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Stochastic disaggregation of daily rainfall based on K-Nearest neighbor resampling method
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 Title & Authors
Stochastic disaggregation of daily rainfall based on K-Nearest neighbor resampling method
Park, HeeSeong; Chung, GunHui;
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 Abstract
As the infrastructures and populations are the condensed in the mega city, urban flood management becomes very important due to the severe loss of lives and properties. For the more accurate calculation of runoff from the urban catchment, hourly or even minute rainfall data have been utilized. However, the time steps of the measured or forecasted data under climate change scenarios are longer than hourly, which causes the difficulty on the application. In this study, daily rainfall data was disaggregated into hourly using the stochastic method. Based on the historical hourly precipitation data, Gram Schmidt orthonormalization process and K-Nearest Neighbor Resampling (KNNR) method were applied to disaggregate daily precipitation into hourly. This method was originally developed to disaggregate yearly runoff data into monthly. Precipitation data has smaller probability density than runoff data, therefore, rainfall patterns considering the previous and next days were proposed as 7 different types. Disaggregated rainfall was resampled from the only same rainfall patterns to improve applicability. The proposed method was applied rainfall data observed at Seoul weather station where has 52 years hourly rainfall data and the disaggregated hourly data were compared to the measured data. The proposed method might be applied to disaggregate the climate change scenarios.
 Keywords
Gram-Schmidt orthonormalization process;K-Nearest Neighbor Resampling (KNNR) method;Rainfall pattern;Urban runoff;Stochastic disaggregation of rainfall data;
 Language
Korean
 Cited by
 References
1.
Kyoung, M.S., Sivakumar, B., Kim, H.S., and Kim, B.S., (2008). "Chaotic Disaggregation of Daily Rainfall from the Climate Change Scenarios" Proceedings of Korea Water Resources Association, pp. 178-183.

2.
Kim, B.S., Kim, B.K., Kyung, M.S., and Kim, H.S., (2008). "Impact Assessment of Climate Change on Extreme Rainfall and I-D-F Analysis." Journal of Korea Water Resources Association, Vol. 41, No. 4, pp. 379-394. crossref(new window)

3.
Kim, T.J., Kwon, H.H., Lee, D.R., and Yoon, S.K. (2014). "Development of Stochastic Downscaling Method for Rainfall Data Using GCM." Journal of Korea Water Resources Association, Vol. 47, No. 9, pp. 825-838. crossref(new window)

4.
Kumar, D.N., Lall, U., and Peterson, M.R. (2000). "Multisite disaggregation of monthly to daily streamflow." Water Resources Research, Vol. 36, No. 7, pp. 1823-1833. crossref(new window)

5.
Lall, U., and Sharma, A. (1996). "A nearest neighbor bootstrap for resampling hydrologic time series." Water Resources Research, Vol. 32, No. 3, pp. 679-693. crossref(new window)

6.
Lee, T., Salas, J.D., and Prairie, J. (2010). "An enhanced nonparametric streamflow disaggregation model with genetic algorithm." Water Resources Research, Vol. 46, W08545.

7.
Lee, T., and Jeong, C., (2014). "Nonparametric statistical temporal downscaling of daily precipitation to hourly precipitation and implications for climate change scenarios." Journal of Hydrology, Vol. 510, pp. 182-196. crossref(new window)

8.
Lee, T., Park, T., Lee, H., and Jeong, C. (2014). "Temporal Downscaling of Precipitation from Daily to Hourly Based on Nonparametric Approach: Assessment of the Climate Change Impacts on the Hourly Precipitation for the Gyeongnam Region." Journal of Korean Society of Hazard Mitigation, Vol. 14, No. 4, pp. 301-308. crossref(new window)

9.
Liou, E.Y. (1970). OPSET-Program for computerized selection of watershed parameter values of the Stanford watershed model, Research Report No. 34, University of Kentucky Water Resources Institute, Lexington, KY.

10.
Matalas, N.C. (1967). "Mathematical assessment of synthetic hydrology." Water Resources Research, Vol. 3, No. 4, pp. 937-946. crossref(new window)

11.
Mandelbrot, B.B., and Wallis, J.R. (1968). "Noah, Josep, and operational hydrology." Water Resources Research, Vol. 4, No. 5, pp. 909-918. crossref(new window)

12.
Mandelbrot, B.B., and Wallis, J.R. (1969a). "Computer experiments with fractional Gaussian noises, 1. averages and variances." Water Resources Research, Vol. 5, No. 1, pp. 228-241. crossref(new window)

13.
Mandelbrot, B.B., and Wallis, J.R. (1969b). "Computer experiments with fractional Gaussian noises, 2. Rescaled ranges and spectra." Water Resources Research, Vol. 5, No. 1, pp. 242-259. crossref(new window)

14.
Mandelbrot, B.B., and Wallis, J.R. (1969c). "Some longrun properties of geophysical records." Water Resources Research, Vol. 5, No. 2, pp. 321-340. crossref(new window)

15.
Ormsbee (1989). "Rainfall disaggregation model for continuous hydrologic modeling." Journal of Hydraulic Engineering, Vol. 115, pp. 507-525. crossref(new window)

16.
Prairie, J.R., Rajagopalan, B. Lall, U., and Fulp, T. (2007). "A stochastic nonparametric technique for space-time disaggregation of streamflows." Water Resources Research, Vol. 43, W03432, doi:10.1029/2005WR004721. crossref(new window)

17.
Rodriguez-Iturbe, I., Mejia, J.M., and Dawdy, D.R. (1972). "Streamflow simulation, 1, A new look at markovian models, fractional Gaussian noise and crossing theory." Water Resources Research, Vol. 8, No. 4, pp. 921-930. crossref(new window)

18.
Sharma, A., and O'Neill, R. (2002). "A nonparametric approach for representing interannual dependence in monthly streamflow." Water Resources Research, Vol. 138, No. 7, pp. 5-1.

19.
Tarboton, D.G., Sharma, A., and Lall, U. (1998). "Disaggregation procedures for stochastic hydrology based on nonparametric density estimation." Water Resources Research, Vol. 34, No. 1, pp. 107-119. crossref(new window)

20.
Valencia, D.R., and Schaake, J.C. (1973). "Disaggregation process in stochastic hydrology." Water Resources Research, Vol. 9, No. 3, pp. 580-585. crossref(new window)