Browse > Article
http://dx.doi.org/10.3741/JKWRA.2014.47.9.825

Development of Stochastic Downscaling Method for Rainfall Data Using GCM  

Kim, Tae-Jeong (Department of Civil Engineering, Chonbuk National University)
Kwon, Hyun-Han (Department of Civil Engineering, Chonbuk National University)
Lee, Dong-Ryul (Korea Institute of Construction Technology, Water Resources Research Division)
Yoon, Sun-Kwon (Climate Change Research Team, Climate Research Department, APEC Climate Center)
Publication Information
Journal of Korea Water Resources Association / v.47, no.9, 2014 , pp. 825-838 More about this Journal
Abstract
The stationary Markov chain model has been widely used as a daily rainfall simulation model. A main assumption of the stationary Markov model is that statistical characteristics do not change over time and do not have any trends. In other words, the stationary Markov chain model for daily rainfall simulation essentially can not incorporate any changes in mean or variance into the model. Here we develop a Non-stationary hidden Markov chain model (NHMM) based stochastic downscaling scheme for simulating the daily rainfall sequences, using general circulation models (GCMs) as inputs. It has been acknowledged that GCMs perform well with respect to annual and seasonal variation at large spatial scale and they stand as one of the primary sources for obtaining forecasts. The proposed model is applied to daily rainfall series at three stations in Nakdong watershed. The model showed a better performance in reproducing most of the statistics associated with daily and seasonal rainfall. In particular, the proposed model provided a significant improvement in reproducing the extremes. It was confirmed that the proposed model could be used as a downscaling model for the purpose of generating plausible daily rainfall scenarios if elaborate GCM forecasts can used as a predictor. Also, the proposed NHMM model can be applied to climate change studies if GCM based climate change scenarios are used as inputs.
Keywords
Markov chain model; downscaling; Non-stationarity; GCM;
Citations & Related Records
Times Cited By KSCI : 8  (Citation Analysis)
연도 인용수 순위
1 Moon, Y.-I., and Cha, Y.-I. (2004). "Simulation of Daily precipitation data using Nonhomegeneous markov chain model I-Theory." Journal of the Korean Society of Civil Engineers, Vol. 24, No. 5B, pp. 431-435.
2 Muhammad, Z.H., Asaad, Y.S., and Bruce, W.M. (2011). "Comparison of SDSM and LARS-WG for simulation and downscaling of extreme precipitation events in a watershed." Stochastic Environmental Research and Risk Assessment, Vol. 25, No. 4, pp. 475-484.   DOI
3 Nord, J. (1975). "Some applications of Markov chains." Proceedings Fourth Conference on Probability and Statistics in Atmospheric Science, Tallahas, pp. 125-130.
4 Pan, W. (2001). "Akaike's information criterion in generalized estimating equations." Biometrics, Vol. 57, No. 1, pp. 120-125.   DOI   ScienceOn
5 Stedinger, J.R., and Crainiceanu, C.M. (2000) "Climate variability and flood-risk management" Risk-based decision making in Water Resources IX, Proceedings of the 9th Conference, pp. 77-86.
6 Strupczewski, W.G., Singh, V.P., and Feluch, W. (2001). "Non-stationary approach to at-site flood frequency modelling 1. Maximum likelihood estimation." Journal of Hydrology, Vol. 248, No. 1, pp. 123-142.   DOI   ScienceOn
7 Vrac, M., and Naveau, P. (2007). "Stochastic downscaling of precipitation: from dry event to heavy rainfalls." Water Resources. Research, Vol. 43, No. 7, DOI:10.1029/2006WR005308.   DOI   ScienceOn
8 Wilks, D.S., and Wilby, R.L. (1999), "The weather generation game : a review of stochastic weather models." Progress in Physical Geography, Vol. 23, No. 3, pp. 329-357.   DOI
9 Willems, P., and Vrac, M. (2011). "Statistical precipitation downscaling for small-scale hydrological impact investigations of climate change." Journal ofHydrology, Vol. 402, No. 3, pp. 193-205.   DOI   ScienceOn
10 Yang, Y., and Zou, H. (2004). "Combining time series models for forecasting." Journal of Forecasting, Vol. 20, No. 1, pp. 69-84.   DOI   ScienceOn
11 Yonas, B.D., and Paulin, C. (2006). "Temporal neural networks for downscaling climate variability and extremes." Neural Networks, Vol. 19, No. 2, pp. 135-144.   DOI   ScienceOn
12 Hurvich, C.M., and Tsai, C.-L. (1998). "A crossvalidatory AIC for hard wavelet thresholding in spatially adaptive function estimation." Biometrika, Vol. 85, No. 3, pp. 701-710.   DOI   ScienceOn
13 Akaike, H. (1974). "A new look at the statistical model identification." IEEE Transactions on Automatic Control, Vol. 19, No. 6, pp. 716-723.   DOI
14 Baum, L.E., Petrie, T., Soules, G., and Weiss, N. (1970). "A maximization technique occurring in the statistical analysis of probabilistic functions of markov chain." The Annals of Mathematical Statistics, Vol. 41, No. 1, pp. 164-174.   DOI   ScienceOn
15 Dempster, A., Laird, N., and Rubin, D. (1977). "Maximum likelihood from incomplete data via the EM algorithm." Journal of the Royal Statistical Society, Vol. 39, No. 1, pp. 1-38.
16 Faraway, J., and Chatfield, C. (1998). "Time series forecasting with neural networks: a comparative study using the airline data." Journal of the Royal Statistical Society, Vol. 47, No. 2, pp. 231-250.
