DOI QR코드

DOI QR Code

Comparison of Daily Rainfall Interpolation Techniques and Development of Two Step Technique for Rainfall-Runoff Modeling

강우-유출 모형 적용을 위한 강우 내삽법 비교 및 2단계 일강우 내삽법의 개발

  • Received : 2010.11.04
  • Accepted : 2010.12.01
  • Published : 2010.12.31

Abstract

Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. However, widely used estimation schemes fail to describe the realistic variability of daily precipitation field. We compare and contrast the performance of statistical methods for the spatial estimation of precipitation in two hydrologically different basins, and propose a two-step process for effective daily precipitation estimation. The methods assessed are: (1) Inverse Distance Weighted Average (IDW); (2) Multiple Linear Regression (MLR); (3) Climatological MLR; and (4) Locally Weighted Polynomial Regression (LWP). In the suggested simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before applying IDW scheme (one of the local scheme) to estimate the amount of precipitation separately on wet days. As the results, the suggested method shows the better performance of daily rainfall interpolation which has spatial differences compared with conventional methods. And this technique can be used for streamflow forecasting and downscaling of atmospheric circulation model effectively.

분포형 수문 모형의 일강우 입력 자료는 불가피하게 불규칙하고 밀도가 낮은 관측망에서 기록된 값을 내삽해 사용하게 되나, 흔히 사용되는 대부분의 내삽법들은 실제 일강우의 다양한 공간적 분포를 잘 재현하지 못하는 문제가 있다. 본 연구에서는 널리 사용되는 다섯 가지의 강우 내삽 방법을 두개의 유역에 사용하여 비교하고 실제 공간적 분포를 보다 잘 나타낼 수 있는 2단계 내삽법을 제안하였다. 비교에 사용된 내삽법은 (1) 역가중치 방법(IDW), (2) 다중회귀분석 (MLR), (3) 월강우를 이용한 다중회귀분석법(CMLR), (4) 국지가중치 다중회귀분석(LWP) 등이다. 보다 향상된 내삽을 위한 2단계 내삽법은 먼저 로지스틱 회귀분석으로 강우-비강우 지역을 구분하고 강우 지역에서만 기존의 내삽법을 적용하여 강우량을 구하는 방법이다. 기존 방법과의 비교결과 공간적인 편차가 심한 일강우의 특성을 2단계 내삽법에서 잘 표현하고 있는 것으로 나타났다. 제안된 방법은 수문모형에의 적용뿐만 아니라 유출량의 예보 및 대기 순환 모형의 다운 스케일링에도 효과적으로 사용될 수 있을 것으로 기대된다.

Keywords

References

  1. Chua, S.H., and Bras, R. (1982). “Optimal estimators of mean areal precipitation in regions of orographic influences.” Journal of Hydrology, Vol. 57, No. 1-2, pp. 23-48. https://doi.org/10.1016/0022-1694(82)90101-9
  2. Clark, M., Gangopadhyay, S., Hay, L., Rajagopalan, B., and Wilby, R. (2004). “The Schaake shuffle: A method for reconstructing space-time variability in forecasted precipitation and temperature fields.” Journal of Hydrometeorology, Vol. 5, No. 1, pp. 243-262. https://doi.org/10.1175/1525-7541(2004)005<0243:TSSAMF>2.0.CO;2
  3. Dingman, L.S. (1994). Physical hydrology, Macmillan Publishing Company, New York.
  4. Dirks, K.N., Hay, J.E., Stow, C.E., and Harris, D. (1998). “High-resolution studies of rainfall on Norfolk Island part II: Interpolation of rainfall data.” Journal of Hydrology, Vol. 208, No. 3-4, pp. 187-193. https://doi.org/10.1016/S0022-1694(98)00155-3
  5. Dodson, R., and Marks, D. (1997). “Daily air temperature interpolated at high spatial resolution over a large mountainous region.” Climate Research, Vol. 8, No. 1, pp. 1-20. https://doi.org/10.3354/cr008001
  6. Goodale, C.L., Aber, J.D., and Ollinger, S.V. (1998). “Mapping monthly precipitation, temperature, and solar radiation for ireland with polynomial regression and a digital elevation model.” Climate Research, Vol. 10, No. 1, pp. 35-49. https://doi.org/10.3354/cr010035
  7. Hartkamp, A.D., De Beurs, K., Stein, A., and White, J.W. (1999). Interpolation techniques for climate variables. Cimmyt, Mexico, D.F.
  8. Hay, L.E., and Mccabe, G.J. (2002). “Spatial variability in water-balance model performance in the conterminous United States.” Journal of American Water Resources Association, Vol. 38, No. 3, pp. 847-860. https://doi.org/10.1111/j.1752-1688.2002.tb01001.x
  9. Helsel, D.R., and Hirsch, R.M. (2002). Statistical methods in water resources, Techniques of Water-Resources Investigations Book 4, Chapter A3, U.S. Geological Survey.
  10. Kurtzman, D., and Kadmon, R. (1999). “Mapping of temperature variables in Israel: A comparison of different interpolation methods.” Climate Research, Vol. 13, No. 1, pp. 33-43. https://doi.org/10.3354/cr013033
  11. Lanza, L.G., Ramirez, J.A., and Todini, E. (2001). “Stochastic rainfall interpolation and downscaling.” Hydrology and Earth System Sciences, Vol. 5, No. 2, pp. 139-143. https://doi.org/10.5194/hess-5-139-2001
  12. Loader, C. (1999) Local regression & likelihood, Springer- Verlag, New York.
  13. Parajka, J. (2000). “Estimation of average basin precipitation for mountain basins in Western Tatra mountains.” ERB200-Monitoring and Modelling Cachment Water Quantity and Quality, September, 27-29. Ghent: Belgium.
  14. Rajagopalan, B., and Lall, U. (1998). “Locally weighted polynomial estimation of spatial precipitation.” Journal of Geographic Information and Decision Analysis, Vol. 2, No. 2, pp. 44-51.
  15. Reek, T., Doty, S.R., and Owen, T.W. (1992). “A deterministic approach to the validation of historical daily temperature and precipitation data from the cooperative network.” Bulletin of the American Meteorological Society, Vol. 73, No. 6, pp. 753-762. https://doi.org/10.1175/1520-0477(1992)073<0753:ADATTV>2.0.CO;2
  16. Thiessen, A.H. (1911). “Precipitation averages for large areas. A Subsection in the Climatology Data for July 1911, District No. 10, Great Basin.” Monthly Weather Review, July, pp. 1082-1084.
  17. Todd, D.K., and Mays, L.W. (2005). Groundwater hydrology, 3rd Ed., John Wiley and Sons, New York.

Cited by

  1. Analysis of Spatial Precipitation Field Using Downscaling on the Korean Peninsula vol.46, pp.11, 2013, https://doi.org/10.3741/JKWRA.2013.46.11.1129