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Impact of Vegetation Heterogeneity on Rainfall Excess in FLO-2D Model : Yongdam Catchment

용담댐 유역에서 식생 이질성이 FLO-2D 유량 산정에 미치는 영향

  • Song, Hojun (Department of Civil Enginerring, Daegu University) ;
  • Lee, Khil-Ha (Department of Civil Enginerring, Daegu University)
  • 송호준 (대구대학교 공과대학 토목공학과) ;
  • 이길하 (대구대학교 공과대학 토목공학과)
  • Received : 2019.01.21
  • Accepted : 2019.02.21
  • Published : 2019.02.28

Abstract

Two main sources of data, meteorological data and land surface characteristics, are essential to effectively run a distributed rainfall-runoff model. The specification and averaging of the land surface characteristics in a suitable way is crucial to obtaining accurate runoff output. Recent advances in remote sensing techniques are often being used to derive better representations of these land surface characteristics. Due to the mismatch in scale between digital land cover maps and numerical grid sizes, issues related to upscaling or downscaling occur regularly. A specific method is typically selected to average and represent the land surface characteristics. This paper examines the amount of flooding by applying the FLO-2D routing model, where vegetation heterogeneity is manipulated using the Manning's roughness coefficient. Three different upscaling methods, arithmetic, dominant, and aggregation, were tested. To investigate further, the rainfall-runoff model with FLO-2D was facilitated in Yongdam catchment and heavy rainfall events during wet season were selected. The results show aggregation method provides better results, in terms of the amount of peak flow and the relative time taken to achieve it. These rwsults suggest that the aggregation method, which is a reasonably realistic description of area-averaged vegetation nature and characteristics, is more likely to occur in reality.

Keywords

Rainfall-runoff;Land cover;FLO-2D;Aggregation

Acknowledgement

Supported by : 한국연구재단

References

  1. Koster, R. D., Suarez, M. D., 1992, A Comparative analysis of two land surface heterogeneity representations, Journal of Climate, 5, 1379-1390. https://doi.org/10.1175/1520-0442(1992)005<1379:ACAOTL>2.0.CO;2
  2. Lee, K., 2008, Integrated remotely sensed data uisng a simple vegetation parameter aggregation method applicable to a distributed rainfall-runoff model, Journal of Hydrologic Engineering-ASCE, 13(4), 236-241. https://doi.org/10.1061/(ASCE)1084-0699(2008)13:4(236)
  3. Maidment, D. R., 1993, Handbook of hydrology, McGraw-Hill, New York, USA.
  4. McNaughton, K. G., 1993, Effective stomatal and boundary layer resistances of heterogeneous surfaces. Plant, Cell & Environment, 17(1), 1061-1068.
  5. Nash, J. E., Sutcliffe, J. V., 1970, River flow forecasting through conceptual models part I-A discussions of Principles, Journal of Hydrology, 10(3), 282-290. https://doi.org/10.1016/0022-1694(70)90255-6
  6. Raupach, M. R., 1995, Vegetation-atmosphere interaction and surface conductance at leaf, canopy, and regional scales, Agricultural Forest Meteorology, 73(3), 151-179. https://doi.org/10.1016/0168-1923(94)05071-D
  7. Shuttleworth, W. J., 1998, Combining remotely sensed data using aggregation algorithms, Hydrology and Earth System Science, 2(2-3), 149-158. https://doi.org/10.5194/hess-2-149-1998
  8. Vieux, B. E., 1994, Distributed hydrologic modeling using GIS, Kluwer Academic, Dordrecht, The Netherlands.