26 research outputs found

    The increased carbon storage changes with a decrease in phosphorus availability in the organic paddy soil

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    This study aimed to investigate the effect of organic rice farming on the various forms of inorganic phosphorus, the concentration of dissolved organic carbon (DOC) and carbon storage, and the relationship between DOC and P fractions in organic rice farming (ORF). The soil samples were taken from 11 organic plots, and three pseudo-replicates were sampled from individuals of various soil depths. The P-fractions, the soil organic carbon (SOC), DOC, and other soil properties were analyzed by standard methods from soils. The data were analyzed using One-way and Two-way ANOVA and tested using the least significant difference. The results showed that ORF soils had less labile P than conventional rice farming, while ORF had a higher average of DOC, SOC, and C stock than conventional rice soil (P<0.05). Organic fertilizers such as animal manure application and rice straw retention were used for ten years in the ORF. The agricultural practices of ORF would convince the amount of amorphous Fe and Al on soil minerals significantly and would increase the adsorption capacity of the soil mineral surfaces by organic fertilization. The Fe-P fraction responded to the increased adsorption capacity in the ORF and shown along with the DOC and P which were less than in ORF. Both of them were more adsorbed on the surface mineral. Meanwhile, the lower P for nutrient cycling in ORF soil, the lesser the decomposition of DOC and SOC, which then affected the increase of soil C storage

    āļ›āļĢāļ°āļŠāļīāļ—āļ˜āļīāļ āļēāļžāļ‚āļ­āļ‡āđāļšāļšāļˆāļģāļĨāļ­āļ‡āļ­āļļāļ—āļāļ§āļīāļ—āļĒāļē SWAT āđƒāļ™āļāļēāļĢāļˆāļģāļĨāļ­āļ‡āļ›āļĢāļīāļĄāļēāļ“āļ™āđ‰āļģāļ—āđˆāļēāļ‚āļ­āļ‡āļĨāļļāđˆāļĄāļ™āđ‰āļģāļ§āļąāļ‡Performance of SWAT Hydrologic Model for Runoff Simulation in Wang River Basin

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    āļāļēāļĢāļĻāļķāļāļĐāļēāļ™āļĩāđ‰āļĄāļĩāļ§āļąāļ•āļ–āļļāļ›āļĢāļ°āļŠāļ‡āļ„āđŒāđ€āļžāļ·āđˆāļ­āļ›āļĢāļ°āđ€āļĄāļīāļ™āļ›āļĢāļ°āļŠāļīāļ—āļ˜āļīāļ āļēāļžāļ‚āļ­āļ‡āđāļšāļšāļˆāļģāļĨāļ­āļ‡āļ­āļļāļ—āļāļ§āļīāļ—āļĒāļē SWAT āđƒāļ™āļāļēāļĢāļˆāļģāļĨāļ­āļ‡āļ›āļĢāļīāļĄāļēāļ“āļ™āđ‰āļģāļ—āđˆāļēāđƒāļ™āļĨāļļāđˆāļĄāļ™āđ‰āļģāļ§āļąāļ‡ āļ‚āđ‰āļ­āļĄāļđāļĨāļ™āļģāđ€āļ‚āđ‰āļēāļ›āļĢāļ°āļāļ­āļšāđ„āļ›āļ”āđ‰āļ§āļĒāļ‚āđ‰āļ­āļĄāļđāļĨāļ•āļĢāļ§āļˆāļ§āļąāļ”āļ­āļļāļ•āļļāļ™āļīāļĒāļĄāļ§āļīāļ—āļĒāļēāļĢāļēāļĒāļ§āļąāļ™ āļ›āļĩ āļž.āļĻ. 2547–2556 āļ‚āđ‰āļ­āļĄāļđāļĨāļāļēāļĢāđƒāļŠāđ‰āļ—āļĩāđˆāļ”āļīāļ™āļ›āļĩ āļž.