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Multi-site Daily Precipitation Generator: Application to Nakdong River Basin Precipitation Gage Network  

Keem, Munsung (Department of Environmental System Engineering, Pukyong National University)
Ahn, Jae Hyun (Department of Civil Engineering, Seokyeong University)
Shin, Hyun Suk (Department of Civil Engineering, Pusan National University)
Han, Suhee (Department of Environmental System Engineering, Pukyong National University)
Kim, Sangdan (Department of Environmental System Engineering, Pukyong National University)
Publication Information
Journal of Korean Society on Water Environment / v.24, no.6, 2008 , pp. 725-740 More about this Journal
Abstract
In this study a multi-site daily precipitation generator which generates the precipitation with similar spatial correlation, and at the same time, with conserving statistical properties of the observed data is developed. The proposed generator is intended to be a tool for down-scaling the data obtained from GCMs or RCMs into local scales. The occurrences of precipitation are simultaneously modeled in multi-sites by 2-parameter first-order Markov chain using random variables of spatially correlated while temporally independent, and then, the amount of precipitation is simulated by 3-parameter mixed exponential probability density function that resolves the issue of maintaining intermittence of precipitation field. This approach is applied to the Nakdong river basin and the observed data are daily precipitation data of 19 locations. The results show that spatial correlations of precipitation series are relatively well simulated and statistical properties of observed precipitation series are simulated properly.
Keywords
Climate change; Correlation; Precipitation; Weather generator;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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1 Bardossy, A. and Plate, E. J. (1992). Space-time model for daily rainfall using atmospheric circulation patterns. Water Resources Research, 28, pp. 1247-1259   DOI
2 Bras, R. and Rodriguez-Iturbe, I. (1985). Random Functions and Hydrology, Addison-Wesley, Reading, MA
3 Gates, W. L., Henderson-Sellers, A., Boer, G. J., Folland, C. K., Kitoh, A., McAvaney, J. B., Semazzi, F., Smith, N., Weaver, A. J., and Zeng, Q. C. (1996). Climate modelsevaluation. Climate change 1995, J. T. Houghton, L. G. Filho, B. A. Callander, N. Harris, A. Kattenburg and K. Maskell (eds.), Cambridge University Press, Cambridge, pp. 229-284
4 Mearns, L. O., Schneider, S. H., Thompson, S. L., and McDaniel, L. R. (1990). Analysis of climate variability in general circulation models: comparison with observations and changes in variability in $2{\times}CO_{2}$ experiments. J. Geophy Res, 95, pp. 20469-20490   DOI
5 Semenov, M. A. and Barrow, E. M. (1997). Use of a stochastic weather generator in the development of climate change scenarios. Clim Change, 35, pp. 397-414   DOI   ScienceOn
6 Smith, J. A. and Karr, A. F. (1985). Parameter estimation for a model of space-time rainfall. Water Resources Research, 21, pp. 1251-1257   DOI
7 Smith, R. E. and Schreiber, H. A. (1974). Point processes of seasonal thunderstorm rainfall. Part 2: rainfall depth probabilities. Water Resources Research, 10, pp. 418-423   DOI
8 Todorovic, P. and Woolhiser, D. A. (1975). A stochastic model of n-day precipitation. Journal of Applied Meteorology, 14, pp. 17-24   DOI
9 Wigley, T. M. L., Jones, P. D., Briffa, K. R., and Smith, G. (1990). Obtaining sub-grid-scale information from coarseresolution general circulation model output. J Geophys Res, 95, pp. 1943-1953   DOI
