Journal of Korean Society on Water Environment (한국물환경학회지)

Korean Society on Water Environment (한국물환경학회)
권호 표지

Development of a Grid-Based Daily Land Surface Temperature Prediction Model considering the Effect of Mean Air Temperature and Vegetation

평균기온과 식생의 영향을 고려한 격자기반 일 지표토양온도 예측 모형 개발

  • Choi, Chihyun (Department of Environmental Engineering, Pukyong National University) ;
  • Choi, Daegyu (Department of Environmental Engineering, Pukyong National University) ;
  • Choi, Hyun Il (Department of Civil Engineering, Yeungnam University) ;
  • Kim, Kyunghyun (Water Quality Control Center, National Institute of Environmental Research) ;
  • Kim, Sangdan (Department of Environmental Engineering, Pukyong National University)
  • 최치현 (부경대학교 환경공학과) ;
  • 최대규 (부경대학교 환경공학과) ;
  • 최현일 (영남대학교 건설시스템공학과) ;
  • 김경현 (국립환경과학원 수질통합관리센터) ;
  • 김상단 (부경대학교 환경공학과)
  • Published : 2012.01.30
⟨ Previous Next ⟩

Abstract

Land surface temperature in ecohydrology is a variable that links surface structure to soil processes and yet its spatial prediction across landscapes with variable surface structure is poorly understood. And there are an insufficient number of soil temperature monitoring stations. In this study, a grid-based land surface temperature prediction model is proposed. Target sites are Andong and Namgang dam region. The proposed model is run in the following way. At first, geo-referenced site specific air temperatures are estimated using a kriging technique from data collected from 60 point weather stations. Then surface soil temperature is computed from the estimated geo-referenced site-specific air temperature and normalized difference vegetation index. After the model is calibrated with data collected from observed remote-sensed soil temperature, a soil temperature map is prepared based on the predictions of the model for each geo-referenced site. The daily and monthly simulated soil temperature shows that the proposed model is useful for reproducing observed soil temperature. Soil temperatures at 30 and 50 cm of soil depth are also well simulated.

Keywords

References

  1. 경민수, 김상단, 김형수, 박석근(2006). 통계적 기법을 이용한 경안천 유역의 수질 측정망 구성, 대한토목학회논문집, 26(1B), pp. 262-276.
  2. 김문성, 고익환, 김문성(2009). CGCM의 미래 기후 정보를 이용한 기후변화가 낙동강 유역 유황에 미치는 영향분석, 수질보전 한국물환경학회지, 25(6), pp. 863-871.
  3. 수자원관리종합정보시스템(2011). http://www.wamis.go.kr/.
  4. 이아연, 김상단(2011). 낙동강 유역 환경유량에 대한 기후변화의 영향분석, 수질보전 한국물환경학회지, 27(3), pp. 273-285.
  5. 이재수(2008). 수문학, 구미서관, pp. 724
  6. 최대규, 이진희, 조덕준, 김상단(2010). 우리나라 기후 재현성을 중심으로 한 GCMs 평가, 수질보전 한국물환경학회지, 26(3), pp. 482-490.
  7. Carlson, T. N., Capehart, W. J., and Gillies, R. R. (1995). A New Look at the Simplified Method for Remote-Sensing of Daily Evapotranspiration, Remote Sensing of Environment, 54(2), pp. 161-167.
  8. Choi, D., Jun, H., Shin, H., Yoon, Y., and Kim, S. (2010). The Effect of Climate Change on Byeongseong Stream's Water Quantity and Quality, Desalination and Water Treatment, 19, pp 105-112.
  9. Choler, P., Sea, W., Briggs, P., Raupach, M., and Leuning, R. (2010). A Simple Ecohydrological Model Captures Essentials of Seasonal Leaf Dynamics in Semi-Arid Tropical Grasslands, Biogeosciences, 7, pp. 907-920.
  10. Choudhury, B. J., Ahmed, N. U., Idso, S. B., Reginato, R. J., and Daughtry, C. S. T. (1994). Relations Between Evaporation Coeffcients and Vegetation Indexes Studied by Model Simulations, Remote Sensing of Environment, 50(1), pp. 1-17.
  11. Gutman, G. and Ignatov, A. (1998). The Derivation of the Green Vegetation Fraction from NOAA/AVHRR Data for Use in Numerical Weather Prediction Models, International Journal of Remote Sensing, 19(8), pp. 1533-1543.
  12. IPCC (2007). Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambrideg University Press, United Kingdom and New York, NY, USA, pp. 996.
  13. Kang, S., Kim, S., Oh, S., and Lee, D. (2000). Predicting Spatial and Temporal Patterns of Soil Temperature Based on Topography, Surface Cover and Air Temperature, Forest Ecology and Management, 136, pp. 173-184.
  14. Kaushal, S. S., Likens, G. E., Jaworski, N. A., Pace, M. L., Sides, A. M., Seekell, D., Belt, K. T., Secor, D. H., and Wingate, R. L. (2010). Rising Stream and River Temperatures in the United States, Frontiers in Ecology and the Environment, 8(9), pp. 461-466.
  15. Land Processes Distributed Active Archive Center (2011). http://lpdaac.usgs.gov/.
  16. Morrill, J. C., Bales, R. C., and Conklin, M. H. (2005). Estimating Stream Temperature from Air Temperature: Implications for Future Water Quality, Journal of Environmental Engineering, 131(1), pp. 139-146.
  17. Nash, J. E. and Sutcliffe, J. V. (1970). River Flow Forecasting Through Conceptual Models: Part I - A Discussion of Principles, Journal of Hydrology, 10(3), pp. 282-290.
  18. Seaquist, J. W., Olsson, L., and Ardo, J. (2003). A Remote Sensing-Based Promary Production Model for Grassland Biomes, Ecological Modelling, 169(1), pp. 131-155.
  19. Soil Conservation Service (1975). Soil Taxonomy: a Basic System of Soil Classification for Making and Interpreting Soil Surveys, Agriculture Handbook, 436, pp. 768.
  20. Zheng, D., Hunt Jr, E. R., and Running, S. W. (1993). A Daily Soil Temperature Model Based on Air Temperature and Precipitation for Continental Applications, Climate Research, 2, pp. 183-191.