āļĻ. 2552 āđāļĨāļ°āļ‚āđ‰āļ­āļĄāļđāļĨāļ āļđāļĄāļīāļ›āļĢāļ°āđ€āļ—āļĻ āļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒāļžāļ·āđ‰āļ™āļ—āļĩāđˆāļĨāļļāđˆāļĄāļ™āđ‰āļģ āđāļšāđˆāļ‡āđ€āļ›āđ‡āļ™ 18 āļĨāļļāđˆāļĄāļ™āđ‰āļģāļĒāđˆāļ­āļĒ āđāļĨāļ°āļāļģāļŦāļ™āļ”āļˆāļļāļ”āļ—āļēāļ‡āļ­āļ­āļāļ‚āļ­āļ‡āļĨāļļāđˆāļĄāļ™āđ‰āļģāļ—āļĩāđˆāļĄāļĩāļ‚āđ‰āļ­āļĄāļđāļĨāļ•āļĢāļ§āļˆāļ§āļąāļ”āļ›āļĢāļīāļĄāļēāļ“āļ™āđ‰āļģāļ—āđˆāļēāļĢāļēāļĒāļ§āļąāļ™āļˆāļģāļ™āļ§āļ™ 10 āļˆāļļāļ” āđ€āļžāļ·āđˆāļ­āđƒāļŠāđ‰āļŠāļ­āļšāđ€āļ—āļĩāļĒāļšāđāļšāļšāļˆāļģāļĨāļ­āļ‡ SWAT āļ™āļ­āļāļˆāļēāļāļ™āļĩāđ‰āļĒāļąāļ‡āđ„āļ”āđ‰āļ—āļģāļāļēāļĢāļ›āļĢāļąāļšāļ„āđˆāļēāļžāļēāļĢāļēāļĄāļīāđ€āļ•āļ­āļĢāđŒāļ•āđˆāļēāļ‡āđ†āļ—āļĩāđˆāđ€āļ›āđ‡āļ™āļ‚āđ‰āļ­āļĄāļđāļĨāđ€āļŠāļīāļ‡āļāļēāļĒāļ āļēāļžāļ‚āļ­āļ‡āļĨāļļāđˆāļĄāļ™āđ‰āļģ āđ„āļ”āđ‰āđāļāđˆ āļ™āđ‰āļģāļœāļīāļ§āļ”āļīāļ™ āļ™āđ‰āļģāđƒāļ•āđ‰āļ”āļīāļ™ āļĨāļļāđˆāļĄāļ™āđ‰āļģ āļ”āļīāļ™ āđāļĨāļ°āļāļēāļĢāđ„āļŦāļĨāđƒāļ™āļ—āļēāļ‡āļ™āđ‰āļģ āđ€āļžāļ·āđˆāļ­āđ€āļžāļīāđˆāļĄāļ›āļĢāļ°āļŠāļīāļ—āļ˜āļīāļ āļēāļžāļ‚āļ­āļ‡āđāļšāļšāļˆāļģāļĨāļ­āļ‡āļŠāļēāļĄāļēāļĢāļ–āļˆāļģāļĨāļ­āļ‡āđƒāļ™āļāļēāļĢāļˆāļģāļĨāļ­āļ‡āļ›āļĢāļīāļĄāļēāļ“āļ™āđ‰āļģāļ—āđˆāļē āļˆāļēāļāļœāļĨāļāļēāļĢāļĻāļķāļāļĐāļēāļžāļšāļ§āđˆāļēāđāļšāļšāļˆāļģāļĨāļ­āļ‡ SWAT āļĄāļĩāļ›āļĢāļ°āļŠāļīāļ—āļ˜āļīāļ āļēāļžāđƒāļ™āļāļēāļĢāļˆāļģāļĨāļ­āļ‡āļ›āļĢāļīāļĄāļēāļ“āļ™āđ‰āļģāļ—āđˆāļēāļ‚āļ­āļ‡āļĨāļļāđˆāļĄāļ™āđ‰āļģāļ§āļąāļ‡āđƒāļ™āđ€āļāļ“āļ‘āđŒāļ—āļĩāđˆāļĒāļ­āļĄāļĢāļąāļšāđ„āļ”āđ‰āđ‚āļ”āļĒāđ€āļ‰āļžāļēāļ°āđƒāļ™āļĨāļļāđˆāļĄāļ™āđ‰āļģāļĒāđˆāļ­āļĒāļ—āļĩāđˆāđ„āļĄāđˆāļĄāļĩāđ€āļ‚āļ·āđˆāļ­āļ™āļ•āļ­āļ™āļ›āļĨāļēāļĒāļ‚āļ­āļ‡āļĨāļļāđˆāļĄāļ™āđ‰āļģāļ§āļąāļ‡āđāļĨāļ°āđ€āļŦāļĄāļēāļ°āļŠāļĄāđƒāļ™āļāļēāļĢāļˆāļģāļĨāļ­āļ‡āļ›āļĢāļīāļĄāļēāļ“āļ™āđ‰āļģāļ—āđˆāļēāđƒāļ™āļŠāđˆāļ§āļ‡āļĪāļ”āļđāļāļ™āļĄāļēāļāļāļ§āđˆāļēāļĪāļ”āļđāđāļĨāđ‰āļ‡ āļ­āļĒāđˆāļēāļ‡āđ„āļĢāļāđ‡āļ•āļēāļĄ āđāļšāļšāļˆāļģāļĨāļ­āļ‡ SWAT āļĒāļąāļ‡āļĄāļĩāļ‚āđ‰āļ­āļˆāļģāļāļąāļ”āđƒāļ™āļāļēāļĢāļˆāļģāļĨāļ­āļ‡āļ›āļĢāļīāļĄāļēāļ“āļ™āđ‰āļģāļ—āđˆāļēāļĢāļēāļĒāđ€āļ”āļ·āļ­āļ™āļŠāļđāļ‡āļŠāļļāļ” āđ‚āļ”āļĒāđ€āļ‰āļžāļēāļ°āđƒāļ™āđ€āļ”āļ·āļ­āļ™āļ—āļĩāđˆāļĄāļĩāļ›āļąāļāļŦāļēāļ™āđ‰āļģāļ—āđˆāļ§āļĄāļ›āļĢāļīāļĄāļēāļ“āļ™āđ‰āļģāļ—āđˆāļēāļŠāļđāļ‡āļŠāļļāļ”āļĢāļēāļĒāđ€āļ”āļ·āļ­āļ™āļˆāļēāļāđāļšāļšāļˆāļģāļĨāļ­āļ‡āļĄāļĩāļ„āđˆāļēāļ•āđˆāļģāļāļ§āđˆāļēāļ„āđˆāļēāļ•āļĢāļ§āļˆāļ§āļąāļ”āļˆāļĢāļīāļ‡The purpose of this study was to evaluate the efficiency of SWAT hydrological model for runoff simulation in Wang River Basin. The input data includes the daily meteorological observation data during the year 2004–2013, land-use of the year 2009 and topographical data. The analyses were divided the whole basin into 18 sub-basins and defined the basin outlets at 10 stations of observed daily runoff data to calibrate the SWAT model. In addition, the basin physical parameters including surface water, groundwater, watershed, soil, and channel flow, have been adjusted to improve the efficiency of the model in the runoff simulation. The results showed that the SWAT model was effective in simulating the runoff of the Wang River Basin, especially in the non-dam sub-basin and the end of the Wang River Basin. The model is appropriate to simulate the runoff during the wet season rather than the dry season. However, the SWAT model has limitations on the maximum monthly runoff model, especially in the month of flood problem, the maximum monthly runoff from the model was lower than the observed value

    āļāļēāļĢāļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒāļœāļĨāļāļĢāļ°āļ—āļšāļˆāļēāļāļāļēāļĢāđ€āļ›āļĨāļĩāđˆāļĒāļ™āđāļ›āļĨāļ‡āļŠāļ āļēāļžāļ āļđāļĄāļīāļ­āļēāļāļēāļĻāļ•āđˆāļ­āļĻāļąāļāļĒāļ āļēāļžāļāļēāļĢāđ€āļāļīāļ”āđ„āļŸāļ›āđˆāļēāđƒāļ™āļˆāļąāļ‡āļŦāļ§āļąāļ”āđ€āļŠāļĩāļĒāļ‡āđƒāļŦāļĄāđˆāđ‚āļ”āļĒāđƒāļŠāđ‰āđāļšāļšāļˆāļģāļĨāļ­āļ‡āļāļēāļĢāļ–āļ”āļ–āļ­āļĒAnalysis of Impact of Climate Change on Forest Fire Potential in Chiang Mai by Using of Regression Model

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    āļāļēāļĢāļĻāļķāļāļĐāļēāļ™āļĩāđ‰āļĄāļĩāļ§āļąāļ•āļ–āļļāļ›āļĢāļ°āļŠāļ‡āļ„āđŒāđ€āļžāļ·āđˆāļ­āļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒāļœāļĨāļāļĢāļ°āļ—āļšāļˆāļēāļāļāļēāļĢāđ€āļ›āļĨāļĩāđˆāļĒāļ™āđāļ›āļĨāļ‡āļŠāļ āļēāļžāļ āļđāļĄāļīāļ­āļēāļāļēāļĻāļ•āđˆāļ­āļĻāļąāļāļĒāļ āļēāļžāđƒāļ™āļāļēāļĢāđ€āļāļīāļ”āđ„āļŸāļ›āđˆāļēāđƒāļ™āļˆāļąāļ‡āļŦāļ§āļąāļ”āđ€āļŠāļĩāļĒāļ‡āđƒāļŦāļĄāđˆ āļ”āđ‰āļ§āļĒāļāļēāļĢāļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒāđāļĨāļ°āļŠāļĢāđ‰āļēāļ‡āđāļšāļšāļˆāļģāļĨāļ­āļ‡āļāļēāļĢāļ–āļ”āļ–āļ­āļĒāļ—āļąāđ‰āļ‡āđāļšāļšāđ€āļŠāđ‰āļ™āļ•āļĢāļ‡āđāļĨāļ°āđ„āļĄāđˆāđ€āļ›āđ‡āļ™āđ€āļŠāđ‰āļ™āļ•āļĢāļ‡ āđ‚āļ”āļĒāđƒāļŠāđ‰āļ‚āđ‰āļ­āļĄāļđāļĨāļ•āļĢāļ§āļˆāļ§āļąāļ”āļŠāļ āļēāļžāļ āļđāļĄāļīāļ­āļēāļāļēāļĻāđāļĨāļ°āļ‚āđ‰āļ­āļĄāļđāļĨāļžāļ·āđ‰āļ™āļ—āļĩāđˆāđ€āļœāļēāđ„āļŦāļĄāđ‰āļ—āļąāđ‰āļ‡āļˆāļēāļāļ āļēāļžāļ”āļēāļ§āđ€āļ—āļĩāļĒāļĄāđāļĨāļ°āļˆāļļāļ”āļ„āļ§āļēāļĄāļĢāđ‰āļ­āļ™āđƒāļ™āļŠāđˆāļ§āļ‡āļĪāļ”āļđāđ„āļŸāļ›āđˆāļē (āļ˜āļąāļ™āļ§āļēāļ„āļĄâ€“āļžāļĪāļĐāļ āļēāļ„āļĄ) āđƒāļ™āļŠāđˆāļ§āļ™āļāļēāļĢāļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒāđāļ™āļ§āđ‚āļ™āđ‰āļĄāļāļēāļĢāđ€āļāļīāļ”āļžāļ·āđ‰āļ™āļ—āļĩāđˆāđ„āļŸāļ›āđˆāļēāđƒāļ™āļ­āļ™āļēāļ„āļ•āđƒāļŠāđ‰āļ‚āđ‰āļ­āļĄāļđāļĨāļ„āļ§āļēāļĄāļŠāļ·āđ‰āļ™āļŠāļąāļĄāļžāļąāļ—āļ˜āđŒāļˆāļēāļāđāļšāļšāļˆāļģāļĨāļ­āļ‡ WRF-ECHAM5 āļ™āļģāđ€āļ‚āđ‰āļēāļŠāļĄāļāļēāļĢāļ–āļ”āļ–āļ­āļĒāļ‚āđ‰āļēāļ‡āļ•āđ‰āļ™ āļ‹āļķāđˆāļ‡āļˆāļēāļāļāļēāļĢāļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒāđ€āļ›āļĢāļĩāļĒāļšāđ€āļ—āļĩāļĒāļšāļžāļšāļ§āđˆāļēāđāļšāļšāļˆāļģāļĨāļ­āļ‡āļāļēāļĢāļ–āļ”āļ–āļ­āļĒāđāļšāļšāđ„āļĄāđˆāđ€āļ›āđ‡āļ™āđ€āļŠāđ‰āļ™āļ•āļĢāļ‡āđ€āļ›āđ‡āļ™āļ§āļīāļ˜āļĩāļ—āļĩāđˆāļĒāļ·āļ”āļŦāļĒāļļāđˆāļ™āđāļĨāļ°āđ€āļŦāļĄāļēāļ°āļŠāļĄāļāļ§āđˆāļēāļāļēāļĢāļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒāļ”āđ‰āļ§āļĒāļ§āļīāļ˜āļĩāļāļēāļĢāļ–āļ”āļ–āļ­āļĒāđāļšāļšāđ€āļŠāđ‰āļ™āļ•āļĢāļ‡ āđ‚āļ”āļĒāļ›āļąāļˆāļˆāļąāļĒāļ—āļĩāđˆāļŠāļēāļĄāļēāļĢāļ–āļ™āļģāđ€āļ‚āđ‰āļēāļŠāļĄāļāļēāļĢāļ–āļ”āļ–āļ­āļĒāļĄāļĩāđ€āļžāļĩāļĒāļ‡āļ„āļ§āļēāļĄāļŠāļ·āđ‰āļ™āļŠāļąāļĄāļžāļąāļ—āļ˜āđŒāđ€āļžāļĩāļĒāļ‡āļ›āļąāļˆāļˆāļąāļĒāđ€āļ”āļĩāļĒāļ§ āļŠāđˆāļ§āļ™āļ›āļąāļˆāļˆāļąāļĒāļ•āļąāļ§āđāļ›āļĢāļ āļđāļĄāļīāļ­āļēāļāļēāļĻāļ­āļ·āđˆāļ™āđ† āđ„āļĄāđˆāļŠāļēāļĄāļēāļĢāļ–āļ™āļģāđ€āļ‚āđ‰āļēāđ„āļ”āđ‰āđ€āļ™āļ·āđˆāļ­āļ‡āļˆāļēāļāđ„āļĄāđˆāļŠāļēāļĄāļēāļĢāļ–āļĒāļ­āļĄāļĢāļąāļšāļ—āļēāļ‡āļŠāļ–āļīāļ•āļīāđ„āļ”āđ‰ āđ€āļĄāļ·āđˆāļ­āļ™āļģāļŠāļĄāļāļēāļĢāļ–āļ”āļ–āļ­āļĒāđāļšāļšāđ„āļĄāđˆāđ€āļ›āđ‡āļ™āđ€āļŠāđ‰āļ™āļ•āļĢāļ‡āļ—āļĩāđˆāđ„āļ”āđ‰āļ—āļģāļāļēāļĢāļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒāļĄāļēāļ—āļ”āļŠāļ­āļšāļāļąāļšāļ‚āđ‰āļ­āļĄāļđāļĨāļ„āļ§āļēāļĄāļŠāļ·āđ‰āļ™āļŠāļąāļĄāļžāļąāļ—āļ˜āđŒāļˆāļēāļāđāļšāļšāļˆāļģāļĨāļ­āļ‡āļ āļđāļĄāļīāļ­āļēāļāļēāļĻāļ āļđāļĄāļīāļ āļēāļ„ WRFECHAM5 āļžāļšāļ§āđˆāļēāļ„āļ§āļēāļĄāļŠāļ·āđ‰āļ™āļĄāļĩāđāļ™āļ§āđ‚āļ™āđ‰āļĄāļĨāļ”āļĨāļ‡āđƒāļ™āļ­āļąāļ•āļĢāļē 1.3% āđāļĨāļ°āļĄāļĩāļ„āļ§āļēāļĄāđāļ›āļĢāļ›āļĢāļ§āļ™āļĢāļ°āļŦāļ§āđˆāļēāļ‡āļ›āļĩāļ‚āļ­āļ‡āļ„āļ§āļēāļĄāļŠāļ·āđ‰āļ™āļŠāļđāļ‡ āļ‹āļķāđˆāļ‡āļ—āļģāđƒāļŦāđ‰āđ€āļĄāļ·āđˆāļ­āļ™āļģāļ„āļ§āļēāļĄāļŠāļ·āđ‰āļ™āļˆāļēāļāđāļšāļšāļˆāļģāļĨāļ­āļ‡āđ€āļ‚āđ‰āļēāļŠāļĄāļāļēāļĢāļ–āļ”āļ–āļ­āļĒāđāļšāļšāđ„āļĄāđˆāđ€āļ›āđ‡āļ™āđ€āļŠāđ‰āļ™āļ•āļĢāļ‡āļ—āļĩāđˆāđ„āļ”āđ‰āļŠāđˆāļ‡āļœāļĨāļ—āļģāđƒāļŦāđ‰āļĻāļąāļāļĒāļ āļēāļžāđƒāļ™āļāļēāļĢāđ€āļāļīāļ”āđ„āļŸāļ›āđˆāļēāđƒāļ™āļ­āļ™āļēāļ„āļ•āļĄāļĩāđāļ™āļ§āđ‚āļ™āđ‰āļĄāļĨāļ”āļĨāļ‡ āļ­āļĒāđˆāļēāļ‡āđ„āļĢāļāđ‡āļ•āļēāļĄ āļˆāļēāļāļāļēāļĢāļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒāļžāļšāļ§āđˆāļēāļ„āļ§āļēāļĄāđāļ›āļĢāļ›āļĢāļ§āļ™āļ‚āļ­āļ‡āļŠāļ āļēāļžāļ āļđāļĄāļīāļ­āļēāļāļēāļĻāđāļšāļšāļŠāļļāļ”āļ‚āļĩāļ”āđƒāļ™āļ­āļ™āļēāļ„āļ•āļĄāļĩāđāļ™āļ§āđ‚āļ™āđ‰āļĄāļĄāļēāļāļ‚āļķāđ‰āļ™āđ‚āļ”āļĒāļĄāļĩāļ„āļēāļšāđƒāļ™āļāļēāļĢāđ€āļāļīāļ”āļ—āļļāļ 5 āļ›āļĩ āđāļĨāļ°āļŠāđˆāļ‡āļœāļĨāļ•āđˆāļ­āļ„āļ§āļēāļĄāđāļ›āļĢāļ›āļĢāļ§āļ™āļĨāļ°āļ„āļ§āļēāļĄāļĢāļļāļ™āđāļĢāļ‡āļ‚āļ­āļ‡āļĻāļąāļāļĒāļ āļēāļžāļāļēāļĢāđ€āļāļīāļ”āđ„āļŸāļ›āđˆāļē āļ™āļ­āļāļˆāļēāļāļ™āļĩāđ‰āļĒāļąāļ‡āļžāļšāļ§āđˆāļēāļĻāļąāļāļĒāļ āļēāļžāđƒāļ™āļāļēāļĢāđ€āļāļīāļ”āđ„āļŸāļ›āđˆāļēāđƒāļ™āļ­āļ™āļēāļ„āļ•āļĄāļĩāļŠāđˆāļ§āļ‡āđ€āļ§āļĨāļēāļāļēāļĢāđ€āļāļīāļ”āļ—āļĩāđˆāđ€āļĢāđ‡āļ§āļ‚āļķāđ‰āļ™āļāļ§āđˆāļēāđ€āļ”āļīāļĄ āļ‹āļķāđˆāļ‡āđ€āļ›āđ‡āļ™āļœāļĨāļĄāļēāļˆāļēāļāļ„āļ§āļēāļĄāļŠāļ·āđ‰āļ™āđƒāļ™āđ€āļ”āļ·āļ­āļ™āļĄāļāļĢāļēāļ„āļĄāđāļĨāļ°āđ€āļ”āļ·āļ­āļ™āļāļļāļĄāļ āļēāļžāļąāļ™āļ˜āđŒāļĄāļĩāđāļ™āļ§āđ‚āļ™āđ‰āļĄāļĨāļ”āļĨāļ‡āđƒāļ™āļ‚āļ“āļ°āļ—āļĩāđˆāđ€āļ”āļ·āļ­āļ™āļ­āļ·āđˆāļ™āđ† āļĄāļĩāđāļ™āļ§āđ‚āļ™āđ‰āļĄāļ„āļ§āļēāļĄāļŠāļ·āđ‰āļ™āđ€āļžāļīāđˆāļĄāļ‚āļķāđ‰āļ™This study aims to analyze the impact of climate change on future forest fire potential in Chiang Mai Province, analyzed by regression analysis with the linear and non-linear approach. Following the approach used to observe weather data and burn scar area from both MODIS sensor and forest fire hotspot. In a part of burn area trend analysis in the future used absolute humidity data from WRF-ECHAM5 model, which used