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Predicting biomass global warming potential with FT-NIR spectroscopy
(2025-12-01)
Gyawali, Prakash
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Shrestha, Bijendra
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Phanomsophon, Thitima
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Posom, Jetsada
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Pornchaloempong, Pimpen
This research is to predict the global warming potential (GWP) of biomass by using Fourier transform near-infrared (FT-NIR) spectroscopy. A partial least squares regression model of 197 biomass chip samples was developed for predicting GWP of fast-growing trees and agricultural residues. The reference value of GWP of biomass sample was calculated by the method provided by Intergovernmental Panel on Climate Change (IPCC). After applying different spectral pretreatments and variable selection methods, the best model for predicting GWP was found using the 1st derivative spectrum pretreatment and covariance method (COVM) based variable selection. The results indicate GWP model exhibit good predictive capabilities, where the model can be usable with caution for any purpose including research, by achieving a coefficient of determination for prediction set (R2<inf>P</inf>) of 0.86, and ratio of prediction to deviation (RPD) of 2.6. Additionally, the RMSEP of 0.00063 suggests a low prediction error. This pioneering approach presents a swift and efficient means to determine GWP, the complex functionality parameter, which reveals an optimal relationship model, showcasing its efficacy in a significant advancement in the assessment of biomass functionality related to climate change issue. Additionally, the further research is recommended to integrate FT-NIR data with thermogravimetric analyser to simulate of different thermal conversion of biomass type where different emission gases are generated and with gas chromatography–mass spectrometry for evaluation of concentration of the generated gases for further refine GWP predictions which providing more comprehensive insights and exact content of emission gases affect global warming to support the IPCC.
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Analysis of drought and extreme precipitation events in Thailand: trends, climate modeling, and implications for climate change adaptation
(2025-12-01)
de Oliveira-Júnior, José Francisco
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Mendes, David
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Porto, Helder Dutra
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Cardoso, Kelvy Rosalvo Alencar
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Neto, José Augusto Ferreira
Thailand is under threat from climate change, where extreme climate events are expected to intensify and increase in the coming decades. The objective is to assess extreme drought and rainfall events in Thailand based on climate modeling through an ensemble for future projections of extreme climate indices. The climate indices used were Consecutive Dry Days (CDD), Maximum Number of Consecutive Summer Days (CSU), Consecutive Wet Days (CWD), Warm Spell Duration Index (WSDI), and Maximum Number of Consecutive Wet Days (WW) derived from simulations of an ensemble composed of six models from the Intergovernmental Panel on Climate Change (IPCC) via the Coupled Model Intercomparison Project Phase 6 (CMIP6) using Artificial Neural Networks (ANN) with the backpropagation method. The projections were based on three scenarios: historical (20th century); intermediate forcing (RCP 4.5) and high forcing (RCP 8.5). The results of the climate indices pointed to significant regional differences in Thailand. Historically, the CDD indicated 35 consecutive dry days in the northern (N) and northeastern (NE) parts of Thailand, whereas the southern region showed CDD values of fewer than 10 consecutive dry days. In the R4.5 scenario, a meridional pattern emerged in CDD, increasing from east (E) to west (W). In the R8.5 scenario, the number of consecutive dry days increased across the entire country. The WSDI stood out in both the R4.5 and R8.5 scenarios, with an increase in the duration of warm spells in Thailand. The CSU did not perform satisfactorily in the scenarios adopted. Historically, the CWD indicated consecutive wet days in the N and NE, whereas in the R4.5 and R8.5 scenarios, this was observed only in the Central and Southern regions. Historically, the maximum number of consecutive rainy days varied in the NE and South via WW. In the R4.5 and R8.5 scenarios, there was a significant increase in the maximum number of consecutive rainy days across Thailand. Projections based on climate indices indicate that Thailand needs to adopt mitigation measures across its regions to neutralize the impacts of extreme drought and rainfall events on socioeconomic sectors, particularly in tourism, industry, agricultural production, and food security for its population.
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Decadal and seasonal oceanographic trends influenced by climate changes in the Gulf of Thailand
(2025-06-01)
Lubis, Muhammad Zainuddin
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Ghazali, Muhammad
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Simanjuntak, Andrean V.H.
