Browse > Article
http://dx.doi.org/10.3741/JKWRA.2014.47.8.671

Intercomparison of Change Point Analysis Methods for Identification of Inhomogeneity in Rainfall Series and Applications  

Lee, Sangho (Department of Civil Engineering, Pukyoung National University)
Kim, Sang Ug (Department of Civil Engineering, Kangwon National University)
Lee, Yeong Seob (Department of Civil Engineering, Kangwon National University)
Sung, Jang Hyun (Ministry of Land, Infrastructure and Transport)
Publication Information
Journal of Korea Water Resources Association / v.47, no.8, 2014 , pp. 671-684 More about this Journal
Abstract
Change point analysis is a efficient tool to understand the fundamental information in hydro-meteorological data such as rainfall, discharge, temperature etc. Especially, this fundamental information to change points to future rainfall data identified by reasonable detection skills can affect the prediction of flood and drought occurrence because well detected change points provide a key to resolve the non-stationary or inhomogeneous problem by climate change. Therefore, in this study, the comparative study to assess the performance of the 3 change point detection skills, cumulative sum (CUSUM) method, Bayesian change point (BCP) method, and segmentation by dynamic programming (DP) was performed. After assessment of the performance of the proposed detection skills using the 3 types of the synthetic series, the 2 reasonable detection skills were applied to the observed and future rainfall data at the 5 rainfall gauges in South Korea. Finally, it was suggested that BCP (with 0.9 posterior probability) could be best detection skill and DP could be reasonably recommended through the comparative study. Also it was suggested that BCP (with 0.9 posterior probability) and DP detection skills to find some change points could be reasonable at the North-eastern part in South Korea. In future, the results in this study can be efficiently used to resolve the non-stationary problems in hydrological modeling considering inhomogeneity or nonstationarity.
Keywords
change point analysis; climate change; inhomogeneity; CUSUM; BCP; DP;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Carlin, B.P., Gelfand, A.E., and Smith, A.F.M. (1992). "Hierarchical Bayesian analysis of changepoint problems." Applied Statistics, Vol. 41, No. 2, pp. 389-405.   DOI   ScienceOn
2 Abdul Aziz, O.I., and Burn, D.H.T (2006). "Trends and variability in the hydrological regime of the Mackenzie River Basin." Journal of hydrology, Vol. 319, pp. 282-294.   DOI   ScienceOn
3 Aksoy, H., Gedikli, A., Unal, N.E., and Kehagias, A. (2008). "Fast segmentation algorithms for long hydrometeorological time series." Hydrological Proccesses, Vol. 22, No. 23, pp. 4600-4608.   DOI
4 Barry, D., and Hartigan, J.A. (1992). "Product partition models for change point problems." The Annals of Statistics, Vol. 20, No. 1, pp. 260-279.   DOI   ScienceOn
5 Barry, D., and Hartigan, J.A. (1993). "A Bayesian analysis for change point problems." Journal of the American Statistical Association, Vol. 88, No. 421, pp. 309-319.
6 Beaulieu, C., Seidou, O., Ouarda, B.M.J., and Zhang, X. (2009). "Intercomparison of homogenization techniques for precipitation data continued: Comparison of two recent Bayesian change point models."Water Resources Research, Vol. 45, W08410.
7 Burn, D.H. (1994). "Hydrologic effects of climatic change in West Central Canada." Journal of Hydrology, Vol. 160, pp. 53-70.   DOI   ScienceOn
8 Carslaw, D.C., Ropkins, K., and Bell, M.C. (2006). "Changepoint detection of gaseous and particulate trafficrelated pollutants at a roadside location." Environ. Sci. Technol. Vol. 40, No. 22, pp. 6912-6918.   DOI   ScienceOn
9 Chelani, A.B. (2011). "Change detection using CUSUM and modified CUSUM method in air pollutant concentrations at traffic site in Delhi." Stochastic Environmental Research and Risk Assessment, Vol. 25, pp. 827-834.   DOI
10 Chib, S. (1998). "Estimation and comparison of multiple change-point models." Journal of Econometrics, Vol. 86, pp. 221-241.   DOI   ScienceOn
11 Chowdhury, R.K., and Beecham, S. (2010). "Australian rainfall trends and their relation to the southern oscillation index." Hydrological Processes, Vol. 24, No. 4, pp. 504-514.
