Rainfall In Thailand Research Articles

Regional flood frequency analysis of extreme rainfall in Thailand, based on L-moments

Regional flood frequency analysis of extreme rainfall in Thailand, based on <i>L</i>-moments

Simulating Heavy Rainfall Associated with Tropical Cyclones and Atmospheric Disturbances in Thailand Using the Coupled WRF-ROMS Model—Sensitivity Analysis of Microphysics and Cumulus Parameterization Schemes

Predicting heavy rainfall events associated with Tropical Cyclones (TCs) and atmospheric disturbances in Thailand remains challenging. This study introduces a novel approach to enhance forecasting precision by utilizing the coupled Weather Research and Forecasting (WRF) and Regional Oceanic Model (ROMS), known as WRF-ROMS. We aim to identify the optimal combination of microphysics (MP) and cumulus (CU) parameterization schemes. Three CU schemes, namely, Betts-Miller-Janjic (BMJ), Grell 3D Ensemble (G3), and Kain-Fritsch (KF), along with three MP schemes, namely, Eta (ETA), Purdue Lin (LIN), and WRF Single-moment 3-class (WSM3), are selected for the sensitivity analysis. Seven instances of heavy (35.1–90.0 mm) to violent (&gt;90.1 mm) rainfall in Thailand, occurring in 2020 and associated with tropical storms and atmospheric disturbances, are simulated using all possible combinations of the chosen physics schemes. The simulated rain intensities are compared against observations from the National Hydroinformatics Data Center. Performance was assessed using the probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) metrics. While the models performed well for light (0.1–10.0 mm) to moderate (10.1–35.0 mm) rainfall, forecasting heavy rainfall remained challenging. Certain parameter combinations showed promise, like BMJ and KF with LIN microphysics, but challenges persisted. Analyzing density distribution of daily rainfall, we found effective parameterizations for different sub-regions. Our findings emphasize the importance of tailored parameterizations for accurate rainfall prediction in Thailand. This customization can benefit water resource management, flood control, and disaster preparedness. Further research should expand datasets, focusing on significant heavy rainfall events and considering climate factors, for example, the Madden-Julian Oscillation (MJO) for extended-range forecasts, potentially contributing to sub-seasonal and seasonal (S2S) predictions.

20–60-day intraseasonal variation of summer rainfall in Thailand and its associated large-scale atmospheric moisture circulation

20–60-day intraseasonal variation of summer rainfall in Thailand and its associated large-scale atmospheric moisture circulation

Bayesian Estimation for the Difference between Coefficients of Quartile Variation of Delta-Lognormal Distributions: An Application to Rainfall in Thailand

The coefficient of quartile variation is a valuable measure used to assess data dispersion when it deviates from a normal distribution or displays skewness. In this study, we focus specifically on the delta-lognormal distribution. The lognormal distribution is characterized by its asymmetrical nature and comprises exclusively positive values. However, when these values undergo a logarithmic transformation, they conform to a symmetrical (or normal) distribution. Consequently, this research aims to establish confidence intervals for the difference between coefficients of quartile variation within lognormal distributions incorporating zero values. We employ the Bayesian, generalized confidence interval, and fiducial generalized confidence interval methods to construct these intervals, involving data simulation using RStudio software. We evaluate the performance of these methods based on coverage probabilities and average lengths. Our findings indicate that the Bayesian method, employing Jeffreys’ prior, performs well in low variability, while the generalized confidence interval method is more suitable for higher variability. Therefore, we recommend using both approaches to construct confidence intervals for the difference between the coefficients of the quartile variation in lognormal distributions that include zero values. Furthermore, we apply these methods to rainfall data in Thailand to illustrate their alignment with actual and simulated data.

Estimation of common percentile of rainfall datasets in Thailand using delta-lognormal distributions.

Weighted percentiles in many areas can be used to investigate the overall trend in a particular context. In this article, the confidence intervals for the common percentile are constructed to estimate rainfall in Thailand. The confidence interval for the common percentile help to indicate intensity of rainfall. Herein, four new approaches for estimating confidence intervals for the common percentile of several delta-lognormal distributions are presented: the fiducial generalized confidence interval, the adjusted method of variance estimates recovery, and two Bayesian approaches using fiducial quantity and approximate fiducial distribution. The Monte Carlo simulation was used to evaluate the coverage probabilities and average lengths via the R statistical program. The proposed confidence intervals are compared in terms of their coverage probabilities and average lengths, and the results of a comparative study based on these metrics indicate that one of the Bayesian confidence intervals is better than the others. The efficacies of the approaches are also illustrated by applying them to daily rainfall datasets from various regions in Thailand.

