Seeboonruang, Uma

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Seeboonruang, Uma
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Seeboonruang, U.
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uma.se@kmitl.ac.th

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    The relationship between the climatic indices and the rainfall fluctuation in the lower central plain of Thailand
    (2019-02-01)
    Taweesin, Korrakoch
    ;
    Seeboonruang, Uma
    Global climate changes are revealing the interconnections between natural conditions, natural resources, and regional climate variability that may affect the rain fluctuation. Rainfall plays an important role in the process of hydrology. This research presents an analysis of rainfall in the lower central plain of Thailand and the climate variability/oceanographic events in the wider geographical region, including the El Niño/Southern Oscillation (ENSO), Asian Summer Monsoon (ASM), and Indian Ocean Dipole (IOD). Data from 1980-2010 and 2011-2014 were collected for calibration and verification. Next, the frequency domains, spectra, and wavelet transforms were analyzed, together with the climate index and rainfall. The results revealed that rainfall occurs in seasons, yearly cycles, and off-seasons. The behavior of ASMs, for example, Indian Summer Monsoon Index (IMI) and Western North Pacific Monsoon Index (WNPMI), is the most similar to that of rainfall events, while the similarity of the other indices to rainfall events is not so strong. Cross-correlation analysis showed that there were delays between the climate indices and rainfall, so that multiple linear regression with lag time is required for further analysis. The results illustrate that the cross-correlation coefficients of IMI and WNPMI with rainfall are both approximately 0.6. The multiple regression with lag time shows that the average multiple coefficient correlation (R) is 0.64. The indicator of the summer monsoon index value is WNPMI, which is the most influential factor for rainfall. Finally, the proposed equations, based on the cross-correlation and multiple-linear regression with lag time techniques, can be used to predict precipitation and be applied to the development of rainfall forecasting in the future.
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    An empirical decomposition of deep groundwater time series and possible link to climate variability
    (2014-01-01)
    Seeboonruang, U.
    Deep groundwater data reflects hydrological processes, climate change and variability, as well as any anthropogenic influence. Decomposition of deep groundwater signal examines the history of the groundwater region. Detrending is a vital step in decomposition of groundwater time series because it is expected to remove anthropogenic effects and long-term cyclic patterns. Eight detrending methods were applied to long-term groundwater records monitored in the Lower Chao Phraya basin in Thailand. Detrended residuals and subsequently periodograms of the residuals were computed by applying the Fourier series analysis. The result from this study indicates that the 5<sup>th</sup> order polynomial interpolation provides the trendlines that significantly relate to the groundwater withdrawal background. The detrended residual function is imbedded with two major cyclic patterns, which can be the result from global climate variability, e.g. Indian Ocean Dipole and the El Niño Southern Oscillation. The magnitude of deep groundwater dynamics as the result from the anthropogenic effect, is much greater than that of the climate variability in this region. In addition, this study demonstrates that caution must be exercised when fitting groundwater time series with different detrending techniques can yield mistaken cyclic patterns and may infer to different climate variability phenomenon. © 2014 Global NEST Printed in Greece. All rights reserved.
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    The influence of climate variability effects on groundwater time series in the lower central plains of Thailand
    (2018-03-08)
    Taweesin, Korrakoch
    ;
    Seeboonruang, Uma
    ;
    Saraphirom, Phayom
    This research studies the relationship between the climate index and the groundwater level of the lower Chao Phraya basin, in order to forecast the groundwater level in the studied area by using Autoregressive IntegratedMoving Average with Explanatory (ARIMAX). The combination of 6 climate indices-Dipole Mode Index, Indian Summer Monsoon Index, Multivariate ENSO Index, Sea Surface Temperature NINO4, Southern Oscillation Index and theWestern North PacificMonsoon Index-were used, along with the groundwater level data from 14 stations during the period 1980-2011 to develop the forecastmodel and verify itwith the data of 2012.The first stepwas correlation of the ARIMAmodel with Autocorrelation Function and Partial Autocorrelation Function. The possible model was then selected using BIC statistics. Diagnostic Checking was done to consider the white noise characteristic of estimated residuals by using the statistics of Box and Ljung (Q-statistic). If the selected models were found to be proper, then the Granger Causality Test of the leading parameters or the climate index would be performed as the next step. The results show that there is a relationship between the groundwater level and the climate index. The model could be used to forecast effectively the average RMSE value at 0.6. The last step was to develop theMODFLOWfor a conceptualmodel and synthesize groundwater levels in the study area, which covers around 43,000 km<sup>2</sup> and has 8 layers of groundwater, with Bangkok clay on the top. All other boundary values were set to be steady. The calibration was done using the data of 325 observed wells. The normalized RMS value was 9.705%. The results were verified by the data using ARIMAX over the same time periods. To conclude, the simulated results of the monthly groundwater level in 2012 of the wells have a confidence interval of around 95%, which is near the result from the ARIMAX model. The advantages of the ARIMAX model include high accuracy, no requirement for a large amount of data and inexpensive implementation. It is one of the effective tools for the groundwater prediction.