Generating Hourly Rainfall Model using Bayesian Time Series Model (A Case Study at Sentral Station, Bondowoso)

Entin Hidayah, Nur Iriawan, Nadjadji Anwar, Edijatno Edijatno

Abstract


Disaggregation of hourly rainfall data is very important to fulfil the input of continual rainfall-runoff model, when the availability of automatic rainfall records are limited. Continual rainfall-runoff modeling requires rainfall data in form of series of hourly. Such specification can be obtained by temporal disaggregation in single site. The paper attempts to generate single-site rainfall model based upon time series (AR1) model by adjusting and establishing dummy procedure. Estimated with Bayesian Markov Chain Monte Carlo (MCMC) the objective variable is hourly rainfall depth. Performance of model has been evaluated by comparison of history data and model prediction. The result shows that the model has a good performance for dry interval periods. The performance of the model good represented by smaller number of MAE by 0.21 respectively.

Keywords


temporal rainfall; hourly; Bayesian time series

Full Text:

PDF

References


Valencia, R. D. and Schaake, Jr, Disagregation process in stocastic hydrology”, Water Resour. Res., Vol. 9, No. 3, pp 580-585, 1973.

Maidment, D.R,.“Handbook of hydrology”, Mc.GRAW-HILL. INC, New York. 1992.

Hershenhorn, J., Woolhiser, D.A. “Disaggregation of daily rainfall”, J. Hydrol, Vol. 95, pp. 299-322, 1987.

Koutsoyiannis, D., “A stochastic disaggregation method for design storm and flood synthesis”, J. Hydrol, Vol. 156, pp. 193-225. 1994.

J.L. Marien, and G.L. Vandewiele, “A point rainfall generator with internal storm structure”, Water Resour. Res, Vol. 22, No. 4, pp. 475-482, 1986.

D.A. Woolhiser, H.B. Osborn, “A stochastic model of dimensionless thunderstorm rainfall”, Water Resour. Res.Vol. 21 (4), pp.511-522, 1985.

K.M. Wong, “Disaggregation of rainfall time series using adjustments”, Thesis Report, Department of Hydro National Technique University of Athens, 2000.

S.J. Burian, S.R. Durrans, S.J. Nix, and R.E. Pitt, “Training artificial neural networks to perform rainfall disaggregation”, J. Hydrol. Eng.-ASCE, Vol. 6, No. 1, pp. 43-51, 2001.

S.J. Burian, S.R. Durrans, S. Tomic, R.L. Pimmel, C.N. Wai, “Rainfall disaggregation using artificial neural networks”, J. Hydrol. Engng ASCE, Vol. 5, pp. 299–307, 2000.

D. Koutsoyiannis and C. Onof, “Rainfall disaggregation using adjusting procedures on a poisson cluster model”, Journal of Hydrology, Vol. 246, pp. 109-122, 2001.

D. Koutsoyiannis, C. Onof, and H.S. Wheater, “Multivariate rainfall disaggregation at a fine time scale”, Water Resources Research (in press), 2003.

E. Hidayah, N. Iriawan, N. Anwar, and Edijatno, “Evaluating evaluating error of temporal disaggregation from daily rainfall into hourly rainfall using heytos model at sampean catchments area”, Journal IPTEK, Vol. 21, No. 1, 2010.

C. Onof and K. Arrnbjerg-Nielsen, “Quantification of anticipated future changes in high resolution design rainfall for urban area”, Atmospheric Research, Vol. 92, pp. 350-363, 2009.

E. Todini, and C Mazzetti, “A bayesian multisensor combination approach to rainfall estimate”, 2005.

www.eurosip.0rg/Proceedings/Ext/ISCCSP2006/defevent/../cr1364.pdf

S.K. Sahu, G.J Lasinio, A. Orasi, and K. V. Mardia, “A comparison of spatio-temporal bayesian models for reconstruction of rainfall fields in a cloud seeding experiment”, Journal of Mathematics and Statistics, Vol. 1, No. 4, pp. 273-281, 2005.

R.H.C Lima, “Hierarchical bayesian modeling of multisite daily rainfall occurrence”, PhD Thesis, Colombia University, New York, 2009.

T.M. Carpenter and K. P. Georgakakos, “Intercomparison of lumped versus distributed hydrologic model ensemble simulations on operational forecast scales”, 2006.

W.W.S Wei ,“Time series analysis: univariat and multivariate methods”, Singapore: Addison-wesley, 1994.

G.E.P Box and G.M. Jenkins, “Time series analisys and forcasting and control”, Holden Day Inc, USA, 1976.

J.B. Carlin, A. Gelman, H.S. Stern and D.B. Rubin, “Bayesian data analysis”, London: Chapman and Hall/CRC, 2003.

N. Iriawan, “Penaksiran model mixtue normal univariabel: suatu pendekatan metode bayesian dengan MCMC”, Prosiding Seminar Nasional dan Konferda VII Matematika Wilayah DIY dan Jawa Tengah, pp. 105-110, Yogyakarta, 2001.

Casella and George, “Explaining gibbs sampler”, Journal of the American Association, Vol. 46, No. 3, pp. 167-174, 1992.




DOI: http://dx.doi.org/10.12962/j20882033.v22i1.57

Refbacks

  • There are currently no refbacks.


Creative Commons License

IPTEK Journal of Science and Technology by Lembaga Penelitian dan Pengabdian kepada Masyarakat, ITS is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at https://iptek.its.ac.id/index.php/jts.