Search Results (18)

This study aims to build, for the first time, a model that uses a machine learning (ML) approach to predict long-term retrospective PM_2.5 concentrations in upper northern Thailand, a region impacted by biomass burning and transboundary pollution. The dataset includes PM₁₀ levels, fire hotspots, and critical meteorological data from 1 January 2011 to 31 December 2020. ML techniques, namely multi-layer perceptron neural network (MLP), support vector machine (SVM), multiple linear regression (MLR), decision tree (DT), and random forests (RF), were used to construct the prediction models. The best ML prediction model was selected considering root mean square error (RMSE), mean prediction error (MPE), relative prediction error (RPE) (the lower, the better), and coefficient of determination (R²) (the bigger, the better). Our study found that the ML model-based RF technique using PM₁₀, CO₂, O₃, fire hotspots, air pressure, rainfall, relative humidity, temperature, wind direction, and wind speed performs the best when predicting the concentration of PM_2.5 with an RMSE of 6.82 µg/m³, MPE of 4.33 µg/m³, RPE of 22.50%, and R² of 0.93. The RF prediction model of PM_2.5 used in this research could support further studies of the long-term effects of PM_2.5 concentration on human health and related issues. Full article

Rubber (Hevea brasiliensis Muell.) plantations are vital agricultural ecosystems in tropical regions. These plantations provide key industrial raw materials and sequester large amounts of carbon dioxide, playing a vital role in the global carbon cycle. Climate change has intensified droughts in Southeast Asia, negatively affecting rubber plantation growth. Limited in situ observations and short monitoring periods hinder accurate assessment of drought impacts on the gross primary productivity (GPP) of rubber plantations. This study used GPP data from flux observations at four rubber plantation sites in China and Thailand, along with solar-induced chlorophyll fluorescence (SIF), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIRv), and photosynthetically active radiation (PAR) indices, to develop a robust GPP estimation model. The model reconstructed eight-day interval GPP data from 2001 to 2020 for the four sites. Finally, the study analyzed the seasonal drought impacts on GPP in these four regions. The results indicate that the GPP prediction model developed using SIF, EVI, NDVI, NIRv, and PAR has high accuracy and robustness. The model’s predictions have a relative root mean square error (rRMSE) of 0.22 compared to flux-observed GPP, with smaller errors in annual GPP predictions than the MOD17A3HGF model, thereby better reflecting the interannual variability in the GPP of rubber plantations. Drought significantly affects rubber plantation GPP, with impacts varying by region and season. In China and northern Thailand (NR site), short-term (3 months) and long-term (12 months) droughts during cool and warm dry seasons cause GPP declines of 4% to 29%. Other influencing factors may alleviate or offset GPP reductions caused by drought. During the rainy season across all four regions and the cool dry season with adequate rainfall in southern Thailand (SR site), mild droughts have negligible effects on GPP and may even slightly increase GPP values due to enhanced PAR. Overall, the study shows that drought significantly impacts rubber the GPP of rubber plantations, with effects varying by region and season. When assessing drought’s impact on rubber plantation GPP or carbon sequestration, it is essential to consider differences in drought thresholds within the climatic context. Full article

Melioidosis is an endemic infectious disease caused by Burkholderia pseudomallei bacteria, which contaminates soil and water. To better understand the environmental changes that have contributed to melioidosis outbreaks, this study used spatiotemporal analyses to clarify the distribution pattern of melioidosis and the relationship between melioidosis morbidity rate and local environmental indicators (land surface temperature, normalised difference vegetation index, normalised difference water index) and rainfall. A retrospective study was conducted from January 2013 to December 2022, covering data from 219 sub-districts in Northeast Thailand, with each exhibiting a varying morbidity rate of melioidosis on a monthly basis. Spatial autocorrelation was determined using local Moran’s I, and the relationship between the melioidosis morbidity rate and the environmental indicators was evaluated using a geographically weighted Poisson regression. The results revealed clustered spatiotemporal patterns of melioidosis morbidity rate across sub-districts, with hotspots predominantly observed in the northern region. Furthermore, we observed a range of coefficients for the environmental indicators, varying from negative to positive, which provided insights into their relative contributions to melioidosis in each local area and month. These findings highlight the presence of spatial heterogeneity driven by environmental indicators and underscore the importance of public health offices implementing targeted monitoring and surveillance strategies for melioidosis in different locations. Full article

