Addressing the Potential Impacts of Climate Change and Variability on Agricultural Crops and Water Resources in Pennar River Basin of Andhra Pradesh

Chapter
Part of the Climate Change Management book series (CCM)

Abstract

The objective of the current study is to address the possible potential impacts of climate change and variability on agricultural crops and water resources in Pennar river basin, of Southern India. As part of the study Integrated Modelling Assessment (IMS) was developed by establishing functional links between hydrological model Soil Water Assessment Tool (SWAT), agricultural crop simulation model Environmental Policy Integrated Climate (EPIC) and regional climate model Providing REgional Climates for Impacts Studies (PRECIS). Database pertaining to climatic parameters, hydrological and agro-meteorological inputs to run integrated assessment systems are synthesized to run the model for study area. The model in general aim at major driver of this study is HadRM3 (Hadley Centre third generation regional climate model)—The Hadley Center Regional Climate Models resolution, which is 0.44° × 0.44° (approx. 50 km cell–size) on ground covering an average size of typical Indian districts/sub-basins. For regional levels the results are obtained by aggregating from the sub-basin/district level. The assessment will include the following components: (1) Baseline climatology, (2) Under global warning HadRM3 derived climate change scenarios, (3) Water Resources (Hydrological) analysis including irrigation water, and (4) agro-meteorological analysis including soil-water regime, plant growth and cropping pattern. Overall in Pennar region results revealed that the mean annual flows in the river system would increase by 8 % in A2 and 4 % in B2 whereas, increase in evapotranspiration losses were found to be about 10 % in A2 and 12 % in B2. Impacts on crop yields is the combined effect of increased surface temperatures, decreased rainfall and higher ambient atmospheric CO2. Three rain-fed crops (Groundnut, Sorghum, Sunflower) show decreased yields under A2, whereas B2 seemed to be relatively better than A2. The decrease is significant for groundnut (−38 % for A2 and −20 % for B2), but compared to groundnut impact were less detrimental for other two rain-fed crops (Sorghum and Sunflower). Rice being an irrigated crop in the region showed decrease in yield by −15 and −7 % for A2 and B2 scenarios respectively. Negative simulated crop yields in the region are predominantly due to increased surface temperatures in the future climate change scenarios.

Keywords

Climate change SWAT EPIC PRECIS 

References

  1. Arnold JG, Srinivasan A, Muttiah RS, Williams JR (1998) Large area hydrologic modeling and assessment part I: model development. J Am Water Resour Assoc 34(1):73–89CrossRefGoogle Scholar
  2. Boote KJ, Jones JW, Hoogenboom G (1998) Simulation of crop growth: CROPGRO model. In: Peart RM, Curry RB (eds) Agricultural systems modelling and simulation. M. Dekker, New York, pp 651–691Google Scholar
  3. Challinor AJ, Slingo JM, Wheeler TR, Doblas-Reyes FJ (2005) Probabilistic hind casts of crop yield over western India. Tellus 57A:498–512CrossRefGoogle Scholar
  4. Challinor AJ, Wheeler TR (2008) Crop yield reduction in the tropics under climate change: processes and uncertainties. Agric For Meteorol 148:343–356CrossRefGoogle Scholar
  5. Chiotti QP, Johnston T (1995) Extending the boundaries of climate change research: a discussion on agriculture. J Rural Stud 11:335–350CrossRefGoogle Scholar
  6. Gnanadesikan A et al (2006) GFDL’s CM2 global coupled climate models. Part II: the baseline ocean simulation. J Clim 19:675–697CrossRefGoogle Scholar
  7. Harding BL, Wood AW, Prairie JR (2012) The implications of climate change scenario selection for future stream flow projection in the upper colorado river basin. Hydrol Earth Syst Sci Discuss 16:3989–4007. doi:10.5194/hess-16-3989-2012 CrossRefGoogle Scholar
  8. Houghton JT, DingY, Griggs DJ, Noguer M, Van der Linden PJ, Dai X, Maskell K, Johnson CA (2001) Climate change 2001. The scientific basis. contribution of working group I to the third assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, p 881Google Scholar
  9. Mitra AP, Kumar D, Rupa M, Kumar K, Abrol YP, Kalra N, Velayutham M, Naqvi SWA (2002) Global change and biogeochemical cycles: the South Asia region. In: Tyson P, Fuchs R, Fu C, Lebel L, Mitra AP, Odada E, Perry J, Steffen W, Virji H (eds) Global-regional linkages in the earth system. Springer, BerlinGoogle Scholar
  10. Osborne TM, Lawrence DM, Challinor AJ, Slingo JM, Wheeler TR (2007) Development and assessment of acoupled crop–climate model. Glob Change Biol 13:169–183CrossRefGoogle Scholar
  11. Schlenker W, Roberts MJ (2008) Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. PANS 106:15594–15598CrossRefGoogle Scholar
  12. Slingo JM, Challinor AJ, Hiskins BJ, Wheeler TR (2005) Introduction: food crops in a changing climate. Philos Trans R Soc B 360:1983–1989CrossRefGoogle Scholar
  13. Williams JR (1995) The EPIC model. In: Singh VP (ed) Computer models of watershed hydrology. Water Resources Publisher, USA, pp 909–1000Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)Addis AbabaEthiopia

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