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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.

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Air pollution is a major concern for the population in Chiang Mai, northern Thailand, as it is for most people in other large cities around the world. Hazy skies and pollution alert have become normal during late winter and entire summer almost every year. Prolonged expose to PM2.5 can have acute and chronic effects on the respiratory and cardiovascular systems. This research aims to study the correlations between PM2.5 and meteorological variables (rainfall and temperature) in Chiang Mai during 2017 and 2020. The cross wavelet transform (XWT) and wavelet coherence (WTC) have been used to examine these relations by assessing the presence of common power and the relative phase in the time-frequency space. The XWT between PM2.5 and rainfall shows a significant common power in two dominant period bands, one in the period between 10-14 months and the other one between 5-7 months. The first common power occurs during all observed time intervals, so it is obviously related to natural annual periodicities of PM2.5 and rainfall. The second band, which occurs only in the year 2019 may be connected with the beginning of the monsoon season which starts in May and brings a stream of warm moist air to Chiang Mai. Our data shows that PM2.5 typically begins to rise starting in November, and it remains high until March of the next year. The PM2.5 is low in rainy season since rain has a wet scavenging effect on PM2.5. The WTC, which is a measure of the correlation between two time series, indicate that there is a significant correlation between PM2.5 and rainfall in the 10-14 month band. The phase difference between these two time series is defined by arrows. The phase arrows pointing to the left indicated the anti-phase relation, when rainfall increases, PM2.5 decreases and vice versa. The correlation coefficient (r) between PM2.5 and rainfall in rainy season is equal to 0.8504. Our study also finds that there is a proven correlation between PM2.5 and temperature in a day time scale with the correlation coefficient equal to 0.9249. In a one-day period, PM2.5 is low in the day time and high at night. An understanding of how climate variability may impact PM2.5 concentration in Chiang Mai will help the government with better planning and preparation to prevent environmental hazard from PM2.5 pollution.

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