Observed trends in daily rainfall variability result in more severe climate change impacts to agriculture

  • Julie ShortridgeEmail author


There is increasing evidence that climate change is impacting not just total amounts of precipitation, but its temporal dynamics as well. While previous studies have identified the importance that the temporal distribution of daily rainfall has on crop production, climate models often do not represent these distributions consistently with observed trends. The objectives of this study are to (1) evaluate the relationship between rainfall variability and yields for economically important crops in the upper Southeastern United States and (2) assess the potential impact that incorporating observed trends in rainfall variability has on yield projections under future climate change. This study develops statistical models of historic crop yields for five crops, finding that an explanatory variable related to daily rainfall variability, the wet-day Gini coefficient (GC), has a statistically significant negative relationship with crop yields in all cases. These models are then used to estimate the impacts of climate change using an ensemble of downscaled general circulation model (GCM) projections and scenarios that include a continuation of observed GC trends. Most downscaled GCMs evaluated do not project changes in GC consistent with observed trends, and scenarios that assume a continuation of observed trends result in projected yields that are up to 5.8% lower than those directly based on GCM projections. While additional research is needed in the climate science community to better understand how rainfall variability may change in the future, this should be mirrored in the impacts community so that agricultural impact assessments incorporate these potentially important changes.



I would like to thank Mitchell Paoletti and Drew Ellis for their help and perspective on this research.

Funding information

This research was supported by Hatch project VA-160078 from the USDA National Institute of Food and Agriculture.