Simulation of Summer Rainfall in Thailand by IAP-AGCM4.1

Thailand is located in the Southeast Asian region, where the summer rainfall exhibits strong interannual variability, and the successful simulation of rainfall variation in Thailand by current climate models remains a challenge. Therefore, this paper evaluates the capability of the state-of-the-art Atmospheric GCM of the Institute of Atmospheric Physics (IAP-AGCM) in simulating summer rainfall over Thailand by comparing the model’s results with ground-truth observation during 1981–2012. Generally, the model shows a certain skill in reproducing the observed spatial distribution of the summer rainfall climatology and its interannual variability over Thailand, although the model underestimated both rainfall amount and its variability. Using Empirical Orthogonal Function (EOF) analysis, it is found that the IAP climate model reproduced creditably the spatial patterns of the first three dominant modes of summer rainfall in Thailand, whereas it underestimated the explained variance of the observed EOF-1 and overestimated the explained variance of the observed EOF-2 significantly. It was further found that the correlation between the observed rainfall anomalies in Thailand and the Niño3.4 index can be reproduced by the IAP model. However, the observed negative correlation is largely underestimated by the IAP climate model, and this could be the reason for the underestimation of explained variance of the EOF-1 by the IAP model. The evaluation results would be of great importance for further model improvement and thus potential application in seasonal prediction in the region.

Cascading Models of CNN and GRU with Autoencoder Loss for Precipitation Forecast in Thailand

&#x0D; &#x0D; &#x0D; It is a crucial task to accurately forecast precipitation, especially rainfall in Thailand, since it relates to flood prevention and agricultural planning. In our prior work, we have presented a model based on deep learning approach; however, its performance is still limited due to two main issues. First, there is an imbalance issue, where most rainfall is zero or no rain because Thailand has short rainy season. Second, predicted rainfall is still underestimated since moderate and heavy rainfall cases barely occurs. In this paper, we propose an enhanced deep learning model to forecast rainfall in Thailand. Our model is a cascading of CNN and GRU along with exogenous variables, i.e., temperature, pressure, and humidity. There are two stages in our model. First, CNN is specialized for classifying rain and non-rain events. In this stage, an imbalanced issue is alleviated by applying “focal loss”. Second, GRU is responsible for forecasting rainfall. Its predicted range is lifted using “autoencoder loss”. The experiment was conducted on hourly rainfall dataset between 2012 and 2018 obtained from a public government sector in Thailand. The results show that our enhanced model outperforms ARIMA and CNN-GRU in terms on RMSE of most regions in Thailand.&#x0D; &#x0D; &#x0D;

Influence and variability of monsoon trough and front on rainfall in Thailand

AbstractHigh variabilities in rainfall and the subsequent consequences have caused severe water‐related issues for Thailand over recent decades. In this study, the factors that affect rainfall variability over Thailand were determined. The spatio‐temporal structures of the monthly rainfall in Thailand between 1979 and 2018 were extracted via an empirical orthogonal function (EOF) analysis. With a unimodal temporal structure, the first EOF mode suggests the overall phenomenon governing rain characteristics in Thailand. The higher modes, consisting of bimodal temporal structures, indicate different rain phenomena in various regions. A significantly high correlation between the 3‐month average of the monsoon trough index and rainfall was observed, while wet adiabatic front and convective available potential energy (CAPE) data had a moderately high correlation. Only the local front index, within the Gulf of Thailand, correlated to the rain characteristics along the east coast of southern Thailand. The cross‐wavelet analysis indicated that most of the rainfall variability correlates with monsoon trough. However, the front can involve extreme events. The interannual variability of monsoon trough and rainfall with a dominant time scale between 3.0 and 6.0 years has significantly increased since 1995. Meanwhile, the intra‐annual variability is more intermittent, indicating rare intensified strong tropical cyclones along the monsoon troughs in Thailand. The interannual variability of the monsoon trough and front indices also intermittently connects with the Southern Oscillation Index (SOI). However, the relationship in annual and intra‐annual cycles is mostly weak. Therefore, the dependence of interannual variability of rainfall in Thailand on SOI or monsoon trough is comparable. Notably, the intra‐annual rainfall variability is heavily influenced by the monsoon trough and front. This study is the first to identify the crucial factors behind the seasonal rain phenomena in Thailand. This information will benefit seasonal rainfall predictors and increase preparedness for hydro‐climatic related disasters in the Indo‐China region.

