- Volume 44 Issue 3
DOI QR Code
Ensemble Daily Streamflow Forecast Using Two-step Daily Precipitation Interpolation
일강우 내삽을 이용한 일유량 시뮬레이션 및 앙상블 유량 발생
- Hwang, Yeon-Sang (Arkansas State University) ;
- Heo, Jun-Haeng (School of Civil and Environmental Engineering, Yonsei University) ;
- Jung, Young-Hun (School of Civiland Environmental Engineering, Yonsei University)
- Received : 2011.01.05
- Accepted : 2011.03.04
- Published : 2011.03.31
Input uncertainty is one of the major sources of uncertainty in hydrologic modeling. In this paper, first, three alternate rainfall inputs generated by different interpolation schemes were used to see the impact on a distributed watershed model. Later, the residuals of precipitation interpolations were tested as a source of ensemble streamflow generation in two river basins in the U.S. Using the Monte Carlo parameter search, the relationship between input and parameter uncertainty was also categorized to see sensitivity of the parameters to input differences. This analysis is useful not only to find the parameters that need more attention but also to transfer parameters calibrated for station measurement to the simulation using different inputs such as downscaled data from weather generator outputs. Input ensembles that preserves local statistical characteristics are used to generate streamflow ensembles hindcast, and showed that the ensemble sets are capturing the observed steamflow properly. This procedure is especially important to consider input uncertainties in the simulation of streamflow forecast.
- 황연상, 정영훈, 임광섭, 허준행 (2010). “강우-유출 모형 적용을 위한 강우 내삽법 비교 및 2단계 일강우 내삽법의 개발.” 한국수자원학회논문집, 한국수자원학회,제43권, 제12호, pp. 1083-1091.
- Blazkova, S., and Beven, K. (2002). “Flood frequency estimation by continuous simulation for a catchment treated as ungauged (with uncertainty).” Water Resources Research, Vol. 38, No. 8, pp. 1139-1151. https://doi.org/10.1029/2001WR000500
- Carpenter, T.M., and Georgakakos, K.P. (2004). “Impacts of parametric and radar rainfall uncertainty on the ensemble streamflow simulation of a distributedhydrologic model.” Journal of Hydrology, Vol. 298, No. 1-4, pp. 202-221. https://doi.org/10.1016/j.jhydrol.2004.03.036
- Carpenter, T.M., Georgakakos, K.P., and Sperfslagea, J.A. (2001). “On the parametric and NEXRAD-radar sensitivities of a distributed hydrologic modelsuitable for operational use.” Journal of Hydrology, Vol. 253, No. 1-4, pp. 169-193. https://doi.org/10.1016/S0022-1694(01)00476-0
- Clark, M.P., and Hay, L.E. (2004). “Use of mediumrange numerical weather prediction model output to produce forecasts of streamflow.” Journal of Hydrometeorology, Vol. 5, No. 1, pp. 15-32. https://doi.org/10.1175/1525-7541(2004)005<0015:UOMNWP>2.0.CO;2
- Clark, M.P., and Slater, A.G. (2005). “Probabilistic quantitative precipitation estimation in complex terrain.” Journal of Hydrometeorology, Vol. 7, No. 1, pp. 3-22.
- Day, G.N. (1985). “Extended streamflow forecasting using NWSRFS.” Journal of Water Resources Planning and Management, Vol. 111, No. 2, pp. 157-170. https://doi.org/10.1061/(ASCE)0733-9496(1985)111:2(157)
- Duan, Q., Gupta, V.K., and Sorooshian, S. (1993). “A shuffled complex evolution approach for effective and efficient global minimization.” Journal of Optimization Theory and Applications, Vol. 76, No. 3, pp. 501-521. https://doi.org/10.1007/BF00939380
- Gourley, J.J., and Vieux, B.E. (2005). “A method for evaluating the accuracy of quantitative precipitation estimates from a hydrologic modeling perspective.” Journal of Hydrometeorology, Vol. 6, No. 2, pp. 115-133. https://doi.org/10.1175/JHM408.1
- Hay, L.E., and McCabe, G.J. (2002). “Spatial variability in water-balance model performance in the conterminous united states.” Journal of the AmericanWater Resources Association, Vol. 38, No. 3, pp. 847-860. https://doi.org/10.1111/j.1752-1688.2002.tb01001.x
- Hay, L.E., Leavesley, G.H., Clark, M.P., Markstrom, S.L., Viger, R.J., and Umemoto, M. (2006). “Step wise, multiple objective calibration of a hydrologicmodel for a snowmelt dominated basin.” Journal of the American Water Resources Association, Vol. 42, No. 4, pp. 877-890. https://doi.org/10.1111/j.1752-1688.2006.tb04501.x
- 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. pp. 522.
- Leavesley, G.H., Lichty, R.W., Troutman, B.M., and Saindon, L.G. (1983). Precipitation-runoff modeling system: user's manual, U.S. Geological Survey Water-Resources Investigations 83-4238, pp. 207.
- Leavesley, G.H., Restrepo, P.J., Markstrom, S.L., Dixon, M., and Stannard, L.G. (1996). The modular modeling system - MMS: user's manual, Open file report pp. 96-151, U.S. Geological Survey.
- Nash, J.E., and Sutcliffe, J.V. (1970). “River flow forecasting through conceptual models part I-a discussion of principles.” Journal of Hydrology, Vol. 10, No. 3, pp. 282-290. https://doi.org/10.1016/0022-1694(70)90255-6
- 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.
- 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
- Xia, Y., Pitman, A.J., H.V. Gupta, M.L., Henderson-Sellers, A., and Bastidas, L.A. (2002). “Calibrating a land surface model of varying complexity using multicriteria methods and the cabauw dataset.” Journal of Hydrometeorology, Vol. 3, No. 2, pp. 181-194. https://doi.org/10.1175/1525-7541(2002)003<0181:CALSMO>2.0.CO;2