south-west Western Australia, a report to the Australian government from the CSIRO southwest Western Australia sustainable yields project. CSIRO water for a healthy country flagship, Commonwealth Scientific and Industrial Research Organisation, Australia, 171 p

    Google Scholar 

  • Danish Meteorological Institute (DMI) (2012) Climate Grid Denmark. Dataset for use in research and education. Daily and monthly values1989–2010 10 × 10 km observed precipitation 20 × 20 km temperature, potential evaporation (Makkink), wind speed, global radiation, Technical Report 12–10. http://beta.dmi.dk/fileadmin/Rapporter/TR/tr12-10.pdf. Accessed 3 June 2014

  • Dantzig GB, Thapa MN (1997) Linear programming 1: Introduction. Springer

    Google Scholar 

  • Davies DL, Bouldin DW (1979) A Cluster Separation Measure. IEEE Trans Pattern Anal Mach Intell 1(2):224–227

    Article  Google Scholar 

  • Department of Water (DoW) (2008) Water solutions, winter’08, Perth, Western Australia, 9 p

    Google Scholar 

  • Déqué M, Rowell DP, Lüthi D, Giorgi F, Christensen JH, Rockel B, Jacob D, Kjellström E, Castro M, van den Hurk B (2007) An intercomparison of regional climate simulations for Europe: assessing uncertainties in model projections. Clim Change 81:53–70. doi:10.1007/s10584-006-9228-x

    Article  Google Scholar 

  • Déqué M, Somot S, Sanchez-Gomez E, Goodess CM, Jacob D, Lenderink G, Christensen OB (2012) The spread amongst ENSEMBLES regional scenarios: regional climate models, driving general circulation models and interannual variability. Clim Dynam 38:951–964. doi:10.1007/s00382-011-1053-x

    Article  Google Scholar 

  • Doty B, Kinter JI (1993) The grid analysis and display system (GrADS): a desktop tool for earth science visualization. In: Proceedings of American Geophysical Union 1993 Fall Meeting, San Fransico, CA, 6–10 December, 1993

    Google Scholar 

  • Dutta S, Patel NK, Medhavy TT, Srivastava SK, Mishra N, Singh KRP (1998) Wheat crop classification using multidate IRS LISS-I data. J Indian Soc Remote Sens 26(1–2):7–14

    Article  Google Scholar 

  • Errasti I, Ezcurra A, Sáenz J, Ibarra-Berastegi G (2010) Validation of IPCC AR4 models over the Iberian peninsula. Theor Appl Climatol 103:61–79

    Article  Google Scholar 

  • FAO, IIASA, ISRIC, ISSC and JRC (2012) Harmonized World Soil Database v 1.2. http://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/. Accessed 26 June 2013

  • Fekete BM, Vorosmarty CJ, Grabs W (1999) Global, composite runoff fields based on observed river discharge and simulated water balances, GRDC Report 22. Global Runoff Data Center, Koblenz, Germany

    Google Scholar 

  • Fowler HJ, Ekström M (2009) Multi-model ensemble estimates of climate change impacts on UK seasonal precipitation extremes. Int J Climatol 29:385–416. doi:10.1002/joc.1827

    Article  Google Scholar 

  • Fowler HJ, Blenkinsop S, Tebaldi C (2007) Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. Int J Climatol 27(12):1547–1578

    Article  Google Scholar 

  • Frei C, Schöll R, Fukutome S, Schmidli J, Vidale PL (2006) Future change of precipitation extremes in Europe: intercomparison of scenarios from regional climate models. J Geophys Res 111:D06105. doi:10.1029/2005JD005965

    Article  Google Scholar 

  • Gestel T, Suykens J, Baesens B, Viaene S, Vanthienen J, Dedene G, Moor B, Vandewalle J (2004) Benchmarking least squares support vector machine classifiers. Mach Learn 54(1):5–32

    Article  MATH  Google Scholar 

  • Godavari River basin (2017) India WRIS—water resources information system of India. http://india-wris.nrsc.gov.in/wris.html. Last Accessed 31 Jan 2017