17 Greene, A.M., Robertson, A.W., Smyth, R., and Triglia, S. (2011). "Downscaling projections of Indian monsoon rainfall using a non-homogeneous hidden markov model." Journal of the Royal Meteorological Society, Vol. 137, No. 655, pp. 347-359.   DOI   ScienceOn
18 Khalil, A.F., Kwon, H.-H., Lall, U., and Kaheil, Y.H. (2010). "Predictive downscaling based on non-homogeneous hidden markov models." Hydrological Sciences Journal, Vol. 55, No. 3, pp. 333-350.   DOI   ScienceOn
19 Khaliq, M.N., Ouardab, T.B.M.J., Ondob, J.-C., Gachona, P., and Bobeeb, B. (2006). "Frequency analysis of a sequence of dependent and/or non-stationary hydrometeorological observations: A review." Journal of Hydrology, Vol. 329, No. 3, pp. 534-552.   DOI   ScienceOn
20 Kim, T.-J. (2014). Development ofNonstationary Spatio-Temporal Downscaling Technique Using General Circulation Model Multi-Model Ensemble. Master's Thesis, Chonbuk National University, Jeonju, Jeollabuk, Republic of Korea.
21 Kim, T.-J., Kwon, H.-H., and Kim, K.-Y. (2014). "Assessment of typhoon trajectories and Synoptic pattern based on probabilistic cluster analysis for the typhoons affecting the Korean peninsula." Journal of Korea Water Resources Association, Vol. 47, No. 4, pp. 385-396.   과학기술학회마을   DOI   ScienceOn
22 Kwon, H.-H., Brown, C., and Lall, U. (2008) "Climate informed flood frequency analysis and prediction in Montana using hierarchical Bayesian modelling." Geophysical Research Letters, Vol. 35, No. 5, DOI: 10.1029/2007GL032220.   DOI   ScienceOn
23 Kumar, D., Arya, D.S., Murumkar, A.R., and Rahman, M.M. (2014). "Impact of climate change on rainfall in Northwestern Bangladesh using multi-GCMensembles." International Journal of Climatology, Vol. 34, No. 3, 1395-1404.   DOI   ScienceOn
24 Kwon, H.-H., and Kim, B.-S. (2009). "Development of statistical downscaling model using nonstationary markov chain." Journal of Korean Water Resources Association, Vol. 42, No. 3 pp. 213-225.   과학기술학회마을   DOI   ScienceOn
25 Kwon, H.-H., and So, B.-J. (2011). "Development of Daily Rainfall Simulation Model Using Piecewise Kernel-Pareto Continuous Distribution." Journal of Korean Society of Civil Engineers, Vol. 31, No. 3, pp. 277-284.   과학기술학회마을
26 Kwon, H.-H., Brown, C., Xu, K., and Lall, U. (2009). "Seasonal and annual maximum streamflow forecasting using climate information: application to the Tree Gorgers Dam in the Yangtze basin, China." Hydrological Sciences Journal, Vol. 54, No. 3, pp. 582-595.   DOI   ScienceOn
27 Kwon, H.-H., Kim, T.-J. Hwang, S.-H., and Kim, T.-W. (2013). "Development of daily rainfall simulation model based on homogeneous hidden markov chain." Journal of Korean Society of Civil Engineers, Vol. 33, No. 5, pp. 1861-1870.   과학기술학회마을   DOI   ScienceOn
28 Kwon, H.-H., Moon, Y.-I., Choi, B.-G., and Yoon, Y.-N. (2005). "Optimum size analysis for Dam rehabilitation using reliability analysis." Journal of Korean Water Resources Association, Vol. 38, No. 2, pp. 97-110.   과학기술학회마을   DOI
29 Lee, J.-J., Kwon, H.-H., and Kim. T.-W. (2010b) "Concept of Trend Analysis of Hydrologic Extreme Variables and Nonstationary frequency analysis." Journal of the Korean Society of Civil Engineers, Vol. 30, No. 4B, pp. 389-397.   과학기술학회마을
30 Lee, J.-J., Kwon, H.-H., and Hwang. K.-N. (2010a) "Concept of Seasonality Analysis of Hydrologic Extreme Variables and Design Rainfall Estimation using Nonstationary frequency analysis." Journal of Korean Water Resources Association, Vol. 43, No. 8, pp. 733-745.   과학기술학회마을   DOI   ScienceOn
31 Li. P.-H., Kwon, H.-H., Sum, L., Lall, U., and Kao, J.-J (2010) "A modified support vector machine based prediction model on streamflow at the Shihmen Reservoir, Taiwan." International Journal of Climatology, Vol. 30, No. 8, pp. 1256-1268.
32 Lim, Y.-K, Cocke, S., Shin, D.W., Schoof, J.T., Larow, T.E., and O'Brien, J.J. (2010). "Downscaling largescale NCEP CFS to resolve fine-scale seasonal precipitation and extremes for the crop growing seasons over the southeastern United State." Journal of Climate Dynamics, Vol. 35, No. 35, pp. 449-471.   DOI
33 Malcolm. M.R., Cawley, G.C., Harpham, C., Wilby, R.L., and Clare, M.G. (2006). "Downscaling Heavy Precipitation over The United Kingdom: A Comparison of Dynamical and statistical methods and their future scenarios." International Journal of Climatology, Vol. 26, No. 10, pp. 1397-1415.   DOI   ScienceOn
34 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 x $CO_2$ Experiments." Journal of Geophysical Research, Vol. 95, No. D12, pp. 20469-20490.   DOI
35 Milly, P.C.D., Dunne, A.K., and Vecchia, A.V. (2005). "Global pattern of trends in streamflow and water availability in a changing climate." Nature, Vol. 438, pp. 347-350.   DOI   ScienceOn