Ecological analyses of the small and lesser-known insect order Mecoptera in Thailand are presented. Specimens were collected monthly over a period of 12 consecutive months, using both Malaise and pan traps, from 29 sampling sites located in 18 national parks throughout Thailand. A total of 21 species in four genera were identified from 797 specimens, including Panorpa (1 species), Neopanorpa (18 species), Bittacus (1 species), and Terrobittacus (1 species), with the latter genus representing a new genus record to Thailand. Neopanorpa harmandi, N. siamensis, N. byersi, and N. malaisei were the most abundant species, representing 27.4%, 11.3%, 10.3% and 8.8% of the total specimens, respectively. The species with the highest frequency, as indicated by the high percentage of species occurrence (%SO), was N. siamensis (51%), followed by N. byersi (34%), N. harmandi (34%), N. spatulata (27%), and N. inchoata (27%). Eleven species (52%) exhibited specific regional occurrences. N. tuberosa and N. siamensis had the widest distribution, being found in almost all regions except for western and southern regions for the first and second species, respectively. The seasonal species richness of Mecoptera was high during the rainy season in the northern, northeastern, central, eastern, and western regions, with the highest richness observed in July (15 species), followed by the hot (10 species) and cold seasons (7 species), while there was no significant difference in species richness between seasons in the southern region. Multiple regression models revealed a negative association between species richness and abundance of Mecoptera with both elevation and temperature, and a positive association between rainfall and species evenness. It is predicted that climatic changes will have a detrimental effect on the mecopteran community. The results of this study enhance the understanding of the ecological aspects of Mecoptera, offering crucial insights into its biodiversity and distribution, which are vital for conservation and forest management. Full article

Northern Thailand rainfall (NTR) plays a crucial role in supplying surface water resources for downstream regions that millions of Thais rely on. The NTR has been reported to be adversely affected by the recent climate change making it impossible to accurately predict rainfall for better water management. In this work, we attempt to find an indicator that can be used to predict monthly NTR using an oceanic index based on sea surface height anomaly (SSHA) called the South China Sea Index (SCSI). First, we investigate the lead-lag relationships between NTR and several well-known indices. Relationships of NTR-Niño1+2 and NTR-Niño3 appear to be relatively strong. We then perform empirical orthogonal function (EOF) analysis on SSHA in the South China Sea and observe that the 2nd principal component (PC) time series and NTR strongly correlate. However, direct use of PC time series is computationally costly. Instead, we use SSHA information relating to the second EOF mode to create SCSI without performing EOF analysis. The correlation of SCSI-NTR is negatively strong. Lastly, we forecast NTR using SARIMAX models with Niño1+2, Niño3, and SCSI as inputs. The best model was SARIMAX (1, 0, 1)(0, 0, 2)12 using SCSI and Nino3 as inputs with AIC = 2368.705, RMSE = 51.167 mm per month and $R^2$ = 0.732. Result raises capacity for effective climate change-related planning and management in the area. Full article

