Application of Vector Auto Regression Model for Rainfall-River Discharge Analysis

Sri Hartini(1*), Muhammad Pramono Hadi(2), S Sudibyakto(3), Aris Poniman(4)

(1) Geospasial Information Agency
(2) Faculty of Geography, Universitas Gadjah Mada
(3) Faculty of Geography, Universitas Gadjah Mada
(4) Geospasial Information Agency
(*) Corresponding Author

Abstract

River discharge quantity is highly depended on rainfall and initial condition of river discharge; hence, the river discharge has auto-correlation relationships. This study used Vector Auto Regression (VAR) model for analysing the relationship between rainfall and river discharge variables. VAR model was selected by considering the nature of the relationship between rainfall and river discharge as well as the types of rainfall and discharge data, which are in form of time series data. This research was conducted by using daily rainfall and river discharge data obtained from three weirs, namely Sojomerto and Juwero, in Kendal Regency and Glapan in Demak Regency, Central Java Province. Result of the causality tests shows significant relationship of both variables, those on the influence of rainfall to river discharge as well as the influence of river discharge to rainfall variables. The significance relationships of river discharge to rainfall indicate that the rainfall in this area has moved downstream. In addition, the form of VAR model could explain the variety of the relationships ranging between 6.4% - 70.1%. These analyses could be improved by using rainfall and river discharge time series data measured in shorter time interval but in longer period.

Full Text:

PDF

References

Adenomon, M. (2013). Modelling The Dynamic Relationship Between Rainfall and Temperature Time Series Data in Niger State, Nigeria. Mathematical Theory and Modeling. Vol.3 No. 4. pp. 53-70, Retrieved from http://www.iiste.org/Journals/index.php/MTM/article/view/5325, [20 August 2014].

Asdak, C. (2002). Hidrologi dan Pengelolaan Daerah Aliran Sungai. Yogyakarta: Gadjah Mada University Press.

Das D. (2013). Variation of Temperature and Rainfall in India. International Journal of Advances in Engineering & Technology, Sept. 2013, Retrieved from http://www.e-ijaet.org/media/41I16-IJAET0916831_v6_iss4_1803to1812.pdf, [20 August 2014].

Diani, K. A. N., Setiawan, & Suhartono.(2013). Pemodelan VAR-NN dan GSTAR-NN untuk Peramalan Curah Hujan di Kabupaten Malang. JURNAL SAINS DAN SENI POMITS, 2 (1). Retrieved from http://ejurnal.its.ac.id/index.php/sains_seni/article/viewFile/3137/771

Hadi, Y. S. (2003). Analisis Vector Auto Regression (VAR) Terhadap Korelasi Antara Pendapatan Nasional dan Investasi Pemerintah di Indonesia, 983/1984 – 1999/2000. Jurnal Keuangan Dan Moneter, 6 (2), 107–121. Retrieved from http://www.fiskal.depkeu.go.id/webbkf/kajian%5C7.Jonatan-2.pdf, [19 August 2014].

Hadi, M. P. (2006). Pemahaman Karakteristik Hujan Sebagai Dasar Pemilihan Model Hidrologi (Studi Kasus di DAS Bengawan Solo Hulu). Forum Geografi, 20(1), 13–26. Retrieved from 202.154.59.182/ejournal/files/hujan.pdf. [12/06/2010]

Hasria.(2012). Prakiraan Curah Hujan Bulanan Kota Kendari dengan Model ARIMA. Jurnal Aplikasi Fisika, 8 (1 - Februari), 25–30, Retrieved from http://jaf-unhalu.webs.com/5_JAF-_Februari_12__Hasria_.pdf, [22August 2014].

Huda, A. M., Choiruddin, A., Budiarto, O., & Sutikno.(2012). Peramalan Data Curah Hujan dengan Seasonal Autoregressive Integrated Moving Average (SARIMA) Dengan Deteksi Outlier Sebagai Upaya Optimalisasi Produksi Pertanian di Kabupaten Mojokerto. Dalam Proceeding Seminar Nasional: Kedaulatan Pangan dan Energi. Madura: Fakultas Pertanian Universitas Trunojoyo, Retrieved from http://pertanian.trunojoyo.ac.id/semnas/wp-content/uploads/PERAMALAN-DATA-CURAH-HUJAN-DENGAN-SEASONAL-AUTOREGRESSIVE-INTEGRATED-MOVING-AVERAGE-_SARIMA_-DENGAN-DETEKSI-OU.pdf, [22August 2014].

Kar, A. K., Lohani, A. K., Goel, N. K., & Roy, G. P. (2010). Development of Flood Forecasting System Using Statistical and ANN Techniques in the Downstream Catchment of Mahanadi Basin, India. Journal Water Resource and Protection, 2010 (October), 880–887. doi:10.4236/jwarp.2010.210105

Ko, C., & Cheng, Q. (2004). GIS spatial modeling of river flow and precipitation in the Oak Ridges Moraine area, Ontario. Computers & Geosciences, 30 (4), 379–389. doi:10.1016/j.cageo.2003.06.002

Liu, Z., & Todini, E. (2002).Towards a comprehensive physically-based rainfall-runoff model. Hydrology and Earth Sciences, 6(5), 859–881.

Lohani, A. K., Kumar, R., & Singh, R. D. (2012). Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques. Journal of Hydrology, 443, 23–35.

Saputro, D. R. S., Wigena, A. H., &Djuraidah, A. (2011). Model Vektor Autoregressive Untuk Peramalan Curah Hujan di Indramayu. Forum Statistika dan Komputasi, 16(2), 7–11, Retrieved from http://journal.ipb.ac.id/index.php/statistika/article/view/4916/3348, [19 August 2014].

Startz, R. (2013). Eviews Ilustrated for version 8, www.eviews.com/ilillustrated/Eviews_Illustrated.pdf, [19 August 2014].

Vaze, J., Jordan, P., Frost, A., & Summerelle, G. (2012). Guidelines for rainfall-runoff modelling: Towards best practice model application.Bruce, Australia: eWater Cooperation Research Centre. Retrieved from http://www.ewater.com.au/uploads/files/eWater-Modelling-Guidelines-RRM-(v1-Mar-2012).pdf, [23 October 2014].

DOI: 10.23917/forgeo.v29i1.786

Article Metrics

Abstract view(s): 802 time(s)
PDF: 496 time(s)

Refbacks

  • There are currently no refbacks.


Bookmark and Share