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Predicting potential epidemics of rice diseases in Korea using multi-model ensembles for assessment of climate change impacts with uncertainty information

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  • Kwang-Hyung Kim
  • Jaepil Cho

Abstract

It is highly anticipated that meteorological changes resulting from global climate change will affect the pattern of rice disease epidemics worldwide. Here, we evaluated the potential impacts of climate change on two representative rice diseases, leaf blast and sheath blight, in Korea. This study involves analyses of disease simulation using an epidemiological model, EPIRICE, which was validated for Korean rice paddy fields. The goal of our study was to assess likely changes in national disease probabilities using individual climate scenarios across different models and multi-model ensemble scenarios constructed by running 11 global climate models. In this way, the results from this study emphasize the uncertainties in climate change scenarios resulting from the variations in initial conditions as well as the structural differences in the global climate models. Observed and simulated epidemics for both diseases were compared using the area under the disease progress curve from EPIRICE model runs. Overall, the simulated incidence of epidemics for both diseases gradually decreased towards 2100 both from individual global climate models and multi-model ensembles. It was noted that while each individual model resulted in different magnitudes of impact, the multi-model ensemble gave the most reliable result that accounts for uncertainty compared to the individual models. In conclusion, we found that in modeling climate impacts on rice diseases, ensembles account for uncertainty better than individual climate models and can lead to better decision making. Copyright Springer Science+Business Media Dordrecht 2016

Suggested Citation

  • Kwang-Hyung Kim & Jaepil Cho, 2016. "Predicting potential epidemics of rice diseases in Korea using multi-model ensembles for assessment of climate change impacts with uncertainty information," Climatic Change, Springer, vol. 134(1), pages 327-339, January.
    • Handle: RePEc:spr:climat:v:134:y:2016:i:1:p:327-339
    DOI: 10.1007/s10584-015-1503-2
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    References listed on IDEAS

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    1. Matthews, R. B. & Kropff, M. J. & Horie, T. & Bachelet, D., 1997. "Simulating the impact of climate change on rice production in Asia and evaluating options for adaptation," Agricultural Systems, Elsevier, vol. 54(3), pages 399-425, July.
    2. Teng, P. S. & Savary, S., 1992. "Implementing the systems approach in pest management," Agricultural Systems, Elsevier, vol. 40(1-3), pages 237-264.
    3. Luo, Y. & Teng, P. S. & Fabellar, N. G. & TeBeest, D. O., 1997. "A rice-leaf blast combined model for simulation of epidemics and yield loss," Agricultural Systems, Elsevier, vol. 53(1), pages 27-39, January.
    4. James M. Murphy & David M. H. Sexton & David N. Barnett & Gareth S. Jones & Mark J. Webb & Matthew Collins & David A. Stainforth, 2004. "Quantification of modelling uncertainties in a large ensemble of climate change simulations," Nature, Nature, vol. 430(7001), pages 768-772, August.
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    Cited by:

    1. Kyoung-Tae Lee & Hye-Won Jeon & Sook-Young Park & Jaepil Cho & Kwang-Hyung Kim, 2022. "Comparison of projected rice blast epidemics in the Korean Peninsula between the CMIP5 and CMIP6 scenarios," Climatic Change, Springer, vol. 173(1), pages 1-20, July.
    2. Wang, Hui & Mongiano, Gabriele & Fanchini, Davide & Titone, Patrizia & Tamborini, Luigi & Bregaglio, Simone, 2021. "Varietal susceptibility overcomes climate change effects on the future trends of rice blast disease in Northern Italy," Agricultural Systems, Elsevier, vol. 193(C).

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