  1. Allan RP, Soden BJ (2008) Atmospheric warming and the amplification of precipitation extremes. Science 321:1481–1484. CrossRefGoogle Scholar
  2. Auffhammer M, Ramanathan V, Vincent JR (2012) Climate change, the monsoon, and rice yield in India. Clim Chang 111:411–424. CrossRefGoogle Scholar
  3. Baigorria GA, Jones JW (2010) GiST: a stochastic model for generating spatially and temporally correlated daily rainfall data. J Clim 23:5990–6008. CrossRefGoogle Scholar
  4. Bates D, Maechler M, Bolker B, et al (2018) lme4: linear mixed-effects models using “Eigen” and S4 (version 1.1-19)Google Scholar
  5. Bi D, Dix M, Marsland S et al (2013) The ACCESS coupled model: description, control climate and evaluation. Aust Meteorol Oceanogr J 63:41–64. CrossRefGoogle Scholar
  6. Burke M, Emerick K (2016) Adaptation to climate change: evidence from US agriculture. Am Econ J Econ Policy 8:106–140. CrossRefGoogle Scholar
  7. Cammarano D, Rivington M, Matthews KB et al (2017) Implications of climate model biases and downscaling on crop model simulated climate change impacts. Eur J Agron 88:63–75. CrossRefGoogle Scholar
  8. Daly C, Halbleib M, Smith JI et al (2008) Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int J Climatol 28:2031–2064. CrossRefGoogle Scholar
  9. Davis KF, Chhatre A, Rao ND et al (2019) Sensitivity of grain yields to historical climate variability in India. Environ Res Lett 14:064013. CrossRefGoogle Scholar
  10. Demirel MC, Moradkhani H (2016) Assessing the impact of CMIP5 climate multi-modeling on estimating the precipitation seasonality and timing. Clim Chang 135:357–372. CrossRefGoogle Scholar
  11. Dittus AJ, Karoly DJ, Lewis SC et al (2016) A multiregion model evaluation and attribution study of historical changes in the area affected by temperature and precipitation extremes. J Clim 29:8285–8299. CrossRefGoogle Scholar
  12. Donat MG, Alexander LV, Yang H et al (2013) Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: the HadEX2 dataset. J Geophys Res Atmos 118:2098–2118. CrossRefGoogle Scholar
  13. Donner LJ, Wyman BL, Hemler RS et al (2011) The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component AM3 of the GFDL global coupled model CM3. J Clim 24:3484–3519. CrossRefGoogle Scholar
  14. Dufresne JL, Foujols MA, Denvil S, et al (2013) Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5. Clim Dyn 40:2123–2165. doi: CrossRefGoogle Scholar
  15. Easterling DRK (2017) Chapter 7: precipitation change in the United States. In: Climate science special report: fourth National Climate Assessment. U.S. Global Change Research ProgramGoogle Scholar
  16. Fishman R (2016) More uneven distributions overturn benefits of higher precipitation for crop yields. Environ Res Lett 11:024004. CrossRefGoogle Scholar
  17. Flato G, Marotzke J, Abiodun B et al (2014) Evaluation of climate models. In: Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, pp 741–866Google Scholar
  18. Gleick PH (1986) Methods for evaluating the regional hydrologic impacts of global climatic changes. J Hydrol 88:97–116. CrossRefGoogle Scholar
  19. Guo D, Westra S, Maier HR (2018) An inverse approach to perturb historical rainfall data for scenario-neutral climate impact studies. J Hydrol 556:877–890. CrossRefGoogle Scholar
  20. Hawkins E, Osborne TM, Ho CK, Challinor AJ (2013) Calibration and bias correction of climate projections for crop modelling: an idealised case study over Europe. Agric For Meteorol 170:19–31. CrossRefGoogle Scholar
  21. Hoerling M, Eischeid J, Perlwitz J et al (2016) Characterizing recent trends in U.S. heavy precipitation. J Clim 29:2313–2332. CrossRefGoogle Scholar
  22. Homer CG, Dewitz JA, Yang L et al (2015) Completion of the 2011 National Land Cover Database for the conterminous United States-representing a decade of land cover change information. Photogramm Eng Remote Sens 81:345–354Google Scholar
  23. Ines AVM, Hansen JW, Robertson AW (2011) Enhancing the utility of daily GCM rainfall for crop yield prediction. Int J Climatol 31:2168–2182. CrossRefGoogle Scholar
  24. Knutti R, Masson D, Gettelman A (2013) Climate model genealogy: generation CMIP5 and how we got there. Geophys Res Lett 40:1194–1199. CrossRefGoogle Scholar
  25. Lobell DB (2017) Comparing estimates of climate change impacts from processbased and statistical crop models. Env Res Lett 13Google Scholar
  26. Lobell DB, Schlenker W, Costa-Roberts J (2011) Climate trends and global crop production since 1980. Science 333:616–620. CrossRefGoogle Scholar
  27. Mearns LO, Rosenzweig C, Goldberg R (1996) The effect of changes in daily and interannual climatic variability on CERES-wheat: a sensitivity study. Clim Chang 32:257–292. CrossRefGoogle Scholar
  28. Milly PCD, Malyshev SL, Shevliakova E et al (2014) An enhanced model of land water and energy for global hydrologic and earth-system studies. J Hydrometeorol 15:1739–1761. CrossRefGoogle Scholar