  • Gosain A, Sandhya R, Debajit B (2006) Climate change impact assessment on hydrology of indian river basins, in special issue on climate change and India. Curr Sci 90(3):346–353

    Google Scholar 

  • Haupt R, Haupt S (2004) Practical genetic algorithms. Wiley, New Jersey, p 253

    MATH  Google Scholar 

  • Haykin S (2003) Neural networks: a comprehensive foundation. Pearson Education, Singapore

    MATH  Google Scholar 

  • Hempel S, Frieler K,Warszawski L, Schewe J, Piontek F (2013) A trend-preserving bias correction—the ISI-MIP approach. Earth Syst Dynam 4, 219–236, doi:10.5194/esd-4–219-5-2013.

  • Hofstra N, Haylock M, New M, Jones PD (2009) Testing EOBS European high-resolution gridded data set of daily precipitation and surface temperature. J Geophys Res 114:D21101. doi:10.1029/2009JD011799

    Article  Google Scholar 

  • Indian Institute of Tropical Meteorology (2017). http://www.tropmet.res.in/. Accessed 31 Jan 2017)

  • Islam SA, Bari M, Anwar AHMF (2011) Assessment of hydrologic impact of climate change on Ord river catchment of Western Australia for water resources planning: a multi-model ensemble approach. In: Proceedings of the 19th international congress on modelling and simulation, Perth, Western Australia, 12–16 December 2011, pp. 3587–3593

    Google Scholar 

  • Islam SA, Bari MA, Anwar AHMF (2014) Hydrologic impact of climate change on Murray-Hotham catchment of Western Australia: a projection of rainfall–runoff for future water resources planning. Hydrol Earth Syst Sci 18:3591–3614

    Article  Google Scholar 

  • Jarvis A, ReuterHI, Nelson A, Guevara E (2008) Hole-filled SRTM for the globe Version 4. http://srtm.csi.cgiar.org/ Accessed 2 Mar 2013

  • Johnson F, Sharma A (2009) Measurement of GCM Skill in predicting variables relevant for hydroclimatological assessments. J Clim 22:4373–4382

    Article  Google Scholar 

  • Johnson F, Sharma A (2012) A nesting model for bias correction of variability at multiple time scales in general circulation model precipitation simulations. Water Resour Res 48:W01504

    Google Scholar 

  • Jones DA, Wang W, Fawcett R (2009) High-quality spatial climate data-sets for Australia. Aust Meteorol Oceanogr J 58:233–248

    Article  Google Scholar 

  • Karl T, Wang W, Schlesinger M, Knight R, Portman D (1990) A method of relating general circulation model simulated climate to the observed local climate part i: seasonal statistics. J Clim 3:1053–1079

    Article  Google Scholar 

  • Kendall MG (1951) Regression, structure and functional relationship. Part I, Biometrika 38:11–25

    Article  MathSciNet  MATH  Google Scholar 

  • Koponen J, Lauri H, Veijalainen N, Sarkkula J (2010) HBV and IWRM watershed modelling user guide, MRC information and knowledge management programme, DMS—Detailed modelling support for the MRC project. http://www.eia.fi/index.php/support/download.

  • Köppen W (1900) Versuch einer Klassifikation der Klimate, vorzugsweise nach ihren Beziehungen zur Pflanzenwelt, Geogr. Zeitschrift, 6, 593–611

    Google Scholar 

  • Krysanova V, Müller-Wohlfeil D-I, Becker A (1998) Development and test of a spatially distributed hydrological/water quality model for mesoscale watersheds. Ecol Model 106:261–289

    Article  Google Scholar 

  • Krysanova V, Hattermann F, Wechsung F (2005) Development of the ecohydrological model SWIM for regional impact studies and vulnerability assessment. Hydrol Process 19:763–783

