Rainfall Variability Study in Bangkok and Songkhla province Thailand using Cross Wavelet Coherence Stationary Oscillation
Keywords:rainfall variability, stationary oscillation, sea ice extent, cross wavelet coherence
Rainfall variability in Southern Asia is strongly affected by local climate, associated with temporal and spatial variations of large-scale atmospheric circulation. Rainfall is a major water source for drinking water and agriculture. For this reason, knowing the interannual relationship between two local climates can provide significant information for forecasting the rainy season to decrease disaster risk and improve water management. In this paper, we present a cross wavelet coherence stationary oscillation (XWCSO) between Arctic- and Antarctic-wide changes in the sea ice extent and rainfall variability at two locations with very different topography, i.e., Bangkok and Songkhla provinces, Thailand. The XWCSO results can be interpreted as large rainfall variability in Bangkok from 1988 to 1990, 2000 to 2007, and 2011 to 2015 during mid-November to mid-March, mid-August to mid-December, and mid-May to mid-September respectively. However, in Songkhla province, the heavy rainfall from 1988 to 2015 occurred from early July until mid-December. Such variability can be connected to the seasonal winds, El Niño and La Niña Southern Oscillation. The proposed scheme should be useful for analyzing and forecasting the rainfall variability over Southeast Asia and other areas, as well as applications in meteorology and agriculture.
Baddoo T, Guan Y, Zhang D, Andam-Akorful S (2015) Rainfall variability in the Huangfuchuang Watershed and its relationship with ENSO. Water 7(7):3243–3262
Carvalho LMV, Jones C, Ambrizzi T (2005) Opposite phases of the Antarctic oscillation and relationships with intraseasonal to interannual activity in the tropics during the austral summer. J. Clim. 18:702–718
Chellali F, Khellaf A, Belouchrani A (2010) Wavelet spectral analysis of the temperature and wind speed data at Adrar, Algeria. Renewable Energy 35:1214–1219
Cruz FT, Narisma TG, Villafuerte II MQ, Cheng-Chua KU, Olaguera LM (2012) A climatological analysis of the southwest monsoon rainfall in the Philippines. Atmos. Res. 122:609–616
Daubechies I (1990) The wavelet transform time-frequency localization and signal analysis. IEEE Trans. Inform. Theory 36:961–1004
Farge M (1992) Wavelet transforms and their applications to turbulence. Annu. Rev. Fluid Mech. 24:395–457
Gaucherel C (2002) Use of wavelet transform for temporal characterization of remote watersheds. J. Hydrol. 269:101–121
Google (2019) Google Maps. Retrieved September 5, 2019 from http://www.google.co.th/maps/place/ Bangkok/ @11.0641193, 97.7201264,5.31z
Grinsted A, Moore JC, Jevrejeva S (2004) Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlin. Processes Geophys. 11:561–566
Holz A, Paritsis J, Mundo IA, Veblen TT, Kitzberger T, Williamson GJ, Araoz E, Bustos-Schindler C, Gonzalez ME, Grau HR, Quezada JM (2017) Southern annular mode drives multicenter wildfire activity in southern South America. Proc. Natl. Acad. Sci. USA. 114(36):9552–9557
Huang J, Higuchi K, Shabbar A (1998) The relationship between the North Atlantic oscillation and the El Niño-southern oscillation. Geophys. Res. Lett. 25:2707–2710
Hudgins L, Friehe CA, Mayer ME (1993) Wavelet transforms and atmospheric turbulence. Phys. Rev. Lett. 71:3279–3282
Jevrejeva S, Moore JC, Grinsted A (2003) Influence of the arctic oscillation and El niño-southern oscillation (ENSO) on ice conditions in the Baltic Sea: the wavelet approach. J. Geophys. Res. 108(D21):4677–4687
Jevrejeva S (2002) Association between the ice conditions in the Baltic Sea and the North Atlantic oscillation. Nordic Hydrol. 33:319–330
National snow and ice data center (2018) Sea ice index: Arctic-and Antarctic-wide changes in sea ice. Available online: https://nsidc.org/data/seaice_index/archives.
Narasimha R, Bhattacharyya S (2010) A wavelet cross-spectral analysis of solar-ENSO-rainfall connec-tions in the Indian monsoons. Appl. Comput. Harmon. Anal. 28:285–295
Rahman R, Anik AM, Farhana Z, Devnath S, Ahmed Z (2018) Pattern recognition of rainfall using wavelet transform in Bangladesh. Open J. Stat. 8:134–145
Rasmusson EM, Carpenter TH, (1982) Variations in tropical sea surface temperature and surface wind fields associated with the southern oscillation/El niño. Mon. Wea. Rev. 110:354–384
Santos CAG, Galvão CO, Suzuki K, Trigo RM (2001) Matsuyama city rainfall data analysis using wavelet transform. J. Hydraul. Engng, JSCE 45:211-216.
Santos CAG, Galvão CO, Trigo RM (2003) Rainfall data analysis using wavelet transform. IAHS-AISH publication, 195–201
Torrence C, Compo GP (1998) A practical guide to wavelet analysis. Bull. Am. Meteorol. Soc. 79:61–78
Torrence C, Webster P (1999) Interdecadal changes in the ESNO monsoon system. J. Clim. 12:2679–2690
Trenberth KE (1997) The definition of El niño. Bull. Amer. Meteor. Soc. 78:2771–2777
Trenberth KE, Stepaniak DP (2011) Indices of El niño evolution. J. Clim. 14:1697–1701
Trenberth K, National center for atmospheric research staff (2019) The climate data guide: Nino SST indices (Nino 1+2, 3, 3.4, 4; ONI and TNI), last modified 11 Jan 2019. Available online: https://climatedataguide.ucar.edu/climate-data/nino-sst-indices-nino-12-3-34-4-oni-and-tni
Wang G, Cai W (2013) Climate-change impact on the 20th-century relationship between the southern annular mode and global mean temperature. Sci. Rep. 3:2039
Yueqing X, Shuangcheng L, Yunlong C (2004) Wavelet analysis of rainfall variation in the Hebei Plain. Sci. China Ser. D. 48(12):2241–2250
Copyright Notice: a copyright on any article in the published journal is retained by the Ramkhamhaeng International Journal of Science and Technology. Readers or Users grant the right to use of the Article contained in the Content in accordance with the Creative Commons CC BY-NC-ND license and the Data contained in the Content in accordance with the Creative Commons CC BY-NC-ND.