• Title/Summary/Keyword: RCPs scenarios

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Streamflow response to climate change during the wet and dry seasons in South Korea under a CMIP5 climate model (CMIP5 기반 건기 및 우기 시 국내 하천유량의 변화전망 및 분석)

  • Ghafouri-Azar, Mona;Bae, Deg-Hyo
    • Journal of Korea Water Resources Association
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    • v.51 no.spc
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    • pp.1091-1103
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    • 2018
  • Having knowledge regarding to which region is prone to drought or flood is a crucial issue in water resources planning and management. This could be more challenging when the occurrence of these hazards affected by climate change. In this study the future streamflow during the wet season (July to September) and dry season (October to March) for the twenty first century of South Korea was investigated. This study used the statistics of precipitation, maximum and minimum temperature of one global climate model (i.e., INMCM4) with 2 RCPs (RCP4.5 and RCP8.5) scenarios as inputs for The Precipitation-Runoff Modelling System (PRMS) model. The PRMS model was tested for the historical periods (1966-2016) and then the parameters of model were used to project the future changes of 5 large River basins in Korea for three future periods (2025s, 2055s, and 2085s) compared to the reference period (1976-2005). Then, the different responses in climate and streamflow projection during these two seasons (wet and dry) was investigated. The results showed that under INMCM4 scenario, the occurrence of drought in dry season is projected to be stronger in 2025s than 2055s from decreasing -7.23% (-7.06%) in 2025s to -3.81% (-0.71%) in 2055s for RCP4.5 (RCP8.5). Regarding to the far future (2085s), for RCP 4.5 is projected to increase streamflow in the northern part, and decrease streamflow in the southern part (-3.24%), however under RCP8.5 almost all basins are vulnerable to drought, especially in the southern part (-16.51%). Also, during the wet season both increasing (Almost in northern and western part) and decreasing (almost in the southern part) in streamflow relative to the reference period are projected for all periods and RCPs under INMCM4 scenario.

Predicting the suitable habitat of the Pinus pumila under climate change (기후변화에 의한 눈잣나무의 서식지 분포 예측)

  • Park, Hyun-Chul;Lee, Jung-Hwan;Lee, Gwan-Gyu
    • Journal of Environmental Impact Assessment
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    • v.23 no.5
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    • pp.379-392
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    • 2014
  • This study was performed to predict the future climate envelope of Pinus pumila, a subalpine plant and a Climate-sensitive Biological Indicator Species (CBIS) of Korea. P. pumila is distributed at Mt. seorak in South Korea. Suitable habitat were predicted under two alternative RCPscenarios (IPCC AR5). The SDM used for future prediction was a Maxent model, and the total number of environmental variables for Maxent was 8. It was found that the distribution range of P. pumila in the South Korean was $38^{\circ}7^{\prime}8^{{\prime}{\prime}}N{\sim}38^{\circ}7^{\prime}14^{{\prime}{\prime}}N$ and $128^{\circ}28^{\prime}2^{{\prime}{\prime}}E{\sim}128^{\circ}27^{\prime}38^{{\prime}{\prime}}E$ and 1,586m~1,688m in altitude. The variables that contribute the most to define the climate envelope are altitude. Climate envelope simulation accuracy was evaluated using the ROC's AUC. The P. pumila model's 5-cv AUC was found to be 0.99966. which showed that model accuracy was very high. Under both the RCP4.5 and RCP8.5 scenarios, the climate envelope for P. pumila is predicted to decrease in South Korea. According to the results of the maxent model has been applied in the current climate, suitable habitat is $790.78km^2$. The suitable habitats, are distributed in the region of over 1,400m. Further, in comparison with the suitable habitat of applying RCP4.5 and RCP8.5 suitable habitat current, reduction of area RCP8.5 was greater than RCP4.5. Thus, climate change will affect the distribution of P. pumila. Therefore, governmental measures to conserve this species will be necessary. Additionally, for CBIS vulnerability analysis and studies using sampling techniques to monitor areas based on the outcomes of this study, future study designs should incorporate the use of climatic predictions derived from multiple GCMs, especially GCMs that were not the one used in this study. Furthermore, if environmental variables directly relevant to CBIS distribution other than climate variables, such as the Bioclim parameters, are ever identified, more accurate prediction than in this study will be possible.