    Article  Google Scholar 

  • Lauri H, de Moel H, Ward PJ, Rasanen TA, Keskinen M, Kummu M (2012) Future changes in Mekong river hydrology: impact of climate change and reservoir operation on discharge. Hydrol Earth Syst Sci 16:4603–4619

    Article  Google Scholar 

  • Lenderink G (2010) Exploring metrics of extreme daily precipitation in a large ensemble of regional climate model simulations. Clim Res 44:151–166. doi:10.3354/cr00946

    Article  Google Scholar 

  • Lillesand TM, Kiefer RW, Chipman JW (2004) Remote sensing and image interpretation. Wiley India (P). Ltd, New Delhi

    Google Scholar 

  • MacQueen J (ed) (1967) Some methods for classification and analysis of multivariate observation. In: Proceedings of the fifth berkeley symposium on mathematical statistics and probability. University of California Press, Berkeley, pp 281–297

    Google Scholar 

  • Maity R, Nagesh Kumar D (2006) Bayesian dynamic modeling for monthly Indian summer monsoon rainfall using El Niño southern oscillation (ENSO) and equatorial Indian ocean oscillation (EQUINOO). J Geophys Res 111:D07104

    Article  Google Scholar 

  • Mann HB, Whitney DR (1947) On a test of whether one of two random variables is stochastically larger than the other. Ann Math Stat 18:50–60

    Article  MathSciNet  MATH  Google Scholar 

  • Mayer XM, Ruprecht JK, Bari MA (2005) Stream salinity status and trends in south west Western Australia, salinity and land use impacts series, vol 38, Department of Environment, Perth, Western Australia, 188

    Google Scholar 

  • Mehrotra R, Sharma A (2010) Development and application of a multisite rainfall stochastic downscaling framework for climate change impact assessment. Water Resour Res 46 (W07526). doi:10.1029/2009WR008423

  • Mehrotra R, Sharma A, Nagesh Kumar D, Reshmidevi TV (2013) Assessing future rainfall projections using multiple GCMsand A multi-site stochastic downscaling model. J Hydrol 488:84–100

    Article  Google Scholar 

  • Mekong River Commission (2005) Overview of the hydrology of the Mekong Basin, Mekong River Commission, Vientiane, 82

    Google Scholar 

  • Mekong River Commission (2011) Hydrometeorological database of the Mekong River Commission, Mekong River Commission (MRC). Vientiane, Lao PDR

    Google Scholar 

  • Mitchell TD, Jones PD (2005) An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int J Climatol 25:693–712

    Article  Google Scholar 

  • Morais DC, Almeida AT (2012) Group decision making on water resources based on analysis of individual rankings. Omega 40:42–52

    Article  Google Scholar 

  • Moriasi DN, Arnold JG, VanLiew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50(3):885–900

    Article  Google Scholar 

  • Mujumdar PP, Nagesh Kumar D (2012) Floods in a changing climate: hydrologic modeling, international hydrology series. Cambridge University Press, UK

    Book  Google Scholar 

  • Nagesh Kumar D, Janga Reddy M, Maity R (2007) Regional rainfall forecasting using large scale climate teleconnections and artificial intelligence techniques. J Intell Syst 16:307–322

    Google Scholar 

  • Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol 10:282–290

    Article  Google Scholar 

  • NCDC (2010) Global surface summary of the day (GSOD), US National Climatic Data Center (NCDC)

    Google Scholar 

  • NCEP (2017). www.cpc.ncep.noaa.gov/. Accessed 31 Jan 2017

  • NOAA (2011) NCEP-DOE Reanalysis 2, National Oceanic and Athmospheric Admistration, Earth System Research Laboratory, Physical Science Division. http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis2.html.