12 Erdman, C., and Emerson, J.C. (2007). "bcp: an R package for performing a Bayesian analysis of change point problems." Journal of Statistical Software, Vol. 23, No. 3, pp. 1-13.
13 Chu, H.J., Pan, T.Y., and Liou, J.J. (2012). "Change-point detection of long-duration extreme precipitation and the effect on hydrologic design: a case study of south Taiwan." Stochastic Environmental Research and Risk Assessment, Vol. 26, pp. 1123-1130.   DOI
14 Fearnhead, P., and Liu, Z. (2011). "Efficient Bayesian analysis of multiple changpoint models with dependence across segments." Statistics and Computing, Vol. 21, pp. 217-229.   DOI
15 Domonkos, P. (2011). "Efficiency evaluation for detecting inhomogeneities by objective homogenization methods." Theoretical and Applied Climatology, Vol. 105, No. 3-4, pp. 455-467.   DOI
16 Fearnhead, P. (2006). "Exact and efficient Bayesian inference for multiple change point problems." Statistics and Computing, Vol. 16, pp. 203-213.   DOI
17 Hwang, S.H., Kim, J.H., Yoo, C., and Jung, S.W. (2010). "A Probabilistic estimation of changing points of Seoul rainfall using BH Bayesian analysis." Journal of Korea Water Resources Association, Vol. 43, No. 7, pp. 645-655 (In Korean).   과학기술학회마을   DOI
18 Gedikli, A., Aksoy, H., and Unal, N.E. (2008). "Segmentation algorithm for long time series analysis." Stochastic Environmental Research and Risk Assessment, Vol. 22, pp. 291-302.   DOI
19 Hirsch, R.M., Slack, J.R., and Smith, R.A. (1982). "Techniques of trend analysis for monthly water quality data." Water Resources Research, Vol. 18, pp. 107-121.   DOI   ScienceOn
20 Kampata, J.M., Parida, B.P., and Moalafhi, D.B. (2008). "Trend analysis of rainfall in the headstreams of the Zambezi river basin in Zambia." Phys. Chem. Earth, Vol. 33, pp. 621-625.   DOI   ScienceOn
21 Li, Z.L., Xu, Z.X, Li, J.Y., and Li, Z.J. (2008). "Shift trend and step changes for runoff time series in the Shiyang River basin, northwest China." Hydrological Processes, Vol. 22, No. 23, pp. 4639-4646.   DOI
22 Kehagias, A., Nidelkou, E., and Petridis, V. (2006). "A dynamic programming segmentation procedure for hydrological and environmental time series." Stochastic Environmental Research and Risk Assessment, Vol. 20, pp. 77-94.   DOI
23 Kim, J., and Cheon, S. (2010). "Bayesian multiple changepoint estimation with annealing stochastic approximation Monte Carlo." Computational Statstics, Vol. 25, pp. 215-239.   DOI
24 Kim, C., Suh, M.S., and Hong, K.O. (2009). "Bayesian changepoint analysis of the annual maximum of daily and subdaily precipitation over South Korea." Journal of Climate, Vol. 15, pp. 6741-6757.
25 Page, E.S. (1954). "Continuous inspection scheme." Biometrika, Vol. 41, pp. 100-115.   DOI
26 Yao, Y.C. (1984). "Estimation of a noisy discrete-time step function: Bayes and empirical Bayes approaches." The Annals of Statistics, Vol. 12, No. 4, pp. 1434-1447.   DOI
27 Dobigeon, N., and Tourneret, J.Y. (2007) "Joint segmentation of wind speed and direction using a hierarchical model." Comput. Statist. Data Anal., Vol. 51, pp. 5603-5621.   DOI