This study investigates the presence and concentrations of emerging and persistent contaminants (EPCs) in rainwater and throughfall water collected from urban areas and agricultural lands in northern Thailand. It focuses on one daily-use compound (caffeine), two industrial compounds (4-nitrophenol and tris(2-butoxyethyl) phosphate (TBEP)), and three agrichemicals (atrazine, fenobucarb, and 2,4-D). Additionally, information is provided regarding the presence of acetaminophen, fexofenadine, diphenhydramine, and gabapentin. Small differences in the chemical composition of the six main contaminants were observed between rainwater and forest throughfall water. However, significant variations were found in the concentration ranges of each EPC. In most cases, throughfall samples exhibited slightly higher concentrations, suggesting a limited contribution from dry deposition compared to rainfall. Limited reliable evidence was found concerning seasonal patterns in EPC concentrations in precipitation (rainfall and throughfall) and surface water samples in remote ponds and reservoirs. The transportation of EPCs via rainwater appears to vary among the compounds tested and is likely to vary from one rainfall event to another, rather than showing a strong and common seasonal response within the monsoon rainfall regime. These findings suggest that the transport of EPCs to remote areas via rainfall does occur for some EPCs. However, the dominance of this process over other transport mechanisms could not be determined with high confidence. Full article

Phlebopus portentosus is a favorite wild, edible mushroom in the tropical region of China and northern Thailand. P. portentosus is the only bolete in the Boletales order that has been commercially cultivated. Sclerotia produced by the mushroom are often found in its natural habitats and cultivated media. These sclerotia play a key role in its life cycle. However, the regularity and growth characteristics of the sclerotium are unknown. In this paper, the whole process of birth, growth, death and rebirth of the sclerotium of P. portentosus under natural and lab conditions is reported for the first time. Sclerotium formation in nature is due to environmental stress, such as drought or low temperature. The less rainfall, the more sclerotia are produced. It appears that a lower temperature can also initiate sclerotium formation; however, the relationship between sclerotium formation and temperature is not as clear as that between sclerotium formation and rainfall. Under artificial conditions, the sclerotium formation of P. portentosus is related to the fungus’ physiological maturation. The presence of sclerotia is always accompanied by the exudation of liquid droplets on the colony. The results of this study should provide a platform for research on the importance of sclerotium formation in the life cycle of P. portentosus. Full article

Climate change will affect Southeast Asian countries, particularly Thailand. There are still insufficient studies on rainfall, streamflow, and future floods in the Upper Nan Watershed, northern Thailand. This study examined how future climate change will affect the rainfall, streamflow, and flooding in the Upper Nan Watershed. SWAT and HEC-RAS models were utilized to assess the future streamflow and flooding in this area. The models used data from 1980–2020, which were taken from seven Upper Nan meteorological stations and two discharge stations. In this study, the impact of future climate change was predicted using three GCMs, under RCP4.5 and RCP8.5 scenarios. The historical data analyzed in this study indicated that rainfall in the study area has a positive trend. Climate change will increase further, from 18% to 19%, which will cause more fluctuations and lead to wetter conditions, both in the wet and dry seasons. Climate change delayed the hydrograph peak and the SWAT-modelled streamflow in the N1 and N64 stations by between 0.3% and 5.1%. RCP8.5 inundated all of the stations more than RCP4.5. Our models showed that in the medium future (2041–2060), the inundated area will be similar to that during the 100-year flood probability. Thus, monitoring and preparation are necessary to avoid repeating the considerable 2011 flood losses in Thailand. Full article

Flash floods are water-related disasters that cause damage to properties, buildings, and infrastructures in the flow path. Flash floods often occur within a short period of time following intense rainfall in the high, mountainous area of northern Thailand. Therefore, the purpose of this study is to generate a flash flood susceptibility map using watershed morphometric parameters and hydrological approaches. In this study, the Pai River basin, located in Mae Hong Son in northern Thailand, is divided into 86 subwatersheds, and 23 morphometric parameters of the watershed are extracted from the digital elevation model (DEM). In addition, the soil conservation service curve number (SCS-CN) model is used to estimate the precipitation excess, and Snyder’s synthetic unit hydrograph method is used to estimate the time to peak and time of concentration. With respect to the rainfall dataset, in this study, we combined CHIRPS data (as satellite gridded precipitation data) with rainfall data measured within the study area for the runoff analysis. According to the analysis results, 25 out of 86 subwatersheds are classified as highly susceptible areas to flash floods. The similarities in the morphometric parameters representing watersheds in highly flash flood-susceptible areas indicate that this categorization included areas with high relief, high relief ratios, high ruggedness ratios, high stream frequencies, high texture ratios, high annual runoff, high peak discharge, low elongation ratios, and low lemniscates ratios. Full article

