Observed trends in daily rainfall variability result in more severe climate change impacts to agriculture
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.
This research was supported by Hatch project VA-160078 from the USDA National Institute of Food and Agriculture.
- Bates D, Maechler M, Bolker B, et al (2018) lme4: linear mixed-effects models using “Eigen” and S4 (version 1.1-19)Google Scholar
- 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
- 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
- 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
- Lobell DB (2017) Comparing estimates of climate change impacts from processbased and statistical crop models. Env Res Lett 13Google Scholar
- Roque-Malo S, Kumar P (2017) Patterns of change in high frequency precipitation variability over North America. Sci Rep 7. https://doi.org/10.1038/s41598-017-10827-8
- United States Department of Agriculture (2018) USDA National Agricultural Statistics Service Quick Stats database. https://quickstats.nass.usda.gov/. Accessed 18 May 2018
- USDA National Agricultural Statistic Service (2014) 2012 census of agriculture: farm and ranch irrigation survey (2013)Google Scholar