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Wet and dry spell analysis of Global Climate Model-generated precipitation using power laws and wavelet transforms

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Abstract

Climate model simulations for the twenty-first century point toward changing characteristics of precipitation. This paper investigates the impact of climate change on precipitation in the Kansabati River basin in India. A downscaling method, based on Bayesian Neural Network (BNN), is applied to project precipitation generated from six Global Climate Models (GCMs) using two scenarios (A2 and B2). Wet and dry spell properties of monthly precipitation series at five meteorologic stations in the Kansabati basin are examined by plotting successive wet and dry durations (in months) against their number of occurrences on a double-logarithmic paper. Straight-line relationships on such graphs show that power laws govern the pattern of successive persistent wet and dry monthly spells. Comparison of power-law behaviors provides useful interpretation about the temporal precipitation pattern. The impact of low-frequency precipitation variability on the characteristics of wet and dry spells is also evaluated using continuous wavelet transforms. It is found that inter-annual cycles play an important role in the formation of wet and dry spells.

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References

  • Bàrdossy A, Stehlìk J, Caspary HJ (2002) Automated objective classification of daily circulation patterns for precipitation and temperature downscaling based on optimized fuzzy rules. Clim Res 23:11–22

    Article  Google Scholar 

  • Bàrdossy A, Bogardi I, Matyasovszky I (2005) Fuzzy rule-based downscaling of precipitation. Theor Appl Climatol 82:119–129

    Article  Google Scholar 

  • Benestad RE (2004) Empirical-statistical downscaling in climate modeling. EOS 85(42):417

    Google Scholar 

  • Bishop C (1995) Neural network for pattern recognition. Oxford University Press, New York

    Google Scholar 

  • Cannon AJ, Whitfield PH (2002) Downscaling recent streamflow conditions in British Columbia, Canada using ensemble neural network models. J Hydrol 259:136–151

    Article  Google Scholar 

  • Coulibaly P, Dibike YB (2005) Downscaling precipitation and temperature with temporal neural networks. J Hydrometeorol 6:483–495

    Article  Google Scholar 

  • Dhar S, Majumdar A (2009) Hydrological modelling of the Kangsabati River under changed climate scenario: case study in India. Hydrol Process 23:2394–2406

    Article  Google Scholar 

  • Dibike YB, Coulibaly P (2006) Temporal neural networks for downscaling climate variability and extremes. Neural Netw 19:135–144

    Article  Google Scholar 

  • Farge M (1992) Wavelet transforms and their applications to turbulence. Annu Rev Fluid Mech 24:395–457

    Article  Google Scholar 

  • Fowler HJ, Kilsby CG, O’Connell PE (2000) A stochastic rainfall model for the assessment of regional water resource systems under changed climatic conditions. Hydrol Earth Syst Sci 4:261–280

    Article  Google Scholar 

  • Fowler HJ, Kilsby CG, O’Connell PE, Burton A (2005) A weathertype conditioned multi-site stochastic rainfall model for generation of scenarios of climatic variability and change. J Hydrol 3081(4):50–66

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Fritz SC (1996) Paleolimnological records of climate change in North America. Limnol Oceanogr 41(5):882–889

    Article  CAS  Google Scholar 

  • Goodess CM, Palutikof J (1998) Development of daily rainfall scenarios for southeast Spain using a circulation-type approach to downscaling. Int J Climatol 18:1051–1083

    Article  Google Scholar 

  • Grinsted A, Moore JC, Jevrejeva S (2004) Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Proc Geophys 11:561–566

    Article  Google Scholar 

  • Hughes JP, Guttorp P (1994) A class of stochastic models for relating synoptic atmospheric patterns to regional hydrologic phenomena. Water Resour Res 305:1535–1546

    Article  Google Scholar 

  • Huth R (1999) Statistical downscaling in central Europe: evaluation of methods and potential predictors. Clim Res 13:91–101

    Article  Google Scholar 

  • Huth R (2004) Sensitivity of local daily temperature change estimates to the selection of downscaling models and predictors. J Clim 17:640–652

    Article  Google Scholar 

  • Ines AVM, Hansen JW (2006) Bias correction of daily GCM rainfall for crop simulation studies. Agric For Meteorol 138:44–53

    Article  Google Scholar 

  • IPCC (2000) IPCC special report on emission scenarios, Working group III of the Intergovernmental Panel on Climate Change. Cambridge University Press: Cambridge, 599 pp, ISBN: 92-9169-113-5

  • IPCC (2008) Climate change and water, Intergovernmental Panel on Climate Change. Technical Paper VI

  • Kadioglu M, Sen Z (1998) Power–law relationship in describing temporal and spatial precipitation pattern in Turkey. Theor Appl Climatol 59:93–106

    Article  Google Scholar 

  • Keller CF (2009) Global warming: a review of this mostly settled issue. Stoch Environ Res Risk Assess 23:643–676

    Article  Google Scholar 

  • Khan MS, Coulibaly P (2006) Bayesian neural network for rainfall-runoff modeling. Water Resour Res 42:W07409. doi:10.1029/2005WR003971