10 Wilby, R. L. and Wigley, T. M. L. (1997). Downscaling general circulation model output: a review of methods and limitations. Prog Phys Geogr, 21, pp. 530-548   DOI
11 Wilks, D. S. (1992). Adapting stochastic weather generation algorithms for climate change studies. Clim Change, 22, pp. 67-84   DOI
12 Woolhiser, D. A. and Pegram, G. S. (1979). Maximum likelihood estimation of Fourier coefficients to describe seasonal variation of parameters in stochastic daily precipitation models. Journal of Applied Meteorology, 18, pp. 34-42   DOI
13 Katz, R. W. (1977). Precipitation as a chain-dependent process. Journal of Applied Meteorology, 16, pp. 671-676   DOI
14 Grotch, S. L. and MacCracken, M. C. (1991). The use of general circulation models to predict regional climate change. J. Clim., 4, pp. 286-303   DOI
15 Hanson, C. L., Cumming, K. A., Woolhiser, D. A., and Richardson, C. W. (1994). Microcomputer program for daily weather simulations in the contiguous United States. USDA/ ARS, ARS-114, pp. 38
16 von Storch, H., Zorita, E. and Cubasch, U. (1993). Downscaling of global climate change estimates to regional scales: an application to Iberian rainfall in wintertime. J. Clim, 6, pp. 1161-1171   DOI   ScienceOn
17 Bras, R. and Rodriguez-Iturbe, I. (1976). Rainfall generation: a nonstationary time varying multidimensional model. Water Resources Research, 12, pp. 450-456   DOI
18 Kattenberg, A., Giorgi, F., Grassl, H., Meehl, G. A., Mitchell, J. F. B., Stouffer, R. J., Tokioka, T., Weaver, A. J., and Wigley, T. M. L. (1996). Climate models-projections of future climate. Climate change 1995, J. T. Houghton, L. G. Filho, B. A. Callander, N. Harris, A. Kattenburg, and K. Maskell (eds.), Cambridge University Press, Cambridge, pp. 285-357
19 Waymire, E., Gupta, V. K., and Rodriguez-Iturbe, I. (1984). A spectral theory of rainfall at the meso-$\beta$ scale. Water Resources Research, 20, pp. 1453-1465   DOI
20 Waymire, E. and Gupta, V. K. (1981). The mathematical structure of rainfall representations. 1. A review of the stochastic rainfall models. Water Resources Research, 17, pp. 1261-1272   DOI
21 Lettenmaier, D. (1995). Stochastic modeling of precipitation with applications to climate model downscaling. Analysis of Climate Variability: Application of Statistical Techniques, H. von Storch and A. Navarra (eds.), Springer-Verlag, Berlin, pp. 197-212
22 Giorgi, F. and Mearns, L. O. (1991). Approaches to simulation of regional climate change: a review. Rev. Geophys., 29, pp. 191-216   DOI
23 Woo, M. K. (1992). Application of stochastic simulation to climate-change studies. Clim Change, 20, pp. 313-330   DOI
24 Wilks, D. S. (1998). Multisite generalization of a daily stochastic precipitation generation model. Journal of Hydrology, 210, pp. 178-191   DOI   ScienceOn
25 Foufoula-Georgiou, E. and Lettenmaier, D. P. (1987). A Markov renewal model for rainfall occurrences. Water Resources Research, 23, pp. 875-884   DOI
26 Burger, G. (1996) Expanded downscaling for generating local weather scenarios. Clim. Res., 7, pp. 111-128   DOI
27 Stern, R. D. and Coe, R. (1984). A model fitting analysis of daily rainfall data. Journal of Royal Statistical Society, A147, pp. 1-34
28 Woolhiser, D. A. (1992). Modeling daily precipitation-progress and problems. Statistics in the Environmental and Earth Sciences, A. T. Walden and P. Guttorp (eds.), John Wiley, New York, pp. 71-89
29 Cox, D. R. and Isham, V. (1988). A simple spatial-temporal model of rainfall. Proceedings of the Royal Society London, A415, pp. 317-328
30 Crosby, D. S. and Maddock, T. III. (1970). Estimating coefficients of a flow generator for monotone samples of data. Water Resources Research, 6, pp. 1079-1086   DOI