Articles | Volume 25, issue 2
https://doi.org/10.5194/hess-25-551-2021
https://doi.org/10.5194/hess-25-551-2021
Research article
 | 
03 Feb 2021
Research article |  | 03 Feb 2021

Quantifying the impacts of compound extremes on agriculture

Iman Haqiqi, Danielle S. Grogan, Thomas W. Hertel, and Wolfram Schlenker

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Cited articles

Bevacqua, E., Maraun, D., Vousdoukas, M. I., Voukouvalas, E., Vrac, M., Mentaschi, L., and Widmann, M.: Higher probability of compound flooding from precipitation and storm surge in Europe under anthropogenic climate change, Sci. Adv., 5, eaaw5531, https://doi.org/10.1126/sciadv.aaw5531, 2019. 
Boryan, C., Yang, Z., and Di, L.: Deriving 2011 cultivated land cover data sets using usda national agricultural statistics service historic cropland data layers, Proc. of IEEE International Geoscience and Remote Sensing Symposium, 22–27 July 2012, Munich, Germany, 6297–6300, 2012. 
Bradford, J. B., Schlaepfer, D. R., Lauenroth, W. K., Yackulic, C. B., Duniway, M., Hall, S., Jia, G., Jamiyansharav, K., Munson, S. M., Wilson, S. D., and Tietjen, B.: Future soil moisture and temperature extremes imply expanding suitability for rainfed agriculture in temperate drylands, Sci. Rep.-UK, 7, 12923, https://doi.org/10.1038/s41598-017-13165-x, 2017. 
Burke, M. and Emerick, K.: Adaptation to climate change: Evidence from US agriculture, Am. Econ. J. Econ. Policy, 8, 106–140, 2016. 
Comas, L. H., Trout, T. J., DeJonge, K. C., Zhang, H., and Gleason, S. M.: Water productivity under strategic growth stage-based deficit irrigation in maize, Agric. Water Manag., 212, 433–440, https://doi.org/10.1016/j.agwat.2018.07.015, 2019. 
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Short summary
This study combines a fine-scale weather product with outputs of a hydrological model to construct functional metrics of individual and compound hydroclimatic extremes for agriculture. Then, a yield response function is estimated with individual and compound metrics focusing on corn in the United States during the 1981–2015 period. The findings suggest that metrics of compound hydroclimatic extremes are better predictors of corn yield variations than metrics of individual extremes.