LH-Moments of the Wakeby Distribution applied to Extreme Rainfall in Thailand

This article applies the Wakeby distribution (WAD) with high-order L-moments estimates (LH-ME) to annual extreme rainfall data obtained from 99 gauge stations in Thailand. The objectives of this study investigate to obtain appropriate quantile estimates and return levels for several return periods, 2, 5, 10, 25 and 50 years. The 95% confidence intervals for the quantiles determined from the WAD are derived using the bootstrap technique. Isopluvial maps of estimated design values that correspond to selected return periods are presented. The LH-ME results are better than estimates from the more primitive L-moments method for a large majority of the stations considered.

Estimating the average daily rainfall in Thailand using confidence intervals for the common mean of several delta-lognormal distributions.

The daily average natural rainfall amounts in the five regions of Thailand can be estimated using the confidence intervals for the common mean of several delta-lognormal distributions based on the fiducial generalized confidence interval (FGCI), large sample (LS), method of variance estimates recovery (MOVER), parametric bootstrap (PB), and highest posterior density intervals based on Jeffreys’ rule (HPD-JR) and normal-gamma-beta (HPD-NGB) priors. Monte Carlo simulation was conducted to assess the performance in terms of the coverage probability and average length of the proposed methods. The numerical results indicate that MOVER and PB provided better performances than the other methods in a variety of situations, even when the sample case was large. The efficacies of the proposed methods were illustrated by applying them to real rainfall datasets from the five regions of Thailand.

Confidence intervals for the common coefficient of variation of rainfall in Thailand.

The log-normal distribution is often used to analyze environmental data like daily rainfall amounts. The rainfall is of interest in Thailand because high variable climates can lead to periodic water stress and scarcity. The mean, standard deviation or coefficient of variation of the rainfall in the area is usually estimated. The climate moisture index is the ratio of plant water demand to precipitation. The climate moisture index should use the coefficient of variation instead of the standard deviation for comparison between areas with widely different means. The larger coefficient of variation indicates greater dispersion, whereas the lower coefficient of variation indicates the lower risk. The common coefficient of variation, is the weighted coefficients of variation based on k areas, presents the average daily rainfall. Therefore, the common coefficient of variation is used to describe overall water problems of k areas. In this paper, we propose four novel approaches for the confidence interval estimation of the common coefficient of variation of log-normal distributions based on the fiducial generalized confidence interval (FGCI), method of variance estimates recovery (MOVER), computational, and Bayesian approaches. A Monte Carlo simulation was used to evaluate the coverage probabilities and average lengths of the confidence intervals. In terms of coverage probability, the results show that the FGCI approach provided the best confidence interval estimates for most cases except for when the sample case was equal to six populations (k = 6) and the sample sizes were small (nI < 50), for which the MOVER confidence interval estimates were the best. The efficacies of the proposed approaches are illustrated with example using real-life daily rainfall datasets from regions of Thailand.

The Bayesian confidence intervals for measuring the difference between dispersions of rainfall in Thailand.

The coefficient of variation is often used to illustrate the variability of precipitation. Moreover, the difference of two independent coefficients of variation can describe the dissimilarity of rainfall from two areas or times. Several researches reported that the rainfall data has a delta-lognormal distribution. To estimate the dynamics of precipitation, confidence interval construction is another method of effectively statistical inference for the rainfall data. In this study, we propose confidence intervals for the difference of two independent coefficients of variation for two delta-lognormal distributions using the concept that include the fiducial generalized confidence interval, the Bayesian methods, and the standard bootstrap. The performance of the proposed methods was gauged in terms of the coverage probabilities and the expected lengths via Monte Carlo simulations. Simulation studies shown that the highest posterior density Bayesian using the Jeffreys’ Rule prior outperformed other methods in virtually cases except for the cases of large variance, for which the standard bootstrap was the best. The rainfall series from Songkhla, Thailand are used to illustrate the proposed confidence intervals.