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Riama, Nelly F.
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Pasma, Gumilang R.
Our study investigates the decadal and seasonal variability of sea surface height (SSH) and sea surface temperature (SST) in the Gulf of Thailand (GoT) using data from CMEMS from 1993 to 2021. We employed statistical analyses utilizing GLM and GAM to assess the variables comprehensively. The reveals a significant upward trend in SSH, increasing from ∼0.79 m in 1993–1998 to ∼0.89 m in 2017–2021, highlighting the impacts of climate change. SST analysis revealed fluctuations, with a maximum reaching ∼30.6 °C in 2019–2020, correlating with climatic events such as El Niño. Our study results at station 1 (near Bangkok) showed that the average SSH in 1998 during strong El Niño years was equal to 0.82 m, while the maximum SST was equal to 29.89 °C. Seasonal patterns indicated SSH peaks in DJF and SON at ∼0.92 m, while SST peaked in spring MAM and summer JJA at ∼30.7 °C. Volume transport analysis showed significant variability, with 0.3634 Sv (0–55 m) at longitude 99°E-107° E and latitude 6° N, indicating complex circulation patterns influenced by bathymetry and wind. Time series analysis revealed an average SSH increase of 0.0038 m/year, with a high pseudo-R-squared of 0.99. Our findings underscore the critical influence of climate variability on oceanographic conditions in the GoT, emphasizing the need for ongoing monitoring to address the implications of rising sea levels and temperature fluctuations. In conjunction with increased SSH, the rising SST heightens the risk of flooding in low-lying areas, exacerbating vulnerabilities for local populations and necessitating adaptive management strategies to mitigate these impacts.
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Hybrid machine learning models: A comprehensive, data-driven evaluation with diverse data partitioning strategies for net radiation estimation
(2025-04-30)
Bajao, Kristian Lorenz
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Phetpan, Kittisak
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Chophuk, Ponlawat
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Suwalak, Rattapong
Surface net radiation (Rn) is crucial for climate modeling and agricultural management but is often not readily available, especially in regions like Thailand. Accurate prediction of Rn is essential for estimating evapotranspiration, which is vital for irrigation planning and agricultural productivity. This study develops a hybrid machine learning framework that incorporates K-Nearest Neighbors (KNN) for missing data imputation, Random Forest-Recursive Feature Elimination (RF-RFE) for feature selection, and machine learning models (Multi-layer Perceptron, K-Nearest Neighbors, and Random Forest) for prediction. The research evaluates various data partitioning methods, including hold-out split, K-fold cross-validation, and growing-window forward-validation (gwFV), alongside hyperparameter tuning using GridSearch to enhance model robustness and prevent overfitting. The primary objectives are to develop and evaluate the hybrid ML models for daily Rn estimation using basic meteorological inputs (temperature, relative humidity, and sunshine duration), assess the impact of different input combinations on prediction accuracy in Sawi, Chumphon, Thailand, and compare data partitioning techniques to determine the optimal model performance. Utilizing FAO56PM-calculated Rn as a reference, this study finds that the Random Forest model, with average temperature and sunshine duration (M2) as inputs evaluated under the gwFV method, achieves the highest stability and high accuracy (R² of 0.972, RMSE of 0.457 MJ m-2 day-1, and MAPE of 3.50%). The Random Forest demonstrates strong generalization capabilities, making it a reliable choice. Even models using only sunshine duration (M3) perform adequately, offering a solution when data availability is scarce. This study concludes that hybrid machine learning models, combined with careful data partitioning, significantly improve Rn estimation. These advancements provide valuable insights for climate modeling, agricultural management, and irrigation scheduling, particularly in data-scarce regions.
