GIS-Based Drought Assessment in Climate Change Context: A Case Study for Sone Command, Bihar

Abstract

Indian economy by far is primarily agro-based which in turn depends on the monsoon showers. Seasonal variations in monsoon lead to dry and wet spells. Inadequacy in rainfall for different needs leads to drought, and drought indicators are generally used to describe different drought conditions. In this paper, an attempt has been made to assess meteorological drought in the climate change context in Sone Command, Bihar. Three different statistical downscaling techniques, namely Wavelet decomposed neural network (WNN), feed-forward neural network and support vector machine, are used, and their performance is compared in this study. Rainfall is projected for eight raingauge stations for the period 2015–2045. The model performance is evaluated using different metrics, and the outperformance of WNN is found in projecting the rainfall variability. Standardized Precipitation Index (SPI) is calculated using the best performed model rainfall for future conditions. An attempt has also been made for the spatial drought analysis during the projected period of 2015–2045. It is found that stress on drought is prevalent during November–May for the projected period. The areas covered under different drought zones in the Sone Command ranges from near normal to severely dry. For rain-fed crops, such spatial distribution maps are useful for better crop yield with minimum chances of crop water stress. In addition, the rainfall data are projected for the period 2015–2045 from two selected GCMs: MPI-ESM-MR (for RCP 2.6 scenario) and CMCC-CMS (for RCP 4.5 and RCP 8.5 scenarios) using bilinear interpolation method of downscaling. The SPI is calculated for the projected rainfall data, and the results are compared with that of the best performed statistical downscaling model. The results reveal the occurrence of drought of higher severity in the projected period.

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References

  1. 1.

    Intergovernmental Panel on Climate Change, The Scientific Basis. Contribution of WGI to the Third Assessment Report of the Intergovernmental Panel on Climate Change, Vol. 881 (Cambridge University Press, Cambridge, 2001)

  2. 2.

    M. Svoboda, The drought monitor. Bull. Am. Meteorol. Soc. 83, 1181–1190 (2002)

    Article  Google Scholar 

  3. 3.

    D.A. Wilhite, Preparing for drought: a methodology, in drought: a global assessment. Routledge Hazards Disaster Ser. 2, 89–104 (2000)

    Google Scholar 

  4. 4.

    O.C. Penalba, J.A. Rivera, Future changes in drought characteristics over southern South America projected by a CMIP5 multi-model ensemble. Am. J. Clim. Change 2, 173–182 (2013)

    Article  Google Scholar 

  5. 5.

    J. Drisya, K.D. Sathish, T. Roshni, in Integrating Disaster Science and Management, Global Case Studies in Mitigation and Recovery, eds. by S. Pijush, K. Dookie, G. Chandan (2018), pp. 451–460

  6. 6.

    T.B. McKee, N.J. Doesken, J. Kleist, The relationship of drought frequency and duration to time Scales, in Eighth Conference on Applications and Climate, California (1993), pp. 179–184

  7. 7.

    D.S. Pai, L. Sridhar, P. Guhathakurta, H.R. Hatwar, District-wide drought climatology of the southwest monsoon season over India based on standardized precipitation index (SPI). Nat. Hazards 59, 1797–1813 (2011)

    Article  Google Scholar 

  8. 8.

    U.S. Saikia, K. Manoranjan, Standardized Precipitation Index (SPI): an effective drought monitoring tool, in 5th SERC School on Agricultural Drought—Climate Change and Rainfed Agriculture (2011), pp. 1–10

  9. 9.

    G. Leng, Q. Tang, S. Rayburg, Climate change impacts on meteorological, agricultural and hydrological droughts in China. Glob. Plan. Change 126, 23–34 (2015)

    Article  Google Scholar 

  10. 10.

    J. Hughes, K. Petrone, R. Silberstein, Drought, groundwater storage and stream flow decline in southwestern Australia. Geophys. Res. Lett. 39(3), 1–6 (2012)

    Article  Google Scholar 

  11. 11.

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

    Article  Google Scholar 

  12. 12.

    R.H. Moss, The next generation of scenarios for climate change research and assessment. Nature 463(7282), 747–756 (2010)

    Article  Google Scholar 

  13. 13.

    K. Ahmed, S. Shahid, S. Harun, T. Ismail, N. Nawaz, S. Shamsudin, Assessment of groundwater potential zones in an arid region based on catastrophe theory. Earth Sci. Inf. 8, 539–549 (2015)

    Article  Google Scholar 

  14. 14.

    D.A. Sachindra, F. Huang, A. Barton, B.J.C. Perera, Statistical downscaling of general circulation model outputs to precipitation-part 1: calibration and validation. Int. J. Climatol. 34, 3264–3281 (2014)

    Article  Google Scholar 

  15. 15.

    C. Mass, D. Ovens, M. Albright, K. Westrick, Does increasing horizontal resolution produce more skillful forecasts? Bull. Am. Meteorol. Soc. 83, 407–430 (2002)

    Article  Google Scholar 

  16. 16.

    B. Rockel, The regional downscaling approach: a brief history and recent advances. Curr. Clim. Change Rep. 1(1), 22–29 (2015)

    Article  Google Scholar 

  17. 17.