Moisture-Density Relationship of Bottom Ash, Lime Stabilized Bangkok Clay as a Potential Pavement Material in Thailand

Expansive clayey soils like Bangkok clay can shrink and swell with water contact. Therefore, the presence of such soils tends to damage the road conditions with a higher frequency of rainfall in Thailand. The presence of expansive clayey soil has brought the concept of stabilization of such roads with any other stronger materials. This paper shows the test results on the moisture-density relationship of bottom ash and lime stabilized Clayey soil to use as a pavement material in Thailand. In this study, a waste material produced from coal combustion called bottom ash was used as a replacement material and quicklime as a binding material. During the tests 10%, 20%, 30% and 50% bottom ash and 4%, 8%,12% Lime were used. The result shows a decrease in Maximum Dry Density and increase in Optimum Moisture Content with an increase in each amount of replacement material.

Updated Basis Knowledge of Climate Change Summarized from the First part of Thailand’s Second Assessment Report on Climate Change

Recent evidence and key issues on climate change in Thailand have been presented in the first part of Thailand’s Second Assessment Report on Climate Change (2ndTRAC). The report highlights key findings including 1) a significant country-wide warming of 1.30 oC over the past 48 years (1970-2017); b) significant changes in rainfall patterns at smaller spatial and finer temporal scales; c) significant changes in temperature and rainfall extreme events over the last four-five decades; d) a significant decrease in frequency of tropical cyclones entering Thailand; e) significant rise in sea level in the seas around Thailand at higher rates than the global average; and f) significant projected increases in temperature and rainfall in Thailand by 2100. The first part of the 2nd TARC provides a comprehensive and updated analysis of climate change impacts in Thailand that can be used as an authoritative reference for building understanding and awareness, as well as for designing adaptation and mitigation strategies. Moreover, it can serve as a repository for scientific information to support further research related to impact, adaptation and vulnerability to climate change. Regular assessment of national climate change impacts is essential to informing national policy and to allow policymakers to assess priorities and set meaningful targets in line with the country’s international obligations under the Climate Change Agreement.

The Comparison of Grey System and the Verhulst Model for Rainfall and Water in Dam Prediction

A time series of data of rainfall in Thailand between the years 2005 and 2015 was employed to predict possible future rainfall based on Julong Deng’s grey systems theory and the grey Verhulst model to see which model can predict more accurately with uncertain and limited data. Firstly, the rainfall data were arranged to display the overall patterns of rainfall volume along with its frequency as well as the temperature during Thailand’s rainy seasons. This makes it possible to see the cycle of rainfall, which is too long for people to intuitively understand the nature of precipitation. One puzzling phenomenon that has made rainfall forecast elusive is the unpredictability of the haphazard nature of rainfall in Thailand. A more precise prediction would certainly result in a better control of water volume in rivers and dams for fruitful agricultural business and adequate human consumption. This can also prevent the flooding that can devastate the economy and transportation of the whole country and also tremendously improve the future water management policy in many ways. This effective prediction could also be employed elsewhere around the globe for similar benefits. Hence, the grey systems theory and the grey Verhulst model are juxtaposed to determine a better prediction possible.

Sustainability and Competitiveness in Thai Rubber Industries

This article assesses the economic, legal and environmental conditions that Thai rubber farmers face and evaluates actions they can take to increase incomes.Statistical analyses determine relationships between prices of oil, natural and synthetic rubber. Pearson correlation tests found a strong positive relationship (r = 0.887) between the price of Brent crude and Thai ribbed smoked sheets, and a moderate positive relationship between price changes in Brent and synthetic rubber (r = 0.648). Regression analysis showed Brent oil price is a good predictor of natural rubber price. Moderate to strong positive relationships were also found between natural rubber price and the gross domestic products of Japan, China and the United States. Criminal antitrust behaviour in the rubber industries appeared to interfere with normal pricing in rubber markets. No significant bivariate correlation was found between rainfall in Thailand and natural rubber price, production or export, although fl ooding and other environmental issues clearly affected rubber farms. A survey of options showed that Thai rubber farmers can best improve their livelihoods through the collective purchase and use of new technologies and by integrating into downstream supplychain industries. At the very least, farmers are urged to abandon monocrop methods and supplement their incomes with fruit, fi sh, livestock or pigs.