into following regression model. The result of the comparative analysis, nonlinear regression models are more flexible and appropriate than linear regression analysis. Climatic factors that can be applied to the regression equation are relative humidity only. While other climate variables could not be imported because the results were not statistically unacceptable. When applied the acceptable nonlinear regression model with the relative humidity data from the WRF-ECHAM5 regional climate model, it was found that relative humidity decreased by 1.3%, and there is high yearly variation in relative humidity, which leads to the decrease in the forest fires potential in the future when the modeled relative humidity is applied to the non-linear regression equation. However, the analysis found that the variability of extreme climate in the future is more likely to occur every 5 years, and is likely to affect the variability and severity of the potential forest fires. In addition, the potential for future forest fires is much faster than ever before. As a result of the humidity in January and February tend to decrease while other months tend to increase humidity

    Study of the Interaction of Dissolved Organic Carbon, Available Nutrients, and Clay Content Driving Soil Carbon Storage in the Rice Rotation Cropping System in Northern Thailand

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    The appropriate management of crop residues in a rice rotation cropping system (RRCS) can promote carbon storage and contribute to soil health. The objective of this study was to determine and analyze the amount of organic carbon in the soil, the amount of labile carbon in a dissolved state in the soil, and the physicochemical properties of the soil and their relationship with soil organic carbon dynamics under the RRCS in northern Thailand. The RRCS can be divided into the following four categories by pattern: (1) Rice_F (rice (Oryza sativa) followed by a fallow period); (2) Rice_S (rice followed by shallots (Allium cepa L.); (3) Rice_Mixed crop (rice followed by tobacco (Nicotiana tabacum), vegetables, or maize (Zea mays)); and (4) Rice_P (rice followed by potatoes (Solanum tuberosum)). These patterns can be classified according to the dissolved organic carbon (DOC), the availability of nutrients from fertilization, and clay contents. In our study, the Rice-F and Rice-S patterns led to higher soil organic carbon (SOC) and dissolved organic carbon (DOC) in the soil, but when the Rice-P pattern was followed, the soil had a lower clay content, lower available phosphorus (Avail P), the lowest DOC, and high contents of calcium (Ca2+) and magnesium (Mg2+). This study also revealed that on the basis of relationships, clay content, Avail P, and DOC were the most important factors for the formation of SOC, while Ca2+ and Mg2+ were the subordinate factors for the decreased formation of SOC and carbon storage when the RRCS was followed. In addition, low SOC/clay when the Rice-P pattern was followed could reflect carbon saturation, while the percentages of DOC/SOC could indicate the decomposition and formation of SOC