Floods are the most frequent type of natural disaster. Flash floods are one of the most common types of floods, caused by rapid and excessive rainfall. Normally, when a flash flood occurs, the water of the upstream river increases rapidly and flows to the downstream watersheds. The overflow of water increasingly submerges villages in the drainage basins. Flash flood early warning systems are required to mitigate losses. Water level monitoring stations can be installed at upstream river areas. However, telemetry stations face several challenges because the upstream river areas are far away and lack of public utilities (e.g., electric power and telephone lines). This research proposes hybrid effortless resilient operation stations, named HERO stations, in the flash flood early warning system. The HERO station was designed and developed with a modular design concept to be effortlessly customized and maintained. The HERO station adapts its working operation against the environmental changes to maintain a long working period with high data sensing accuracy. Moreover, the HERO station can switch its communication mode between the centralized and decentralized communication modes to increase availability. The network of the HERO stations has already been deployed in the northern part of Thailand. It results in improvements of the telemetry station’s availability. The HERO stations can adapt to environmental changes. The flash flood early warning messages can be disseminated to the villagers to increase the flood preparation time and to reduce flash flood damage. Full article

Landslide incidents frequently occur in the upper northern region of Thailand due to its topography, which is mostly mountainous with high slopes. In the past, when landslides happened in this area, they affected traffic accessibility for rescue and evacuation. For this reason, if the risk of landslides could be evaluated, it would help in the planning of preventive measures to mitigate the damage. This study was carried out to create and develop a risk estimation model using the artificial neural network (ANN) technique for landslides at the edge of the roadside, by collecting field data on past landslides in the study areas in Chiang Rai and Chiang Mai Provinces. A total of 9602 data points were collected. The variables for forecasting were: (1) land cover, (2) physiographic features, (3) slope angle, and (4) five-day cumulative rainfall. Two hidden layers were used to create the model. The number of nodes in the first and second hidden layers were five and one, respectively, which were derived from a total of 25 trials, and the highest accuracy achieved was 96.74%. When applying the model, a graph demonstrating the relationship between the landslide risk, rainfall, and the slopes of the road areas was obtained. The results show that high slopes result in more landslides than low slopes, and that rainfall is a major trigger for landslides on roads. The outcomes of the study could be used to create risk maps and provide information for developing warnings for high-slope mountain roads in the upper northern region of Thailand. Full article

Due to a continuous increase in global temperature, the climate has been changing without sign of alleviation. An increase in the air temperature has caused changes in the hydrologic cycle, which have been followed by several emergencies of natural extreme events around the world. Thailand is one of the countries that has incurred a huge loss in assets and lives from the extreme flood and drought events, especially in the northern part. Therefore, the purpose of this study was to assess the hydrological regime in the Yom and Nan River basins, affected by climate change as well as the possibility of extreme floods and droughts. The hydrological processes of the study areas were generated via the physically-based hydrological model, namely the Soil and Water Assessment Tool (SWAT) model. The projected climate conditions were dependent on the outputs of the Global Climate Models (GCMs) as the Representative Concentration Pathways (RCPs) 2.6 and 8.5 between 2021 and 2095. Results show that the average air temperature, annual rainfall, and annual runoff will be significantly increased in the intermediate future (2046–2070) onwards, especially under RCP 8.5. According to the Flow Duration Curve and return period of peak discharge, there are fluctuating trends in the occurrence of extreme floods and drought events under RCP 2.6 from the future (2021–2045) to the far future (2071–2095). However, under RCP 8.5, the extreme flood and drought events seem to be more severe. The probability of extreme flood remains constant from the reference period to the near future, then rises dramatically in the intermediate and the far future. The intensity of extreme droughts will be increased in the near future and decreased in the intermediate future due to high annual rainfall, then tending to have an upward trend in the far future. Full article

