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Kwon, Hyun-Han

So, Byung-Jin

English

Hydraulic Engineering Repository

Conference Paper, Published VersionSo, Byung-Jin; Kwon, Hyun-HanA Development of Multi-Site Rainfall Simulation ModelUsing Piecewise Generalize Pareto DistributionZur Verfügung gestellt in Kooperation mit/Provided in Cooperation with:Kuratorium für Forschung im Küsteningenieurwesen (KFKI)Verfügbar unter/Available at: https://hdl.handle.net/20.500.11970/109803Vorgeschlagene Zitierweise/Suggested citation:So, Byung-Jin; Kwon, Hyun-Han (2012): A Development of Multi-Site Rainfall SimulationModel Using Piecewise Generalize Pareto Distribution. In: Hagen, S.; Chopra, M.; Madani,K.; Medeiros, S.; Wang, D. (Hg.): ICHE 2012. Proceedings of the 10th InternationalConference on Hydroscience & Engineering, November 4-8, 2012, Orlando, USA.Standardnutzungsbedingungen/Terms of Use:Die Dokumente in HENRY stehen unter der Creative Commons Lizenz CC BY 4.0, sofern keine abweichendenNutzungsbedingungen getroffen wurden. Damit ist sowohl die kommerzielle Nutzung als auch das Teilen, dieWeiterbearbeitung und Speicherung erlaubt. Das Verwenden und das Bearbeiten stehen unter der Bedingung derNamensnennung. Im Einzelfall kann eine restriktivere Lizenz gelten; dann gelten abweichend von den obigenNutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte.Documents in HENRY are made available under the Creative Commons License CC BY 4.0, if no other license isapplicable. Under CC BY 4.0 commercial use and sharing, remixing, transforming, and building upon the materialof the work is permitted. In some cases a different, more restrictive license may apply; if applicable the terms ofthe restrictive license will be binding.The 10th Int.  Conf. on Hydroscience and Engineering (ICHE-2012), Nov. 4 – Nov. 7, Orlando, USA 180      A DEVELOPMENT OF MULTI-SITE RAINFALL SIMULATION MODEL USING PIECEWISE GENERALIZE PARETO DISTRIBUTION   Byung-Jin So1 and Hyun-Han Kwon2   In this study, a stochastic generation framework for simulation of daily rainfall at multiple sites is presented. The limitations of existing Markov chain model for reproducing extreme rainfalls are a known problem, and the problems have increased the uncertainties in establishing water resources plans. Especially, it is very difficult to secure reliability of water resources structures because the design rainfall through the existing Markov chain model is significantly underestimated. In this regard, aims of this study were to develop a new daily rainfall simulation model which is able to reproduce both mean and high order moments such as variance and skewness using a piecewise Kernel-Pareto distribution. In addition, the algorithm to reproduce the spatial correlation was combined. The algorithm can be considered characteristics of kernel density function and the GPD distribution at the same time. As a result Kernel-Pareto distribution based Markov chain model has been shown to perform well at reproducing most of statistics such as mean, standard deviation and skewness while the existing Gamma distribution based Markov chain model generally fails to reproduce high order moments. It was also confirmed that the proposed model can more effectively reproduce low order moments such as mean and median as well as underlying distribution of daily rainfall series by modeling extreme rainfall separately.    Figure 1 Correlation matrix for stations.                                                  1 Department of Civil Engineering, Chonbuk National University, Jeonju, Korea 2 2Corresponding Author, Department of Civil Engineering, Chonbuk National University, Jeonju, Korea (email: hkwon@jbnu.ac.kr) The 10th Int.  Conf. on Hydroscience and Engineering (ICHE-2012), Nov. 4 – Nov. 7, Orlando, USA 181 0 2 4 6 8 10 12 14 16 18100200300400500600700800900Station ID99% Quantile(mm)  ObservationPiecewise DistributionGamma Distribution    Figure 1 Assessment of simulation performance of MPKPD based Morkov Chain Model.  REFERENCES  Ashkar, F. and Tatsambon, C.N. (2007) Revisiting some estimation methods for the generalized Pareto distribution, Journal of Hydrology, Vol. 346, pp. 136-143. Haan, C. T., Allen, D. M., and Street, J. O. (1976), A Markov Chain Model of Daily Rainfall, Water Resour. Res., Vol. 12, No. 3, pp. 443-449. Stehlik, J. and Bardossy, A. (2002) Multivariate stochastic downscaling model for generating daily precipitation series based on atmospheric circulation, Journal of Hydrology, Vol. 256, pp. 120-141. Kwon, H.-H., Sivakumar, B., Moon Y.-I. and Kim B.-S. (2010), Assessment of change in design flood frequency under climate change using a multivariate downscaling model and a precipitation-runoff model, Stochastic Environmental Research and Risk Assessment, DOI: 10.1007/s00477-010-0422-z. Kwon, H.-H., Lall, U. and Obeysekera, J. (2009), Simulation of daily rainfall scenarios with interannual and multidecadal climate cycles for South Florida, Stochastic Environmental Research and Risk Assessment, Vol. 23, No. 7, pp. 879-896. Nord, J. (1975), Some Applications of Markov Chains, Proceedings Fourth Conference on Probability and Statistics in Atmospheric Science, Tallahas, pp. 125-130 Rosenblatt, M. (1956), Remarks on some nonparametric estimates of a density function. Ann. Math. Statist. 27, pp. 832–837. Singh, V.P., Ahmad, M. and Sherif, M.M. (2003), A comparative evaluation of the estimators of the 2-parameter generalized Pareto distribution. Water Engineering Research, Vol. 4, No. 3, pp. 155-173. 

A Development of Multi-Site Rainfall Simulation Model Using Piecewise Generalize Pareto Distribution

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A Development of Multi-Site Rainfall Simulation Model Using Piecewise Generalize Pareto Distribution

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