    āļ„āļ§āļēāļĄāļŠāļąāļĄāļžāļąāļ™āļ˜āđŒāļ‚āļ­āļ‡āļˆāļļāļ”āļ„āļ§āļēāļĄāļĢāđ‰āļ­āļ™āđƒāļ™āļžāļ·āđ‰āļ™āļ—āļĩāđˆāļ āļēāļ„āđ€āļŦāļ™āļ·āļ­āļ•āļ­āļ™āļšāļ™āļ‚āļ­āļ‡āļ›āļĢāļ°āđ€āļ—āļĻāđ„āļ—āļĒāđāļĨāļ°āļžāļ·āđ‰āļ™āļ—āļĩāđˆāđ‚āļ”āļĒāļĢāļ­āļšāļ•āđˆāļ­āļ„āđˆāļēāļ„āļ§āļēāļĄāđ€āļ‚āđ‰āļĄāļ‚āđ‰āļ™ PM2.5: āļāļĢāļ“āļĩāļĻāļķāļāļĐāļēāļŠāđˆāļ§āļ‡āļĪāļ”āļđāļŦāļĄāļ­āļāļ„āļ§āļąāļ™ āļ›āļĩ āļž.āļĻ. 2562 Relationship of Fire Hotspot, PM2.5 Concentrations, and Surrounding Areas in Upper Northern Thailand: A Case S

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    āļāļēāļĢāļ§āļīāļˆāļąāļĒāļ™āļĩāđ‰āļĄāļĩāļ§āļąāļ•āļ–āļļāļ›āļĢāļ°āļŠāļ‡āļ„āđŒāđ€āļžāļ·āđˆāļ­āļĻāļķāļāļĐāļēāļœāļĨāļāļĢāļ°āļ—āļšāļ‚āļ­āļ‡āļ›āļĢāļīāļĄāļēāļ“āļˆāļļāļ”āļ„āļ§āļēāļĄāļĢāđ‰āļ­āļ™āļ•āđˆāļ­āļ„āđˆāļēāļ„āļ§āļēāļĄāđ€āļ‚āđ‰āļĄāļ‚āđ‰āļ™ PM2.5 āđƒāļ™āļžāļ·āđ‰āļ™āļ—āļĩāđˆāļ āļēāļ„āđ€āļŦāļ™āļ·āļ­āļ•āļ­āļ™āļšāļ™āļ‚āļ­āļ‡āļ›āļĢāļ°āđ€āļ—āļĻāđ„āļ—āļĒ āļŠāđˆāļ§āļ‡āļ§āļąāļ™āļ—āļĩāđˆ 1 āļĄāļāļĢāļēāļ„āļĄ – 31 āļžāļĪāļĐāļ āļēāļ„āļĄ āļž.āļĻ. 2562 āđ‚āļ”āļĒāļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒāļ›āļĢāļīāļĄāļēāļ“āđāļĨāļ°āļ„āļ§āļēāļĄāļŦāļ™āļēāđāļ™āđˆāļ™āļˆāļļāļ”āļ„āļ§āļēāļĄāļĢāđ‰āļ­āļ™āđƒāļ™āļžāļ·āđ‰āļ™āļ—āļĩāđˆāļĻāļķāļāļĐāļēāđāļĨāļ°āļžāļ·āđ‰āļ™āļ—āļĩāđˆāđ‚āļ”āļĒāļĢāļ­āļš āļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒāļ„āļ§āļēāļĄāļŠāļąāļĄāļžāļąāļ™āļ˜āđŒāļ•āļēāļĄāđ€āļ§āļĨāļēāđāļĨāļ°āļŠāļąāļĄāļ›āļĢāļ°āļŠāļīāļ—āļ˜āļīāđŒāļŠāļŦāļŠāļąāļĄāļžāļąāļ™āļ˜āđŒ (r) āļ‚āļ­āļ‡āļ„āđˆāļēāļ„āļ§āļēāļĄāđ€āļ‚āđ‰āļĄāļ‚āđ‰āļ™ PM2.5 āļāļąāļšāļ›āļĢāļīāļĄāļēāļ“āļˆāļļāļ”āļ„āļ§āļēāļĄāļĢāđ‰āļ­āļ™āļˆāļēāļāļ āļēāļžāļ–āđˆāļēāļĒāļ”āļēāļ§āđ€āļ—āļĩāļĒāļĄ āđāļĨāļ°āļ‚āđ‰āļ­āļĄāļđāļĨāļ•āļĢāļ§āļˆāļ§āļąāļ”āļ›āļąāļˆāļˆāļąāļĒāļ—āļēāļ‡āļ­āļļāļ•āļļāļ™āļīāļĒāļĄāļ§āļīāļ—āļĒāļēāļˆāļēāļ 9 āļŠāļ–āļēāļ™āļĩ āļœāļĨāļāļēāļĢāļ§āļīāļˆāļąāļĒāļžāļšāļ§āđˆāļē āļˆāļļāļ”āļ„āļ§āļēāļĄāļĢāđ‰āļ­āļ™āđƒāļ™āļžāļ·āđ‰āļ™āļ—āļĩāđˆāļĻāļķāļāļĐāļēāļĄāļĩāļ›āļĢāļīāļĄāļēāļ“āđ€āļžāļīāđˆāļĄāļŠāļđāļ‡āđƒāļ™āļŠāđˆāļ§āļ‡āļ—āļĩāđˆāļ„āđˆāļēāļ„āļ§āļēāļĄāđ€āļ‚āđ‰āļĄāļ‚āđ‰āļ™ PM2.5 āļ­āļĒāļđāđˆāđƒāļ™āđ€āļāļ“āļ‘āđŒāļŠāđˆāļ‡āļœāļĨāļāļĢāļ°āļ—āļšāļ•āđˆāļ­āļŠāļļāļ‚āļ āļēāļž āđ‚āļ”āļĒāļžāļšāļŦāļ™āļēāđāļ™āđˆāļ™āļŠāļđāļ‡āļšāļĢāļīāđ€āļ§āļ“āļĢāļ­āļĒāļ•āđˆāļ­āļĢāļ°āļŦāļ§āđˆāļēāļ‡āļˆāļąāļ‡āļŦāļ§āļąāļ” āļžāļ·āđ‰āļ™āļ—āļĩāđˆāļ›āđˆāļēāđāļĨāļ°āļžāļ·āđ‰āļ™āļ—āļĩāđˆāđ€āļāļĐāļ•āļĢāđƒāļāļĨāđ‰āđ€āļ„āļĩāļĒāļ‡ āļŠāđˆāļ§āļ™āļžāļ·āđ‰āļ™āļ—āļĩāđˆāđ‚āļ”āļĒāļĢāļ­āļšāļžāļšāļˆāļļāļ”āļ„āļ§āļēāļĄāļĢāđ‰āļ­āļ™āļŦāļ™āļēāđāļ™āđˆāļ™āļŠāļđāļ‡āļšāļĢāļīāđ€āļ§āļ“āđƒāļāļĨāđ‰āļāļąāļšāļžāļ·āđ‰āļ™āļ—āļĩāđˆāļĻāļķāļāļĐāļēāđƒāļ™āļ—āļēāļ‡āđ€āļŦāļ™āļ·āļ­ āļ„āļ§āļēāļĄāđ€āļ‚āđ‰āļĄāļ‚āđ‰āļ™ PM2.5 āļ‚āļ­āļ‡āļŠāļ–āļēāļ™āļĩāļŠāđˆāļ§āļ™āđƒāļŦāļāđˆāļĄāļĩāļ„āđˆāļēāļŠāļ­āļ”āļ„āļĨāđ‰āļ­āļ‡āļāļąāļšāļˆāļļāļ”āļ„āļ§āļēāļĄāļĢāđ‰āļ­āļ™āļ‚āļ­āļ‡āļžāļ·āđ‰āļ™āļ—āļĩāđˆāđ‚āļ”āļĒāļĢāļ­āļšāļĄāļēāļāļāļ§āđˆāļēāļˆāļļāļ”āļ„āļ§āļēāļĄāļĢāđ‰āļ­āļ™āļ‚āļ­āļ‡āļˆāļąāļ‡āļŦāļ§āļąāļ” āļ‹āļķāđˆāļ‡āđ€āļŦāđ‡āļ™āđ„āļ”āđ‰āļˆāļēāļāļ„āđˆāļē r āļ—āļĩāđˆāļŠāļ­āļ”āļ„āļĨāđ‰āļ­āļ‡āļāļąāļ™āđƒāļ™āđ€āļāļ“āļ‘āđŒāļ›āļēāļ™āļāļĨāļēāļ‡-āļŠāļđāļ‡ (r = 0.5 – 0.7) āļŠāđˆāļ§āļ™āļˆāļļāļ”āļ„āļ§āļēāļĄāļĢāđ‰āļ­āļ™āļ‚āļ­āļ‡āļˆāļąāļ‡āļŦāļ§āļąāļ”āļŠāļ­āļ”āļ„āļĨāđ‰āļ­āļ‡āļāļąāļšāļ„āđˆāļēāļ„āļ§āļēāļĄāđ€āļ‚āđ‰āļĄāļ‚āđ‰āļ™ PM2.5 āđ€āļŠāđˆāļ™āļāļąāļ™ āļ”āđ‰āļ§āļĒāļ„āđˆāļē r āļ—āļĩāđˆāļ™āđ‰āļ­āļĒāļāļ§āđˆāļē āļ‹āļķāđˆāļ‡āđāļŠāļ”āļ‡āđƒāļŦāđ‰āđ€āļŦāđ‡āļ™āļ–āļķāļ‡āļ­āļīāļ—āļ˜āļīāļžāļĨāļ‚āļ­āļ‡āđāļŦāļĨāđˆāļ‡āļāļģāđ€āļ™āļīāļ”āļˆāļēāļāļžāļ·āđ‰āļ™āļ—āļĩāđˆāđ‚āļ”āļĒāļĢāļ­āļšāļ—āļĩāđˆāļŠāđˆāļ‡āļœāļĨāļ•āđˆāļ­āļ„āđˆāļēāļ„āļ§āļēāļĄāđ€āļ‚āđ‰āļĄāļ‚āđ‰āļ™āđƒāļ™āļžāļ·āđ‰āļ™āļ—āļĩāđˆāļˆāļąāļ‡āļŦāļ§āļąāļ”āļ™āļąāđ‰āļ™ āđ† āļ„āđˆāļēāļ„āļ§āļēāļĄāđ€āļ‚āđ‰āļĄāļ‚āđ‰āļ™ PM2.5 āđƒāļ™āļŠāļ–āļēāļ™āļĩāļŠāđˆāļ§āļ™āđƒāļŦāļāđˆāđāļ›āļĢāļœāļāļœāļąāļ™āļāļąāļšāļ›āļąāļˆāļˆāļąāļĒāļ›āļĢāļīāļĄāļēāļ“āļ™āđ‰āļģāļāļ™āđāļĨāļ°āļ„āļ§āļēāļĄāđ€āļĢāđ‡āļ§āļĨāļĄThe objective of this research is to study the effects of thermal hotspots on PM2.5 concentrations in the upper northern of Thailand during 1 January–31 May 2019. The number and the density of fire hotspots of the examined and adjacent areas was investigated. The time-series relationships between PM2.5 concentrations, the number of satellite-based fire hotspots, and meteorological factors derived from 9 stations were analyzed. As results, the greater number of hotspots was correlated with increased levels of PM2.5 concentrations. Such conditions exhibit considerable impacts on health. High PM2.5 concentrations were specifically found around provincial boundaries, in forests, agricultural areas, as well as in Thailand’s neighboring countries. As for the surrounding areas, the areas that have high density of fire hotspots were found near investigated areas in the north region. Provincial fire hotspots were correlated to high PM2.5 concentration, with a lower r-value. The thermal hotspot locations from the surrounding areas have effects on provincial PM2.5 concentrations. Finally, the effect of meteorological factors on PM2.5 concentrations was analyzed. As a result, precipitation and wind speed have inverse effects on PM2.5 concentrations

    āļāļēāļĢāļ›āļĢāļąāļšāđāļāđ‰āļ„āļ§āļēāļĄāļ„āļĨāļēāļ”āđ€āļ„āļĨāļ·āđˆāļ­āļ™āļ›āļĢāļīāļĄāļēāļ“āļ™āđ‰āļģāļāļ™āļˆāļēāļāđāļšāļšāļˆāļģāļĨāļ­āļ‡ WRF-CFSR āđ‚āļ”āļĒāļ§āļīāļ˜āļĩ EOF āļ āļēāļ„āđ€āļŦāļ™āļ·āļ­āļ•āļ­āļ™āļšāļ™āļ‚āļ­āļ‡āļ›āļĢāļ°āđ€āļ—āļĻāđ„āļ—āļĒPrecipitation Bias Correction of WRF-CFSR Model by EOF Method Over Upper Northern Thailand

