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Spatio-Temporal Model of Extreme Rainfall Data in the Province of South Sulawesi for a Flood Early Warning System

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this study, we model extreme rainfall to study the high rainfall events in the province of South Sulawesi, Indonesia. We investigated the effect of the El Nino South Oscillation (ENSO), Indian Ocean Dipole Mode (IOD), and Madden-Julian Oscillation (MJO) on extreme rainfall events. We also assume that events in a location are affected by events in other nearby locations. Using rainfall data from the province of South Sulawesi, the results showed that extreme rainfall events are related to IOD and MJO.
Rocznik
Strony
5--15
Opis fizyczny
Bibliogr. 21 poz., rys., tab.
Twórcy
  • Hasanuddin University, Faculty of Technic, Department of Civil Engineering, Makassar, Indonesia
  • Hasanuddin University, Faculty of Mathematics and Natural Sciences, Department of Mathematics, Makassar, Indonesia
autor
  • Hasanuddin University, Department of Mathematics, Faculty of Mathematics and Natural Sciences, Makassar, Indonesia
Bibliografia
  • [1] The Intergovernmental Panel on Climate Change (IPCC): Managing the Risk of Extreme Events and Disasters to Advance Climate Change Adaptation. Cambridge University Press, Cambridge, UK and New York, USA, 2012.
  • [2] Coles S.: An Introduction to Statistical Modeling of Extreme Values. Springer, Bristol 2001.
  • [3] Zhang Q., Li J., Singh V.P.: Application of Archimedean copulas in the analysis of the precipitation extremes: effects of precipitation changes. Theoretical and Applied Climatology, vol. 107, 2011, pp. 255–264.
  • [4] Hendon H.H.: Indonesian Rainfall Variability: Impacts of ENSO and Local Air-Sea Interaction. Journal of Climate, vol. 16, issue 11, 2003, pp. 1775–1790.
  • [5] Nur’utami M.N., Hidayat R.: Influences of IOD and ENSO to Indonesian Rainfall Variability: Role of Atmosphere‑ocean Interaction in the Indo‑pacific Sector. Procedia Environmental Sciences, vol. 33, 2016, pp. 196–203.
  • [6] Hidayat R.: Modulation of Indonesian Rainfall Variability by the Madden-Julian Oscillation. Procedia Environmental Sciences, vol. 33, 2016, pp. 167–177.
  • [7] Keim B.D., Cruise F.J.: A Technique to Measure Trends in the Frequency of Discrete Random Events. Journal of Climate, vol. 11, 1998, pp. 848–855.
  • [8] Wong H.L., Hsieh S.H., Tu Y.H.: Application of Non‑Homogeneous Poisson Process Modeling to Containership Arrival Rate. [in:] 2009 Fourth International Conference on Innovative Computing, Information and Control: ICICIC 2009; Kaohsiung, Taiwan, 7–9 December 2009, Institute of Electrical and Electronics Engineers, 2009, pp. 849–854.
  • [9] Achcar J.A., Rodrigues E.R., Paulino C.D., Soares P.: Non‑homogeneous Poisson models with a change‑point: an application to ozone peaks in Mexico city. Environmental and Ecological Statistics, vol. 17, 2010, pp. 521–541.
  • [10] Sirangelo B., Ferrari E., de Luca D.: Occurrence analysis of daily rain‑falls through non‑homogeneous poissonian processes. Natural Hazards and Earth System Sciences, vol. 11, 2011, pp. 1657–1668.
  • [11] Mondal A., Mujumdar P.P.: Modeling non‑stationarity in intensity, duration and frequency of extreme rainfall over India. Journal of Hydrology, vol. 521, 2015, pp. 217–231.
  • [12] Huang X., Grace P., Hu W., Rowlings D., Mengersen K.: Spatial prediction of N2O emissions in pasture: a Bayesian model averaging analysis. PLoS One, vol. 8(6), 2013, e65039.
  • [13] Rusworth A., Lee D., Mitchell R.: A spatio‑temporal model for estimating the long‑term effects of air pollution on respiratory hospital admissions in Greater London. Spatial and Spatio‑temporal Epidemiology, vol. 10, 2014, pp. 29–38.
  • [14] Sharkey P., Winter H.C.: A Bayesian spatial hierarchical model for extreme precipitation in Great Britain. 2017, arXiv:1710.02091v1.
  • [15] Marco M., Gracia E., López‑Quílez A.: Linking Neighborhood Characteristics and Drug‑Related Police Interventions: A Bayesian Spatial Analysis. ISPRS International Journal of Geo‑Information, vol. 6(3), 2017, 65. https://doi.org/10.3390/ijgi6030065.
  • [16] Banerjee S., Carlin B., Gelfand A.: Hierarchical Modeling and Analysis for Spatial Data. Monographs on Statistics and Applied Probability, 101, Chapman and Hall/CRC Press, New York 2004.
  • [17] Deni O.L., Edy S., Sabaruddin S., Iskhaq I.: Respective Influences of Indian Ocean Dipole and El Niño‑Southern Oscillation on Indonesian Precipitation. Journal of Mathematical and Fundamental Sciences, vol. 50, no. 3, 2018, pp. 257–272.
  • [18] Lee H.S.: General Rainfall Patterns in Indonesia and the Potential Impacts of Local Seas on Rainfall Intensity. Water, vol. 7(4), 2015, pp. 1751–1768.
  • [19] Jones Ch., Waliser D.E., Lau K.M., Stern W.: Global occurrences of extreme precipitation and the Madden–Julian Oscillation: observations and predictability. Journal of Climate, vol. 17(23), 2004, pp. 4575–4589.
  • [20] Ashok K., Guan Z., Saji N.H., Yamagata T.: Individual and Combined Influences of ENSO and the Indian Ocean Dipole on the Indian Summer Monsoon. Journal of Climate, vol. 17, 2004, pp. 3141–3155.
  • [21] Pourasghar F., Oliver E.C.J., Holbrook N.J.: Modulation of wet‑season rainfall over Iran by the Madden–Julian Oscillation, Indian Ocean Dipole and El Niño– Southern Oscillation. International Journal of Climatology, vol. 39, 2019, pp. 1–12. https://doi.org/10.1002/joc.6057.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-01b770b1-2c5e-4302-809c-186e77f61e7b
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