A preliminary study on teak tree ring cellulose δ18O from northwestern Thailand: the potential for developing multiproxy records of Thailand summer monsoon variability

Thailand monsoon is located in the transition zone between the Indian and western North Pacific monsoons. Assuredly, proxy climate data from this area could improve our understanding of the nature of Asian monsoon. Tree rings and stalagmites from this area are two potential materials for high-resolution paleoclimate reconstructions. However, a comprehensive understanding of these multiproxy records is still a challenge. In this study, a 76-year tree ring cellulose oxygen isotope value (δ18O) of a teak tree from northwestern Thailand was developed to test its climatic significance and potential for multiproxy climate reconstruction. The results indicate that the interannual variability of cellulose δ18O can be interpreted as a proxy of rainfall in the early monsoon season (May to July rainfall) as well as a proxy of relative humidity. Comparisons with speleothem proxies from the same locality and tree ring records from wider geographical areas provide a basis for developing a multiproxy approach. The results from a teleconnection analysis reveal that the El Nino-Southern Oscillation (ENSO) is an important climate mode that impacts monsoon rainfall in Thailand. High-quality proxy records covering recent decades are critically important not only to improve proxy data calibrations but also to provide a better understanding of teleconnections within the modern atmosphere. Preliminary findings demonstrated the potential of tree ring stable isotopes from Thai teak to develop multiproxy climate reconstruction.

Sustainability and Competitiveness of Thailand’s Natural Rubber Industry in Times of Global Economic Flux

This study assesses economic, legal, and environmental conditions that Thai rubber farmers face, and evaluates actions they can take to increase incomes. Statistical analyses determine relationships between prices of oil, natural and synthetic rubber. Pearson correlation tests found a strong positive relationship (r = 0.887) between the price of Brent crude and Thai ribbed smoked sheets, and a moderate positive relationship between price changes in Brent and synthetic rubber (r = 0.648). Regression analysis showed Brent oil price is a good predictor of natural rubber prices. Moderate to strong positive relationships were also found between natural rubber price and gross domestic product of Japan, China, and the United States. Criminal antitrust behavior in rubber industries appeared to interfere with normal pricing in rubber markets. No significant bivariate correlation was found between rainfall in Thailand and natural rubber price, production, or export although flooding and other environmental issues clearly affected rubber farms. A survey of options showed Thai rubber farmers can improve livelihoods best through collective purchase and use of new technologies, and by integrating into downstream supply chain industries. At very least, farmers are urged to abandon monocrop methods and supplement incomes with fruit, fish, livestock, or pigs. stment budget, 2) architectural Aesthetic, and 3) utilization. Additionally, background of the interviewees is one of reinforcing factors for decision on universal design investment.

Effect of Rainfall for the Dynamical Transmission Model of the Dengue Disease in Thailand.

The SEIR (Susceptible-Exposed-Infected-Recovered) model is used to describe the transmission of dengue virus. The main contribution is determining the role of the rainfall in Thailand in the model. The transmission of dengue disease is assumed to depend on the nature of the rainfall in Thailand. We analyze the dynamic transmission of dengue disease. The stability of the solution of the model is analyzed. It is investigated by using the Routh-Hurwitz criteria. We find two equilibrium states: a disease-free state and an endemic equilibrium state. The basic reproductive number (R0) is obtained, which indicates the stability of each equilibrium state. Numerical results taking into account the rainfall are obtained and they are seen to correspond to the analytical results.

Predicted pattern of Zika virus infection distribution with reference to rainfall in Thailand.

Zika virus infection is the present global medical problem. The disease appears in several countries around the world. The relationship between rainfall and occurrence of Zika virus infection was previously mentioned. Here, the authors use the mathematical modeling technique to reappraise on the previous data on immunoreactivity rate of Zika virus, dengue virus and Ckikungunya virus in Thailand and the reported interrelationship between arboviral infections and rainfall in Thailand for constructing of the predicted pattern of Zika virus distribution in Thailand. This data can be a useful tool for further disease surveillance in this area.