SEARCH

SEARCH BY CITATION

References

  • Anandhi A, Srinivas VV, Kumar DN, Nanjundiah RS. 2009. Role of predictors in downscaling surface temperature to river basin in India for IPCC SRES scenarios using support vector machine. International Journal of Climatology 29: 583603.
  • Arnell NW, Hudson DA, Jones RG. 2003. Climate change scenarios from a regional climate model: Estimating change in runoff in southern Africa. Journal of Geophysical Research Atmospheres 108(D16): AR 4519.
  • Benestad RE. 2001. A comparison between two empirical downscaling strategies. International Journal of Climatology 21: 16451668.
  • Cannon AJ, Lord ER. 2000. Forecasting summertime surface-level ozone concentrations in the Lower Fraser Valley of British Columbia: An ensemble neural network approach. Journal of the Air and Waste Management Association 50: 322339.
  • Cannon AJ, Whitfield PH. 2002. Downscaling recent streamflow conditions in British Columbia, Canada using ensemble neural network models. Journal of Hydrology 259(1): 136151.
  • Carter TR, Parry ML, Harasawa H, Nishioka S. 1994. IPCC technical guidelines for assessing climate change impacts and adaptations, University College, London, United Kingdom, and Centre for Global Environmental Research, Tsukuba, Japan.
  • Cavazos T, Hewitson BC. 2005. Performance of NCEP variables in statistical downscaling of daily precipitation. Climate Research 28: 95107.
  • Cecilia Hellström, Deliang Chen, Christine Achberger, Jouni Räisänen. 2001. Comparison of climate change scenarios for Sweden based on statistical and dynamical downscaling of monthly precipitation. Climate Research 19: 4555.
  • Crane RG, Hewitson BC. 1998. Doubled CO2 precipitation changes for the Susquehanna Basin: Down-Scaling from the Genesis General Circulation Model. International Journal of Climatology 18: 6576.
  • Dibike YB, Coulibaly P. 2006. Temporal neural networks for downscaling climate variability and extremes. Neural Networks 19(2): 135144.
  • Dibike YB, Coulibaly P. 2007. “Validation of hydrologic models for climate scenario simulation: The case of Saguenay watershed in Quebec.” Hydrological Processes 21(23): 31233235.
  • Gardner MW, Dorling SR. 1998. Artificial neural networks (the multi layer perceptron)—A review of applications in the atmospheric sciences. Atmospheric Environment 32: 26272636.
  • Ghosh S, Mujumdar PP. 2006. Future rainfall scenario over Orissa with GCM projections by statistical downscaling. Current Science 90(3): 396404.
  • Ghosh S, Mujumdar PP. 2008. Statistical downscaling of GCM simulations to streamflow using relevance vector machine, Advances in Water Resources 31: 132146.
  • Goyal MK, Ojha CSP. 2010a. Robust Weighted Regression As A Downscaling Tool In Temperature Projections, International Journal of Global Warming 2(3): 234251.
  • Goyal MK, Ojha CSP. 2010b. Application of PLS-Regression as downscaling tool for Pichola lake basin in India, International Journal of Geosciences 1: 5157.
  • Goyal MK Ojha CSP. 2010c. Evaluation of Various Linear Regression Methods for Downscaling of Mean Monthly Precipitation in Arid Pichola Watershed Natural Resources 1(1): 1118.
  • Goyal MK, Ojha CSP. 2010d. Evaluation of Linear Regression Methods As Downscaling Tool in Temperature Projections Over Pichola lake Basin in India, Hydrological Processes, DOI: 10.1002/hyp.7911.
  • Hewitson BC, Crane RG. 1994. Neural nets applications in geography. Kluwer Academic Publishers: Dordrecht.
  • Hewitson BC, Crane RG. 1996. Climate downscaling: techniques and application. Climate Research 7: 8595.
  • Heyen H, Zorita E, von Storch H. 1996. Statistical downscaling of monthly mean North Atlantic air-pressure to sea level anomalies in the Baltic Sea. Tellus 48A: 312323.
  • Hughes JP, Lettenmaier DP, Guttorp P. 1993. A stochastic approach for assessing the effect of changes in synoptic circulation patterns on Gauge precipitation. Water Resources Research 29(10): 33033315.
  • Huth R. 1999. Statistical downscaling in central Europe: Evaluation of methods and potential predictors. Climate Research 13: 91101.
  • Intergovernmental Panel on Climate Change (IPCC). 2001. Climate Change 2001—The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Houghton JT, Ding Y, Griggs DJ, Noguer M, van der Linden PJ, Dai X, Maskell K, Johnson CA. (eds), Cambridge Univ. Press: Cambridge, UK.
  • Intergovernmental Panel on Climate Change (IPCC). 2007. Climate Change 2007—The Physical Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Solomon S, Qin D, Manning M, Marquis M, Averyt K, Tignor MMB, Miller HLR Jr, Chen Z, Cambridge Univ. Press: Cambridge, UK.
  • Jessie CR, Antonio RM, Stahis SP. 1996. Climate Variability, Climate Change and Social Vulnerability in the Semi-arid Tropics. Cambridge University Press: Cambridge.