    J.M. Gregory, J.F.B. Mitchell, A.J. Brady, Summer drought in Northern Midlatitudes in a time-dependent CO2 climate experiment. J. Clim. 10, 662–686 (1997)

    Article  Google Scholar 

  18. 18.

    R.L. Wilby, T.M.L. Wigley, Precipitation predictors for downscaling: observed and general circulation model relationship. Int. J. Climatol. 20, 641–661 (2000)

    Article  Google Scholar 

  19. 19.

    P. Coulibaly, F. Anctil, R. Aravena, B. Bobée, Artificial neural network modeling of water table depth fluctuations. Water Resour. Res. 37, 885–896 (2001)

    Article  Google Scholar 

  20. 20.

    V. Nourani, S. Mousavi, Spatiotemporal groundwater level modeling using hybrid artificial intelligence-meshless method. J. Hydrol. 536, 10–25 (2016)

    Article  Google Scholar 

  21. 21.

    S. Moghim, R.L. Bras, Bias correction of climate modeled temperature and precipitation using artificial neural network. J. Hydrometeorol. 18, 1867–1884 (2017)

    Article  Google Scholar 

  22. 22.

    E.G. Bi, P. Gachon, M. Vrac, F. Monette, Which downscaled rainfall data for climate change impact studies in urban areas? Review of current approaches and trends. Theor. Appl. Climatol. 127, 685–699 (2017)

    Article  Google Scholar 

  23. 23.

    A. Shirvani, W.A. Landman, Seasonal precipitation forecast skill over Iran. Int. J. Climatol. 36, 1887–1900 (2016)

    Article  Google Scholar 

  24. 24.

    D. Han, T. Kwong, S. Li, Uncertainties in real-time flood forecasting with neural networks. Hydrol. Process. 21(2), 223–228 (2007)

    Article  Google Scholar 

  25. 25.

    V. Vapnik, The Nature of Statistical Learning Theory (Springer, New York, 1995)

    Google Scholar 

  26. 26.

    M. Saeid, B. Javad, K. Keivan, Using MARS, SVM, GEP and empirical equations for estimation of monthly mean reference evapotranspiration. Comp. Electron. Agric. 139, 103–114 (2017)

    Article  Google Scholar 

  27. 27.

    H. Ebrahimi, T. Rajaee, Simulation of groundwater level variations using wavelet combined with neural network, linear regression and support vector machine. Glob. Plan. Change 148, 181–191 (2017)

    Article  Google Scholar 

  28. 28.

    S. Kumar, T. Roshni, D. Himayoun, A comparison of emotional neural network (ENN) and artificial neural network (ANN) approach for rainfall-runoff modelling. Civ. Eng. J. 5, 2120–2130 (2019)

    Article  Google Scholar 

  29. 29.

    RStudio, Integrated Development Environment for R (2017)

  30. 30.

    S. Kumar, T. Roshni, E. Kahya, M.A. Ghorbani, Projecting the cropland suitability as a climate change impact for rice and wheat crops in the Sone river command, Bihar. Theor. Appl. Climatol. (2020). https://doi.org/10.1007/s00704-020-03319-9

    Article  Google Scholar 

  31. 31.

    T. Roshni, M.K. Jha, J. Drisya, Neural network modeling for groundwater-level forecasting in coastal aquifers. Neural Comput. Appl. 32, 12737–12754 (2020)

  32. 32.

    J. Chen, F.P. Brissette, D. Chaumont, M. Braun, Water Resour. Res. 49(7), 4187–4205 (2013)

    Article  Google Scholar 

  33. 33.

    T. Roshni, M.K. Jha, R.C. Deo, A. Vandana, Development and evaluation of hybrid artificial neural network architectures for modeling spatio-temporal groundwater fluctuations in a complex aquifer system. Water Resour. Manag. 33(7), 2381–2397 (2019)

    Article  Google Scholar 

  34. 34.

    K. Hornik, M. Stinchcombe, H. White, Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359–366 (1989)

    Article  Google Scholar 

  35. 35.

    I. Daubechies, IEEE Trans. Inf. Theory 36(5), 961–1005 (1990)

    Article  Google Scholar 

  36. 36.

    N. Ceryan, Application of support vector machines and relevance vector machines in predicting uniaxial compressive strength of volcanic rocks. J. Afr. Earth Sci. 100, 634–644 (2014)

    Article  Google Scholar 

  37. 37.

    Y. Seo, S. Kim, O. Kisi, V.P. Singh, Daily water level forecasting using wavelet decomposition and artificial intelligence techniques. J. Hydrol. 520, 224–243 (2015)

    Article  Google Scholar 

  38. 38.

    V. Nourani, M. Komasi, A. Mano, A multivariate ANN-wavelet approach for rainfall–runoff modeling. Water Resour. Manag. 23, 2877–2894 (2009)

    Article  Google Scholar 

Download references

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Correspondence to Thendiyath Roshni.

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Kumar, S., Roshni, T., Kumar, A. et al. GIS-Based Drought Assessment in Climate Change Context: A Case Study for Sone Command, Bihar. J. Inst. Eng. India Ser. A 102, 199–213 (2021). https://doi.org/10.1007/s40030-020-00505-w

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Keywords

  • WNN
  • FFNN
  • SVM
  • Rainfall
  • SPI
  • Climate change
  • Spatial drought