  • Pearson K (1896) mathematical contributions to the theory of evolution. III. Regres Heredity Panmixia Philos Trans Roy Soc A 187:253–318

    Article  MATH  Google Scholar 

  • Pen LJ, Hutchison J (1999) Managing our rivers: a guide to the nature and management of the streams of south-west Western Australia, water and rivers commission, 382 p

    Google Scholar 

  • Pennell C, Reichler T (2011) On the Effective Number of Climate Models. J Clim 24:2358–2367

    Article  Google Scholar 

  • Raju KS, Nagesh Kumar D (2014) Multicriterion analysis in engineering and management. Prentice Hall of India, New Delhi

    Google Scholar 

  • Raju KS, Nagesh Kumar D (2016) Selection of global climate models for india using cluster analysis. J Water Clim Change 7(4):764–774. doi:10.2166/wcc.2016.112

    Article  Google Scholar 

  • Raju KS, Sonali P, Nagesh Kumar D (2017) Ranking of CMIP5 based global climate models for india using compromise programming. Theor Appl Climatol Springer 128(3):563–574. doi:10.1007/s00704-015-1721-6

    Article  Google Scholar 

  • Reichler T, Kim J (2008) How Well Do Coupled Models Simulate Today’s Climate? Bull Am Meteorol Soc 89:303–311

    Article  Google Scholar 

  • Reshmidevi TV, Nagesh Kumar D (2014) Modelling the impact of extensive irrigation on the groundwater resources. Hydrol Process 28(3):628–639

    Article  Google Scholar 

  • Reshmidevi TV, Nagesh Kumar D, Mehrotra R, Sharma A (2017) Estimation of the climate change impact on a catchment water balance using an ensemble of GCMs. J Hydrol. doi:10.1016/j.jhydrol.2017.02.016

    Google Scholar 

  • Sahai AK, Soman MK, Satyan V (2000) All India summer monsoon rainfall prediction using an artificial neural network. Clim Dyn 16:291–302

    Article  Google Scholar 

  • Sahai AK, Grimm AM, Satyan V, Pant GB (2003) Long-lead Prediction of Indian Summer Monsoon Rainfall from Global SST Evolution. Clim Dyn 20:855–863

    Article  Google Scholar 

  • Schaefli B, Gupta HV (2007) Do nash values have value? Hydrol Process 21(15):2075–2080

    Article  Google Scholar 

  • Šercl P (2008) Assessment of methods for area precipitation estimates, Meteorological Bulletin, vol 61

    Google Scholar 

  • Smola A, Scholkopf B, Muller K (1998) The connection between regularization operators and support vector kernels. Neural Netw 11(4):637–649

    Article  Google Scholar 

  • Sousa A, García-Murillo P, Morales J, García-Barrón L (2009) Anthropogenic and natural effects on the Coastal Lagoons in the Southwest of Spain (Doñana National Park). ICES J Mar Sci 66:1508–1514

    Article  Google Scholar 

  • Spearman C (1904a) General intelligence’ objectively determined and measured. Am J Psychol 5:201–293

    Article  Google Scholar 

  • Spearman C (1904b) Proof and measurement of association between two things. Am J Psychol 15:72–101

    Article  Google Scholar 

  • Srivastava AK, Rajeevan M, Kshirsagar SR (2009) Development of a high resolution daily gridded temperature data set (1969–2005) for the Indian region. Atmos Sci Lett 10:249–254

    Google Scholar 

  • Stern H, Hoedt GD, Ernst J (2000) Objective classification of Australian climates. Aust Meteorol Magaz 49:87–96

    Google Scholar 

  • Strahler A (2013) Introducing physical geography. Wiley, 664 p

    Google Scholar 

  • Sugawara M (1979) Automatic calibration of the tank model. Hydrol Sci Bull 24:375–388

    Article  Google Scholar 

  • Sunyer MA, Hundecha Y, Lawrence D, Madsen H, Willems P, Martinkova M, Vormoor K, Bürger G, Hanel M, Kriauciuniene J, Loukas A, Osuch M, Yücel I (2015) Inter-comparison of statistical downscaling methods for projectionof extreme precipitation in Europe. Hydrol Earth Syst Sci 19:1827–1847

    Article  Google Scholar 

  • Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteor Soc 93:485–498

    Article  Google Scholar 

  • Timbal B, Fernandez E, Li Z (2009) Generalization of a statistical downscaling model to provide local climate change projections for Australia. Environ Model Softw 24:341–358. doi:10.1016/j.envsoft.2008.07.007