  • Johnson MS, Coon WF, Mehta VK, Steenhuis TS, Brooks ES, Boll J. 2003. Application of two hydrologic models with different runoff mechanisms to a hillslope dominated watershed in the northeastern US: a comparison of HSPF and SMR. Journal of Hydrology 284: 5776.
  • Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Leetmaa A, Reynolds R, Jenne R, Joseph D. 1996. The NCEP/NCAR 40-year reanalysis project. Bulletin of the American Meteorological Society 77(3): 437471.
  • Kendall MG. 1951. Regression structure and functional relationship Part I. Biometrika 38: 1125.
  • Kettle H, Thompson R. 2004. Statistical downscaling in European mountains: verification of reconstructed air temperature. Climate Research 26(2): 97112.
  • Khobragade SD. 2009. Studies on evaporation from open water surfaces in tropical climate, PhD thesis, Indian Institute of Technology, Roorkee, India.
  • Kilsby CG, Cowpertwait PSP, O'Connell PE, Jones PD. 1998. Predicting rainfall statistics in England and Wales using atmospheric circulation variables. International Journal of Climatology 18: 523539.
  • Linz H, Shiklomanov I, Mostefakara K. 1990. Chapter 4 Hydrology and water Likely impact of climate change IPCC WGII report WMO/UNEP Geneva.
  • Mearns LO, Giorgi F, Whetton PH, Pabon D, Hulme M, Lai M. 2003. Guidelines for Use of Climate Scenarios Developed from Regional Climate Model Experiments. Data Distribution Center of the Intergovernmental Panel on Climate Change.
  • Meehl GA, Covey C, Delworth T, Latif M, McAvaney B, Mitchell JFB, Stouffer RJ, Taylor KE. 2007. The WCRP CMIP3 Multimodel data set. A new era in climate change research. Bulletin of the American Meteorological Society 88: 13831394.
  • Misson L, Rasse DP, Vincke C, Aubinet M, Francois L. 2002. Predicting transpiration from forest stands in Belgium for the 21st century. Agricultural and Forest Meteorology 111(4): 265282.
  • Nash JE, Sutcliffe JV. 1970. River flow forecasting through conceptual models. Part I—a discussion of principles. Journal of Hydrology 10: 282290.
  • Pearson K. 1896. Mathematical contributions to the theory of evolution III regression heredity and panmixia. Philosophical Transactions of the Royal Society of London Series 187: 253318.
  • Rumelhart DE, Durbin R, Golden R, Chauvin Y. 1995. Back-propagation: The basic theory. Back Propagation: Theory, Ar-chitectures, and Applications. Y. Chauvin, D. E. Rumelhart, (eds), Lawrence Earlbaum: 134.
  • Sailor DJ, Hu T, Li X, Rosen JN. 2000. A neural network approach to local downscaling of GCM output for assessing wind power implications of climate change, Renewable Energy 19: 359378.
  • Schoof JT, Pryor SC. 2001. Downscaling temperature and precipitation: a comparison of regression-based methods and artificial neural networks. International Journal of Climatology 21: 773790.
  • Shannon DA, Hewitson BC. 1996. Cross-scale relationships regarding local temperature inversions at Cape Town and global climate change implications. South African Journal of Science 92(4): 213216.
  • Spearman CE. 1904a. General intelligence objectively determined and measured. American Journal of Psychology 5: 201293.
  • Spearman CE. 1904b. Proof and measurement of association between two things. American Journal of Psychology 15: 72101.
  • Tripathi S, Srinivas VV, Nanjundiah RS. 2006. Downscaling of precipitation for climate change scenarios: a support vector machine approach. Journal of Hydrology 330(3–4): 621640.
  • Weisse R, Oestreicher R. 2001. Reconstruction of potential evaporation for water balance studies. Climate Research 16(2): 123131.
  • Wilby RL. 1998. Modelling low-frequency rainfall events using airflow indices, weather patterns and frontal frequencies. Journal of Hydrology 213(1–4): 380392.
  • Wilby RL, Dawson CW, Barrow EM. 2002. SDSM—a decision support tool for the assessment of climate change impacts. Environmental Modelling & Software 17: 147159.
  • 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. 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).
  • Willmott CJ, Rowe CM, Philpot WD. 1985. Small-scale climate map: a sensitivity analysis of some common assumptions associated with the grid-point interpolation and contouring, American Cartographer 12: 516.
  • Xopalki E, Luterbacher J, Burkard R, Patrikas I, Maheras P. 2000. Connection between the large-scale 500 hPa geopotential height fields and precipitation over Greece during wintertime. Climate Research 14: 129146.
  • Zhang B, Govindaraju RS. 2000. Prediction of watershed runoff using bayesian concepts and modular neural network. Water Resources Research 36(3): 753762.
  • Zhang XC, Nearing MA, Garbrecht JD, Steiner JL. 2004. Downscaling monthly forecasts to simulate impacts of climate change on soil erosion and wheat production. Soil Science Society of America Journal 68(4): 13761385.