• Title/Summary/Keyword: bioclimatic zones

Search Result 3, Processing Time 0.017 seconds

Analysis of Future Bioclimatic Zones Using Multi-climate Models (다중기후모형을 활용한 동북아시아의 미래 생물기후권역 변화분석)

  • Choi, Yuyoung;Lim, Chul-Hee;Ryu, Jieun;Jeon, Seongwoo
    • Journal of Environmental Impact Assessment
    • /
    • v.27 no.5
    • /
    • pp.489-508
    • /
    • 2018
  • As climate changes, it is necessary to predict changes in the habitat environment in order to establish more aggressive adaptation strategies. The bioclimatic classification which clusters of areas with similar habitats can provide a useful ecosystem management framework. Therefore, in this study, biological habitat environment of Northeast Asia was identified through the establishment of the bioclimatic zones, and the impac of climate change on the biological habitat was analyzed. An ISODATA clustering was used to classify Northeast Asia (NEA)into 15 bioclimatic zones, and climate change impacts were predicted by projecting the future spatial distribution of bioclimatic zones based upon an ensemble of 17 GCMs across RCP4.5 and 8.5 scenarios for 2050s, and 2070s. Results demonstrated that significant changes in bioclimatic conditions can be expected throughout the NEA by 2050s and 2070s. The overall zones moved upward, and some zones were predicted to be greatly expanded or shrunk where we suggested as regions requiring intensive management. This analysis provides the basis for understanding potential impacts of climate change on biodiversity and ecosystem. Also, this could be used more effectively to support decision making on climate change adaptation.

Spatial Distribution Patterns of Winter Daytime and Nighttime Apparent Temperature in South Korea (남한의 겨울철 주.야간 체감 온도의 공간적 분포 특성)

  • 최광용;강철성
    • Journal of the Korean Geographical Society
    • /
    • v.37 no.3
    • /
    • pp.237-246
    • /
    • 2002
  • This study classified wintertime bioclimatic zones of South Korea based on daytime and nighttime distribution of wind chill index calculated from climate data during the coldest month for latest 30 years (1971- 2000). The results show that the winter daytime and nighttime wind chill index were influenced by climatic factors such as elevation, land-sea breeze, topology, and sea currents etc. as well as climatic components such as temperature, wind speed, and sunshine, so that South Korea was divided into five bioclimatic zones; Cool day- cold night zone, Keen day- Cold night zone, Keen day-Very Cold night zone, Cold day and night zone, and Cold day-Extremely Cold night zone. Especially, coasts and island areas, except for south coast of Korea, shows Keen bioclimatic response during daytime and Very Cold bioclimatic response during nighttime. This indicates that coasts and island areas, except for south coast of Korea are affected by moonson and land-sea breeze. In addition, highly elevated Daegwallyeong shows Cold bioclimatic response during daytime and Extremely Cold during nighttime due to the influence of adiabatic temperature lapse rate and monsoon. This study offers basic data necessary to make decisions concerning insulation such as clothing and architect etc. by classifying winter bioclimatic zones of South Korea based on various daytime and nighttime distribution of wind chill.

Bioclimatic Classification and Characterization in South Korea (남한의 생물기후권역 구분과 특성 규명)

  • Choi, Yu-Young;Lim, Chul-Hee;Ryu, Ji-Eun;Piao, Dongfan;Kang, Jin-Young;Zhu, Weihong;Cui, Guishan;Lee, Woo-Kyun;Jeon, Seong-Woo
    • Journal of the Korean Society of Environmental Restoration Technology
    • /
    • v.20 no.3
    • /
    • pp.1-18
    • /
    • 2017
  • This study constructed a high-resolution bioclimatic classification map of South Korea which classifies land into homogeneous zones by similar environment properties using advanced statistical techniques compared to existing ecological area classification studies. The climate data provided by WorldClim(1960-1990) were used to generate 27 bioclimatic variables affecting biological habitats, and key environmental variables were derived from Correlation Analysis and Principal Component Analysis. Clustering Analysis was performed using the ISODATA method to construct a 30'(~1km) resolution bioclimatic classification map. South Korea was divided into 21 regions and the results of classification were verified by correlation analysis with the Gross Primary Production(GPP), Actual Vegetation map made by the Ministry of Environment. Each zones' were described and named by its environmental characteristics and major vegetation distribution. This study could provide useful spatial frameworks to support ecosystem research, monitoring and policy decisions.