Using wavelet analysis to study floods in Bangkok

Abstract

Floods in Bangkok are regular natural disasters that happen nearly every year during the monsoon season. Many streets of the city may turn into canals and seriously worsened rush-hour traffic causing many commuters to arrive late at work or school. Therefore, in this research, we will study floods in Bangkok area both seasonal flooding and occasional flooding due to the effect from La Nina cycles using the wavelet spectrum analysis and the cross wavelet method. The result from cross wavelet transform (XWT) shows that the standard precipitation index (SPI) (wet) and the oceanic index (ONI) (La Nina) have significant common power mainly in the 20-40 month band from 1967-1974 during the strong La Nina cycle. There is, however, a large area with common power outside the significance level around 32-80 month band from 1982-2002 but depicts a highly significant local correlation. The corresponding wavelet coherence spectra showed that SPI and ONI co-varied mostly in the 32-60 month band from 1966-1981 with the arrow pointing to the right and in the 64-80 month band from 1986-1994 with the arrows also pointing to the right. Although flooding is a common and annual occurrence in Bangkok, the La Nina climatic phenomenon can cause a higher than average rainfall. The intense extremely wet period during strong La Nina years was observed in 1955-1956, 1998-1999 and 2007-2008. In a La Nina years, Bangkok will receive more rain than normal years. In general, this work may contribute to the improvement of the understanding of how climate variability may impact flooding in Bangkok. Knowing of the La Nina years in advance will help the government in better planning and preparation to prevent flooding areas more than normal.

Changes of extreme precipitation in the middle and lower reaches of the Yangtze River and their correlation with atmospheric circulation

“…Wavelet coherence refers to the similarity of two synchronized time series in the time-frequency domain, in the region of common variation (not necessarily with high energy). The wavelet coherence spectrum can reveal not only the common high-energy region and phase relationship of the two-time series, but also measure the proximity of local correlation between them in the time-frequency domain (Juthasirorat et al, 2021;Zhao and Luo, 2021).…”

Spatial-temporal variations of extreme precipitation indices and their linkage with atmospheric circulation in the middle and lower reaches of the Yangtze River

“…Wavelet coherence refers to the similarity of two synchronous time series in time-frequency space in the common variation region (but not necessarily with high energy). Wavelet coherence spectrum can not only reveal the common high energy region and phase relationship of two time series, but also measure the closeness of local correlation between two time series in time-frequency space (Juthasirorat et al, 2021;Zhao and Luo, 2021).…”