  29. Min S-K, Zhang X, Zwiers FW, Hegerl GC (2011) Human contribution to more-intense precipitation extremes. Nature 470:378–381. CrossRefGoogle Scholar
  30. Moore FC, Baldos ULC, Hertel T (2017) Economic impacts of climate change on agriculture: a comparison of process-based and statistical yield models. Environ Res Lett 12:065008. CrossRefGoogle Scholar
  31. Olesen J, Jensen T, Petersen J (2000) Sensitivity of field-scale winter wheat production in Denmark to climate variability and climate change. Clim Res 15:221–238. CrossRefGoogle Scholar
  32. Pendergrass AG, Knutti R (2018) The uneven nature of daily precipitation and its change. Geophys Res Lett 45:11,980–11,988. CrossRefGoogle Scholar
  33. Rajah K, O’Leary T, Turner A et al (2014) Changes to the temporal distribution of daily precipitation: changing precipitation temporal patterns. Geophys Res Lett 41:8887–8894. CrossRefGoogle Scholar
  34. Richter GM, Semenov MA (2005) Modelling impacts of climate change on wheat yields in England and Wales: assessing drought risks. Agric Syst 84:77–97. CrossRefGoogle Scholar
  35. Roberts MJ, Braun NO, Sinclair TR et al (2017) Comparing and combining process-based crop models and statistical models with some implications for climate change. Environ Res Lett 12:095010. CrossRefGoogle Scholar
  36. Roque-Malo S, Kumar P (2017) Patterns of change in high frequency precipitation variability over North America. Sci Rep 7.
  37. Rosenzweig C, Elliott J, Deryng D et al (2014) Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc Natl Acad Sci 111:3268–3273. CrossRefGoogle Scholar
  38. Rowhani P, Lobell DB, Linderman M, Ramankutty N (2011) Climate variability and crop production in Tanzania. Agric For Meteorol 151:449–460. CrossRefGoogle Scholar
  39. Royé D, Martin-Vide J (2017) Concentration of daily precipitation in the contiguous United States. Atmospheric Res 196:237–247. CrossRefGoogle Scholar
  40. Schauberger B, Archontoulis S, Arneth A et al (2017) Consistent negative response of US crops to high temperatures in observations and crop models. Nat Commun 8:13931. CrossRefGoogle Scholar
  41. Schlenker W, Roberts MJ (2009) Nonlinear temperature effects indicate severe damages to US crop yields under climate change. Proc Natl Acad Sci 106:15594–15598CrossRefGoogle Scholar
  42. Semenov MA, Donatelli M, Stratonovitch P et al (2010) ELPIS: a dataset of local-scale daily climate scenarios for Europe. Clim Res 44:3–15. CrossRefGoogle Scholar
  43. Sheffield J, Goteti G, Wood EF (2006) Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling. J Clim 19:3088–3111. CrossRefGoogle Scholar
  44. Tebaldi C, Knutti R (2007) The use of the multi-model ensemble in probabilistic climate projections. Philos Trans R Soc Math Phys Eng Sci 365:2053–2075CrossRefGoogle Scholar
  45. Tebaldi C, Hayhoe K, Arblaster JM, Meehl GA (2006) Going to the extremes. Clim Chang 79:185–211. CrossRefGoogle Scholar
  46. Thrasher B, Maurer EP, McKellar C, Duffy PB (2012) Technical note: bias correcting climate model simulated daily temperature extremes with quantile mapping. Hydrol Earth Syst Sci 16:3309–3314. CrossRefGoogle Scholar
  47. Thrasher B, Xiong J, Wang W et al (2013) Downscaled climate projections suitable for resource management. EOS Trans Am Geophys Union 94:321–323CrossRefGoogle Scholar
  48. Troy TJ, Kipgen C, Pal I (2015) The impact of climate extremes and irrigation on US crop yields. Environ Res Lett 10:054013. CrossRefGoogle Scholar
  49. United States Department of Agriculture (2018) USDA National Agricultural Statistics Service Quick Stats database. Accessed 18 May 2018
  50. USDA National Agricultural Statistic Service (2014) 2012 census of agriculture: farm and ranch irrigation survey (2013)Google Scholar
  51. Volodin EM, Dianskii NA, Gusev AV (2010) Simulating present-day climate with the INMCM4.0 coupled model of the atmospheric and oceanic general circulations. Izv Atmos Ocean Phys 46:414–431. CrossRefGoogle Scholar
  52. Westra S, Alexander LV, Zwiers FW (2012) Global increasing trends in annual maximum daily precipitation. J Clim 26:3904–3918. CrossRefGoogle Scholar
  53. Wilby RL, Dessai S (2010) Robust adaptation to climate change. Weather 65:180–185. CrossRefGoogle Scholar
  54. Wolf J, Evans L, Semenov M et al (1996) Comparison of wheat simulation models under climate change. I. Model calibration and sensitivity analyses. Clim Res 7:253–270. CrossRefGoogle Scholar
  55. Zhao C, Liu B, Piao S et al (2017) Temperature increase reduces global yields of major crops in four independent estimates. Proc Natl Acad Sci 114:9326–9331. CrossRefGoogle Scholar
  56. Zuur AF, Ieno EN, Elphick CS (2010) A protocol for data exploration to avoid common statistical problems. Methods Ecol Evol 1:3–14. CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Biological Systems EngineeringVirginia TechBlacksburgUSA

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