    Article  Google Scholar 

  • MacKay DJC (1992a) The evidence framework applied to classification networks. Neural Comput 45:720–736

    Article  Google Scholar 

  • MacKay DJC (1992b) Bayesian methods for adaptive models. PhD thesis, Computation and Neural Systems, California Institute of Technology, Pasadena, CA

  • MacKay DJC (2003) Information theory, inference, and learning algorithms. Cambridge University Press, Cambridge

    Google Scholar 

  • Mishra AK, Singh VP (2009) Analysis of drought severity-area-frequency curves using a general circulation model and scenario uncertainty. J Geophys Res 114:D06120. doi:10.1029/2008JD010986

    Article  Google Scholar 

  • Mishra AK, Desai VR, Singh VP (2007) Drought forecasting using a hybrid stochastic and neural network model. J Hydrol Eng 12(6):626–638

    Article  Google Scholar 

  • Mishra AK, Ozger M, Singh VP (2009) Trend and persistence of precipitation under climate change scenarios for Kansabati basin, India. Hydrol Process 23:2345–2357

    Article  Google Scholar 

  • Nabney IT (2004) Netlab algorithms for pattern recognition. Springer, New York

    Google Scholar 

  • Neal RM (1996) Bayesian learning for neural networks, Lecture notes in statistics, vol 118. Springer, New York

    Google Scholar 

  • Prudhomme C, Jakob D, Svensson C (2003) Uncertainty and climate change impact on the flood regime of small UK catchments. J Hydrol 277:1–23

    Article  Google Scholar 

  • Schubert S (1998) Downscaling local extreme temperature changes in south-eastern Australia from the CSIRO Mark2 GCM. Int J Climatol 18:1419–1438

    Article  Google Scholar 

  • Sen Z, Altunkaynak A, Ozger M (2003) Autorun persistence of hydrologic design. J Hydrol Eng 8:329–338

    Article  Google Scholar 

  • Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(379–423):623–656

    Google Scholar 

  • Singh VP (1997) The use of entropy in hydrology and water resources. Hydrol Process 11:587–626

    Article  Google Scholar 

  • Suhaila J, Jemain AA (2009) Investigating the impacts of adjoining wet days on the distribution of daily rainfall amounts in Peninsular Malaysia. J Hydrol 368(1–4):17–25

    Article  Google Scholar 

  • Tito EH, Zaverucha G, Vellasco MMBR, Pacheco M (1999) Applying Bayesian neural networks to electrical load forecasting. In: Proceedings of the sixth international conference on neural information processing, ICONIP’99, Perth, Australia

  • Torrence C, Compo GP (1998) A practical guide to wavelet analysis. Bull Am Meteorol Soc 79:61–78

    Article  Google Scholar 

  • Torrence C, Webster PJ (1999) Interdecadal changes in the ENSOmonsoon system. J Clim 12:2679–2690

    Article  Google Scholar 

  • Unal NE, Aksoy H, Akar T (2004) Annual and monthly rainfall data generation schemes. Stoch Environ Res Risk Assess 18:245–257

    Article  Google Scholar 

  • von Storch H, Zwiers F (1999) Statistical analysis in climate research. Cambridge University Press, Cambridge

    Google Scholar 

  • Wilby RL, Wigley TML (1997) Downscaling general circulation model output: a review of methods and limitations. Prog Phys Geogr 21:530–548

    Article  Google Scholar 

  • Wilby RL, Wigley TML, Conway D, Jones PD, Hewitson BC, Main J, Wilks DS (1998) Statistical downscaling of general circulation model output: a comparison of methods. Water Resour Res 34:2995–3008

    Article  Google Scholar 

  • Wilby RL, Hay LE, Leavesly GH (1999) A comparison of downscaled and raw GCM output: implications for climate change scenarios in the San Juan river basin, Colorado. J Hydrol 225:67–91

    Article  Google Scholar 

  • Wood AW, Maurer EP, Kumar A, Lettenmaier DP (2002) Long-range experimental hydrologic forecasting for the eastern United States. J Geophys Res 107(D20):4429

    Article  Google Scholar 

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Acknowledgment

The authors wish to thank Bellie Sivakumar and two reviewers for their useful suggestions that helped to improve the quality of the manuscript.

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Authors and Affiliations

  1. Department of Biological and Agricultural Engineering and Civil Engineering, Texas A&M University, College Station, TX, 77843, USA

    Ashok K. Mishra, Mehmet Özger & Vijay P. Singh

  2. Hydraulics Division, Civil Engineering Department, Civil Engineering Faculty, Istanbul Technical University, 34469, Istanbul, Turkey

    Mehmet Özger

Authors
  1. Ashok K. Mishra
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  2. Mehmet Özger
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  3. Vijay P. Singh
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Correspondence to Ashok K. Mishra.

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Mishra, A.K., Özger, M. & Singh, V.P. Wet and dry spell analysis of Global Climate Model-generated precipitation using power laws and wavelet transforms. Stoch Environ Res Risk Assess 25, 517–535 (2011). https://doi.org/10.1007/s00477-010-0419-7

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