    Article  Google Scholar 

  • Tripathi S, Srinivas VV, Nanjundiah RS (2006) Downscaling of precipitation for climate change scenarios: a support vector machine approach. J Hydrol 330:621–640

    Article  Google Scholar 

  • Tveito OE, Bjørdal I, Skjelvåg AO, Aune B (2005) A GIS-based agro-ecological decision system based on gridded climatology. Meteorol Appl 12:57–68

    Article  Google Scholar 

  • van der Linden P, Mitchell JFB (eds) (2009) ENSEMBLES: climate change and its impacts: summary of research and results from the ENSEMBLES project. Technical report. http://ensembles-eu.metoffice.com/docs/Ensembles_final_report_Nov09.pdf. Accessed 3 June 2014. Met Office Hadley Centre, UK, 160 p

  • van Griensven A (2005) Sensitivity, auto-calibration, uncertainty and model evaluation in SWAT2005 (Unpublished report)

    Google Scholar 

  • Wang K (2007) Cluster validation toolbox MATLAB central, file exchange, Math works. http://www.mathworks.com/matlabcentral/fileexchange/14620. Last Accessed 31 Jan 2017

  • Wang K, Wang B, Peng L (2009) CVAP: validation for cluster for analyses. Data Sci J 8(20):88–93

    Article  Google Scholar 

  • Weedon GP, Gomes S, Viterbo P, Shuttleworth WJ, Blyth E, Österle H, Adam JC, Bellouin N, Boucher O, Best M (2011) Creation of the WATCH forcing data and its use to assess global and regional reference crop evaporation over land during the twentieth century. J. Hydrometeorol 12:823–848

    Article  Google Scholar 

  • Westerberg I, Guerrero JL, Seibert J, Beven KJ, Halldin S (2011) Stage-discharge uncertainty derived with a non-stationary rating curve in The Choluteca river. Honduras Hydrol Process 25:603–613

    Article  Google Scholar 

  • WFD (2011) EU WATCH—Home. http://www.eu-watch.org/templates/dispatcher.asp?page_id=25222705. Accessed 7 Feb 2013

  • Wilby RL, Charles SP, Zorita E, Timbal B, Whetton P, Mearns LO (2004) The guidelines for use of climate scenarios developed from statistical downscaling methods. In: Supporting material of the intergovernmental panel on climate change (IPCC), prepared on behalf of task group on data and scenario support for impacts and climate analysis (TGICA). www.ipcc-data.org/guidelines/dgm_no2_v1_09_2004.pdf. Accessed 31 0 Jan 2017

  • Wilcoxon F (1945) Individual comparisons by ranking methods. Biometrics 1:80–83

    Article  MathSciNet  Google Scholar 

  • Winkler J, Palutikof J, Andresen J, Goodess C (1997) The simulation of daily temperature time series from GCM output part ii: sensitivity analysis of an empirical transfer function methodology. J Clim 10(10):2514–2532

    Article  Google Scholar 

  • Yu PS, Yang TC (2000) Using synthetic flow duration curves for rainfall-runoff model calibration at ungauged Sites. Hydrol Process 14:117–133

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Civil Engineering, Birla Institute of Technology and Science-Pilani, Hyderabad, Telangana, India

    Komaragiri Srinivasa Raju

  2. Department of Civil Engineering, Centre for Earth Sciences, Divecha Centre for Climate Change, Interdisciplinary Centre for Water Research, Indian Institute of Science, Bangalore, Karnataka, India

    Dasika Nagesh Kumar

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  1. Komaragiri Srinivasa Raju
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Correspondence to Komaragiri Srinivasa Raju .

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Srinivasa Raju, K., Nagesh Kumar, D. (2018). Case Studies. In: Impact of Climate Change on Water Resources . Springer Climate. Springer, Singapore. https://doi.org/10.1007/978-981-10-6110-3_6

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