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Crop Suitability Analysis Under Future Climate Change in Chachoengsao Province
(2025-01-01)
Monprapussorn, Sathaporn
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Titseesang, Teerawet
Currently, climate change poses one of the most significant global challenges we face. The rise in average global temperature and the occurrence of extreme weather events have the potential to result in natural disasters such as sea level rise, floods, droughts, and storms, which can negatively impact society. In developing countries, the agricultural sector plays a vital role in driving economic growth and sustaining social well-being. A decrease in crop productivity serves as strong evidence of climate change’s impact on vulnerable communities. Climate conditions are also increasingly influential in determining crop yields. This paper aims to project climate data for the year 2040 and evaluate land suitability for rice, rubber, and cassava cultivation in Thailand’s Chachoengsao province. The projected climate data illustrates a trend toward rising temperatures with minimal changes in rainfall patterns. To predict the level of suitability for rice, rubber, and cassava, the EcoCrop model is employed, requiring the projected climate data as input. The suitability maps for rice and cassava in 2040 exhibit some differences. The areas available for each suitability class for rice are as follows: marginally suitable (101 km2), moderately suitable (873 km2), highly suitable (1247 km2), and excellent (2799 km2). Chachoengsao province is also overall suitable for rubber and cassava cultivation. These findings can be utilized in long-term policy and planning efforts to optimize the productivity of agricultural land in the province.
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Epidemiology of Cholera Epidemic in Southern Africa Region
(2025-01-01)
Nyasulu, Peter Suwirakwenda
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Tamuzi, Jacques
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Gelegbe, Gideon Kofi
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Mphande, Fingani Annie
Persistent issues like urbanization, rapid population expansion, and natural disasters caused by climate change provide a significant risk of cholera epidemics in low- and middle-income countries. The cholera pandemic that impacted numerous nations in Southern Africa from 2022 to 2023 lasted a long time and continues to harm the region, putting further burden on people and healthcare systems. Despite the lack of agreement on future climate change in Southern Africa, there appears to be agreement on increased variability in climate extremes, including regionally diverse drought, and precipitation changes. This must be prioritized in future drought and precipitation planning in Southern Africa to ensure that activities remain effective in the face of climate change-inducing cholera epidemics. Cholera prediction models will benefit from including nutrient influx, phytoplankton and zooplankton blooms, human cholera vulnerability in Southern Africa, and other climate variables to detect outbreaks early. It necessitates a comprehensive and whole-of-society strategy at the local, national, and regional levels to do additional research on cholera pandemic gaps and potential scenarios related with climate change that could induce cholera epidemics in Southern Africa.
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Enhancing sustainable development through Spatiotemporal analysis of Ramsar wetland sites in South Asia
(2024-12-01)
Goyal, Manish Kumar
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Rakkasagi, Shivukumar
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Surampalli, Rao Y.
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Zhang, Tian C.
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Erumalla, Saikumar
The ecological significance of wetlands makes it imperative to study changes in their inundation extent and propose necessary conservation measures. Monitoring wetland dynamics and implementing strategies to protect these essential ecosystems is crucial for maintaining the balance of natural systems. This study used pre-processed Landsat imagery (1991–2020) to generate yearly composites and produce inundation maps based on an automated Short-Wave Infrared thresholding technique within the Google Earth Engine platform. The analysis was executed on individual wetlands to describe their typical condition owing to regional climatic and geographical circumstances. The Mann-Kendall test was used to understand the trends in the change of inundation extent. The thresholding method achieved an overall accuracy of 89.0 %, with average dry and wet Producer's accuracies of 90.6 % and 86.6 %, respectively. The accuracy was higher for open water lakes compared to wetlands with complex vegetation dynamics. The trend analysis revealed that 46 sites follow an increasing trend, while the remaining 43 sites were found to be decreasing. Among these 43, 12 sites were found to be significantly decreasing, with the Upper Ganga River showing a maximum decrease of about 59 % in the inundation extent. Factors such as elevation, precipitation, temperature, and climate type were found to influence the trends in wetland inundation. Wetlands at high altitudes (>4000 m) and those receiving less than 500 mm of annual precipitation were more likely to exhibit decreasing trends. Coastal wetlands showed varying trends, with five increasing and three significantly increasing. The findings of this study provide valuable insights into the relationship between sustainable development and wetland conservation, supporting the Ramsar Convention's goals and the UN's Sustainable Development Goals. The individualized analysis of Ramsar sites enables the development of localized management strategies, climate change adaptation, and informed policy-making, ultimately contributing to the sustainable use of these critical ecosystems in South Asia.