This study analyzed the sensitivity of rainfall patterns in South China and the Indochina Peninsula (ICP) using statistical simulations of observational data. Quantitative changes in rainfall patterns over the ICP were examined for both wet and dry seasons to identify hotspots sensitive to ocean warming in the Indo-Pacific sector. The rainfall variability was amplified by combined and/or independent effects of the El Niño–Southern Oscillation and the Indian Ocean Dipole (IOD). During the years of El Niño and a positive phase of the IOD, rainfall is less than usual in Thailand, Cambodia, southern Laos, and Vietnam. Conversely, during the years of La Niña and a negative phase of the IOD, rainfall throughout the ICP is above normal, except in parts of central Laos, northern Vietnam, and South China. This study also simulated the change of ICP rainfall in the wet and dry seasons with intentional IOD changes and verified IOD-sensitive hotspots through quantitative analysis. The results of this study provide a clear understanding both of the sensitivity of regional precipitation to the IOD and of the potential future impact of statistical changes regarding the IOD in terms of understanding regional impacts associated with precipitation in changing climates. Full article

Climate change is progressing and is now one of the most important global challenges for humanities. Water resources management is one of the key challenges to reduce disaster risk. In Northern Thailand, flood and drought have always occurred because of the climate change impact and non-systematic management in the conjunctive use of both sources of water. Therefore, this study aims to assess the climate change impact on surface water and groundwater of the Yom and Nan river basins, located in the upper part of Thailand. The surface water and groundwater regimes are generated by a fully coupled SWAT-MODFLOW model. The future climate scenarios are considered from the Representative Concentration Pathways (RCPs) 2.6 and 8.5, presented by the Coupled Model Intercomparison Project Phase 5 (CMIP5), in order to mainly focus on the minimum and maximum Green House Gas (GHG) emissions scenarios during the near future (2021–2045) periods. The results show that the average annual air temperature rises by approximately 0.5–0.6 °C and 0.9–1.0 °C under the minimum (RCP 2.6) and maximum (RCP 8.5) GHG emission scenarios, respectively. The annual rainfall, obtained from both scenarios, increased by the same range of 20–200 mm/year, on average. The summation of surface water (water yield) and groundwater recharge (water percolation) in the Yom river basin decreased by 443.98 and 316.77 million m³/year under the RCPs 2.6 and 8.5, respectively. While, in the Nan river basin, it is projected to increase by 355 million m³/year under RCP 2.6 but decrease by 20.79 million m³/year under RCP 8.5. These quantitative changes can directly impact water availability when evaluating the water demand for consumption, industry, and agriculture. Full article

Rainfall is related to economic growth and generally has beneficial impacts on dry and poor areas that are mostly dependent on rainfed agriculture. Thailand is a service-based, upper middle-income country with a tropical climate although rainfall varies regionally. The volume of precipitation in the northern and northeastern regions is rather low while the southern region has the highest rainfall due to its narrow topography running north-south bordering the Andaman Sea to the west and the Gulf of Thailand to the east. The present study explored the effect of rainfall on the growth of the gross provincial product (GPP) by economic sector and subsector using provincial-level panel data from 1995 to 2015. The feasible generalised least squares (FGLS) estimator with fixed effect was used in the regression models. We found that the main impacts of the weather occurred through rainfall and reduced GPP growth at the national level. For the sector level, the results showed that rainfall had a significant negative impact on the agricultural and service sectors while it had a positive but not significant impact on the industrial sector. However, rainfall remains vital in poor regions although it could be detrimental to certain subsectors in those regions. The results confirmed that the positive effects of rainfall mostly affected the economies of poor provinces and suggested that average rainfall could be the key climate effect on economic growth in Thailand. Full article