• Korean Journal of Ecology and Environment
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• v.49 no.1
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• pp.42-50
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• 2016

The purpose of this study is to set the direction to manage national parks to cope with climate change, and offer basic data to establish the relevant policies. Towards this end, this study analyzed the current and future climate change vulnerability of national parks using the 24 proxy variables of vulnerability in the LCCGIS program, a tool to evaluate climate change vulnerability developed by the National Institute of Environmental Research. To analyze and evaluate the current status of and future prospect on climate change vulnerability of national parks, the proxy variable value of climate exposure was calculated by making a GIS spatial thematic map with $1km{\times}1km$ grid unit through the application of climate change scenario (RCP8.5). The values of proxy variables of sensitivity and adaptation capability were calculated using the basic statistics of national parks. The values of three vulnerability evaluation items were calculated regarding the present (2010s) and future (2050s). The current values were applied to the future equally under the assumption that the current state of the proxy variables related to sensitivity and adaptation capability without a future prediction scenario continues. Seoraksan, Odaesan, Jirisan and Chiaksan National Parks are relatively bigger in terms of the current (2010s) climate exposure. The national park, where the variation of heat wave is the biggest is Wolchulsan National Park. The biggest variation of drought occurs to Gyeryongsan National Park, and Woraksan National Park has the biggest variation of heavy rain. Concerning the climate change sensitivity of national parks, Jirisan National Park is the most sensitive, and adaptation capability is evaluated to be the highest. Gayasan National Park's sensitivity is the lowest, and Chiaksan National Park is the lowest in adaptation capability. As for climate change vulnerability, Seoraksan, Odaesan, Chiaksan and Deogyusan National Parks and Hallyeohaesang National Park are evaluated as high at the current period. The national parks, where future vulnerability change is projected to be the biggest, are Jirisan, Woraksan, Chiaksan and Sobaeksan National Parks in the order. Because such items evaluating the climate change vulnerability of national parks as climate exposure, sensitivity and adaptation capability show relative differences according to national parks' local climate environment, it will be necessary to devise the adaptation measures reflecting the local climate environmental characteristics of national parks, rather than establishing uniform adaptation measures targeting all national parks. The results of this study that evaluated climate change vulnerability using climate exposure, sensitivity and adaptation capability targeting Korea's national parks are expected to be used as basic data for the establishment of measures to adapt to climate change in consideration of national parks' local climate environmental characteristics. However, this study analyzed using only the proxy variables presented by LCCGIS program under the situation that few studies on the evaluation of climate change vulnerability of national parks are found, and therefore this study may not reflect overall national parks' environment properly. A further study on setting weights together with an objective review on more proper proxy variables needs to be carried out in order to evaluate the climate change vulnerability of national parks.

• Korean Journal of Ecology and Environment
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• v.54 no.4
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• pp.303-314
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• 2021

To understand how to efficiently observe the biomass and community of phytoplankton, phytoplankton sampling was carried out from June to October 2019 at the Yeongju dam sediment control reservoir(YJ) and Bohyeonsan dam reservoir(BH1 and BH2). The results derived from microscopic observation, such as the conventional phytoplankton qualitative/quantitative analysis, and from the CHEMTAX method based on the pigments, were compared. The relative contribution of phytoplankton, calculated by the microscopy and CHEMTAX methods, showed a significant difference in all four classes: cryptophyta, chlorophyta, cyanobacteria, and diatoms. In addition, the correlation between the two observation methods was poor. This might be caused by methodological differences in microscopy that do not consider the varying cell sizes among phytoplankton species. In this study, by converting the cells into carbon, the slope between both carbon biomasses based on microscopy and CHEMTAX was improved close to the 1 : 1 line, and the y-intercept was closer to 0 for cryptophyta and diatoms. For cyanobacteria, the slope increased, the y-intercept decreased, and the plot approached 1 : 1 although the correlation coefficients were not improved in all classes. The present study suggests that application of CHEMTAX based on pigment analysis could be a possible approach to efficiently determine the relative carbon proportions of individual classes of phytoplankton community composition.