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Energy demand modeling for low carbon cities in Thailand: A case study of Nakhon Ratchasima province
(2023-07-01)
Tippichai, Atit
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Teungchai, Kattreeya
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Fukuda, Atsushi
Nakhon Ratchasima is one of the northeastern cities which has been promoted as one of the low-carbon cities in Thailand. The study aims to evaluate policies and measures on greenhouse gas (GHG) emissions mitigation to meet the target at the provincial level. The Low Emissions Analysis Platform (LEAP) is used as a modeling tool to simulate energy demand for each economic sector. The 2019 data is set as a base year, using top-down and bottom-up approaches depending on the availability of data for the analysis. The model consists of two scenarios: (1) Business-as-usual (BAU) scenario and Low carbon scenario (LCS). Transport and industry sectors are the most energy-consuming and CO2-emitting sectors in Nakhon Ratchasima Province. In the LCS case, the final energy demand and CO2 emissions in 2050 will be reduced by about 40% compared to the BAU case. In addition, CO2 emissions in Nakhon Ratchasima Province will peak around 2038, this is not the case with BAU. The study could predict future energy demand and propose a way forward to reducing GHG emissions at the provincial level.
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Serviceability of cut slope and embankment under seasonal climate variations
(2023-04-01)
Apriyono, Arwan
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Yuliana
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Kamchoom, Viroon
In the next 20 years, there will be an extensive investment in transport infrastructure. Although the cut and embankment slopes seem to have the same appearance, they have different responses to climate variations. Understanding their characteristics and performance is necessary to design a safer and more sustainable slope infrastructure. This paper provides a thorough examination of the seasonal performance of cut slopes and embankments. Furthermore, this study suggests an introduction to the impacts of climate change, amplifying seasonal shrinkage–swelling and progressive failure of slope construction under extreme drought and precipitation. Volumetric water content and pore water pressure fluctuations due to seasonal variation were analysed and compared from both the cut slope and the embankment. Moreover, stress path and slope deformation were also investigated in this study to understand the behaviour of the cut slope and the embankment. The results suggest that the cut slope retains more pore water pressure during the wet season due to its lower permeability than an embankment with respect to the construction history. However, pore water pressure and displacement in the cut slope tend to be increased due to the consolidation process after excavation, which requires more time to reach equilibrium. In addition, greater displacement in the cut slope can increase the possibility of delayed failure in the future.
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Impact of climate change on soil erosion in the lam phra phloeng watershed
(2020-12-01)
Sirikaew, Uba
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Seeboonruang, Uma
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Tanachaichoksirikun, Pinit
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Wattanasetpong, Jatuwat
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Chulkaivalsucharit, Virun
Soil erosion plays a vital role in reducing reservoir capacity. The Lam Phra Phloeng (LPP) dams were built for flood protection and irrigation. However, they have experienced reservoir sedimentation, and the capacity of the reservoir has decreased. The surrounding soil surface was easily eroded and transported by heavy rainfall and surface runoff to streams and eventually into the reservoir. Understanding this soil erosion and sedimentation is necessary for preventing further decline of reservoir capacity and water management. This research aims to estimate long-term average annual soil erosion and predict sediment yield in the reservoir due to climate change. The methodology is determined soil loss parameters and sediment yield using the Universal Soil Loss Equation (USLE) with the Sediment Delivery Ratio (SDR). The USLE and SDR methods differed from field data, with an average absolute error of 4.0%. The Global Climatic Model, Institute Pierre Simon Laplace-Climate Model version 5A (IPSL-CM5A-MR), with Representative Concentration Pathways (RCP) 2.6, 4.5, and 8.5, was downscaled and analyzed to forecast future rainfall in the watershed. The high intensity of rainfall contributed to higher soil erosion, in RCP 8.5. Interestingly, the high and very high-risk areas increased, but the moderate risk area declined, indicating that the moderate risk area should be a priority in land management. However, the heavy rainfall and high slope gradient led to a slight increase in the soil erosion in some areas because the land covers were evergreen and deciduous forest. The prediction of sediment yield was positively correlated with the intensity of rainfall in the central part of the watershed, because the rainfall and runoff led the sediment to the river and streams, indicating that the land cover should be managed to prevent capacity decline.

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