How climate scenarios alter future predictions of field-scale water and nitrogen dynamics and crop yields

JOURNAL OF ENVIRONMENTAL MANAGEMENT

卷:252

Article

Jeong, Hanseok^1,2  Bhattarai, Rabin¹  Hwang, Syewoon³ 

[1] Univ Illinois, Dept Agr & Biol Engn, Urbana, IL 61801 USA

[2] Seoul Natl Univ Sci & Technol, Dept Environm Engn, Seoul 01811, South Korea

[3] Gyeongsang Natl Univ, Inst Agr & Life Sci, Dept Agr Engn, Jinju 52828, South Korea

关键词: Crop yields;    General circulation model (GCM);    Nitrate loss;    Root Zone Water Quality Model (RZWQM);    Tile drainage;   

DOI  :  10.1016/j.jenvman.2019.109623

来源: Elsevier

【 摘 要 】

Climate change scenarios are widely used for exploring future changes in environmental systems. However, many aspects of the uncertainties associated with the use of climate change scenarios in environmental systems modeling have not yet been studied sufficiently. We explore how the way that baseline scenarios are defined and general circulation model (GCM) outputs are used affects climate change impact assessments of agricultural systems. Our study builds on a previously validated agricultural systems model, the Root Zone Water Quality Model (RZWQM), coupled with the Decision Support System for Agrotechnology Transfer (DSSAT), which models a tiled-drained field in central Illinois of the United States and uses nine GCM outputs to investigate the effects. Our model simulations demonstrated the following three results. Firstly, the evaluation of climate change impacts presented a significant difference between the types of baseline used. The baseline scenario should be defined using the bias-corrected retrospective GCM outputs. Secondly, once GCM outputs are bias-corrected, the selective use of GCM outputs did not add significant value over using all available GCM outputs to provide more plausible future descriptions of agricultural systems' responses. Notably, however, selective use may have impacts comparable to carbon dioxide (CO2) emission scenarios in the field-scale agricultural climate change impact assessments. Thirdly, raw GCM outputs should be avoided for the predictions of field-scale agricultural systems' responses to climate change. Our findings can help provide a clearer picture of how GCM outputs should be used in agricultural systems modeling and might enable us to have more plausible descriptions of how future agricultural systems might unfold.

【 授权许可】

Free