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    āļĢāļ°āļšāļšāđāļšāļšāļˆāļģāļĨāļ­āļ‡āļ āļđāļĄāļīāļ­āļēāļāļēāļĻāđ€āļ›āđ‡āļ™āļ‡āļēāļ™āļ—āļĩāđˆāļĄāļĩāļ„āļ§āļēāļĄāļ—āđ‰āļēāļ—āļēāļĒāđāļĨāļ°āļĄāļĩāļ„āļ§āļēāļĄāļĒāļēāļ āđ€āļ™āļ·āđˆāļ­āļ‡āļˆāļēāļāļāļēāļĢāļ›āļĢāļ°āļĄāļ§āļĨāļœāļĨāļˆāļēāļāđāļšāļšāļˆāļģāļĨāļ­āļ‡āļĄāļĩāļ„āļ§āļēāļĄāđ„āļĄāđˆāđāļ™āđˆāļ™āļ­āļ™āļ‹āļķāđˆāļ‡āđ€āļāļīāļ”āļˆāļēāļāļŦāļĨāļēāļĒāļ›āļąāļˆāļˆāļąāļĒāļŠāđˆāļ‡āļœāļĨāļ•āđˆāļ­āļ„āļ§āļēāļĄāļ„āļĨāļēāļ”āđ€āļ„āļĨāļ·āđˆāļ­āļ™āļ‚āļ­āļ‡āļœāļĨāļāļēāļĢāļˆāļģāļĨāļ­āļ‡āļ—āļąāđ‰āļ‡āđƒāļ™āđ€āļŠāļīāļ‡āļžāļ·āđ‰āļ™āļ—āļĩāđˆāđāļĨāļ°āđ€āļ§āļĨāļē āļ‰āļ°āļ™āļąāđ‰āļ™āđƒāļ™āļāļēāļĢāļĻāļķāļāļĐāļēāļ„āļĢāļąāđ‰āļ‡āļ™āļĩāđ‰āļˆāļķāļ‡āļĄāļĩāļ§āļąāļ•āļ–āļļāļ›āļĢāļ°āļŠāļ‡āļ„āđŒāđƒāļ™āļāļēāļĢāļ›āļĢāļ°āļĒāļļāļāļ•āđŒāđƒāļŠāđ‰āļ§āļīāļ˜āļĩāļāļēāļĢāļŦāļĢāļ·āļ­āđ€āļ—āļ„āļ™āļīāļ„āļāļēāļĢāļ›āļĢāļąāļšāđāļāđ‰āļ„āļ§āļēāļĄāļ„āļĨāļēāļ”āđ€āļ„āļĨāļ·āđˆāļ­āļ™āļŠāļģāļŦāļĢāļąāļšāļ›āļĢāļīāļĄāļēāļ“āļ™āđ‰āļģāļāļ™āļˆāļēāļāđāļšāļšāļˆāļģāļĨāļ­āļ‡āļŠāļ āļēāļžāļ­āļēāļāļēāļĻāļĢāļ°āļ”āļąāļšāļ āļđāļĄāļīāļ āļēāļ„ WRF-CFSR āđāļĨāļ°āļ›āļĢāļ°āđ€āļĄāļīāļ™āļ›āļĢāļ°āļŠāļīāļ—āļ˜āļīāļ āļēāļžāļ§āļīāļ˜āļĩāļāļēāļĢāļ›āļĢāļąāļšāđāļāđ‰āļ„āļ§āļēāļĄāļ„āļĨāļēāļ”āđ€āļ„āļĨāļ·āđˆāļ­āļ™āļ›āļĢāļīāļĄāļēāļ“āļ™āđ‰āļģāļāļ™āļˆāļēāļāđāļšāļšāļˆāļģāļĨāļ­āļ‡āļŠāļ āļēāļžāļ­āļēāļāļēāļĻāļĢāļ°āļ”āļąāļšāļ āļđāļĄāļīāļ āļēāļ„ WRF-CFSR āđ‚āļ”āļĒāđƒāļ™āļāļēāļĢāļĻāļķāļāļĐāļēāđ„āļ”āđ‰āđ€āļĨāļ·āļ­āļāđƒāļŠāđ‰āļ§āļīāļ˜āļĩāļāļēāļĢ Empirical Orthogonal Function (EOF) āđƒāļ™āļāļēāļĢāļ›āļĢāļąāļšāđāļāđ‰āļ„āļ§āļēāļĄāļ„āļĨāļēāļ”āđ€āļ„āļĨāļ·āđˆāļ­āļ™āļ›āļĢāļīāļĄāļēāļ“āļ™āđ‰āļģāļāļ™āđāļšāļšāļĢāļēāļĒāđ€āļ”āļ·āļ­āļ™ āđ‚āļ”āļĒāļĻāļķāļāļĐāļēāđƒāļ™āļžāļ·āđ‰āļ™āļ—āļĩāđˆāļ āļēāļ„āđ€āļŦāļ™āļ·āļ­āļ•āļ­āļ™āļšāļ™āļ‚āļ­āļ‡āļ›āļĢāļ°āđ€āļ—āļĻāđ„āļ—āļĒāļ—āļąāđ‰āļ‡āļŦāļĄāļ” 18 āļŠāļ–āļēāļ™āļĩ āļ„āļĢāļ­āļšāļ„āļĨāļļāļĄāļ•āļąāđ‰āļ‡āđāļ•āđˆāļ›āļĩ āļ„.āļĻ. 1980-2010 (31āļ›āļĩ) āđāļĨāļ°āđƒāļŠāđ‰āļ‚āđ‰āļ­āļĄāļđāļĨāļ•āļĢāļ§āļˆāļ§āļąāļ”āđāļšāļšāļāļĢāļīāļ” (APHRODITE CRU āđāļĨāļ°GPCP) āđƒāļ™āļāļēāļĢāđ€āļ›āļĢāļĩāļĒāļšāđ€āļ—āļĩāļĒāļšāļœāļĨāļĢāđˆāļ§āļĄāļāļąāļšāļ‚āđ‰āļ­āļĄāļđāļĨāđāļšāļšāļˆāļģāļĨāļ­āļ‡āļŠāļ āļēāļžāļ­āļēāļāļēāļĻāļĢāļ°āļ”āļąāļšāļ āļđāļĄāļīāļ āļēāļ„ WRF-CFSR āļˆāļēāļāļāļēāļĢāļĻāļķāļāļĐāļēāļžāļšāļ§āđˆāļēāļ§āļīāļ˜āļĩāļ›āļĢāļąāļšāđāļāđ‰ EOF āļŠāļēāļĄāļēāļĢāļ–āļĨāļ”āļ„āđˆāļēāļ„āļ§āļēāļĄāđāļ•āļāļ•āđˆāļēāļ‡āļĢāļ°āļŦāļ§āđˆāļēāļ‡āļ„āđˆāļēāļ›āļĢāļīāļĄāļēāļ“āļ™āđ‰āļģāļāļ™āļœāļīāļ”āļ›āļāļ•āļīāđāļĨāļ°āļ„āđˆāļēāļ›āļĢāļīāļĄāļēāļ“āļ™āđ‰āļģāļāļ™āļ›āļāļ•āļīāđ€āļ‰āļĨāļĩāđˆāļĒāđƒāļŦāđ‰āļĄāļĩāļ„āļ§āļēāļĄāđƒāļāļĨāđ‰āđ€āļ„āļĩāļĒāļ‡āļāļąāļšāļ„āđˆāļēāļ„āļ§āļēāļĄāđāļ•āļāļ•āđˆāļēāļ‡āļ‚āļ­āļ‡āļ‚āđ‰āļ­āļĄāļđāļĨāļ•āļĢāļ§āļˆāļ§āļąāļ” āđāļĨāļ°āđƒāļ™āļāļēāļĢāļ•āļĢāļ§āļˆāļŠāļ­āļšāļ„āļ§āļēāļĄāļ–āļđāļāļ•āđ‰āļ­āļ‡āļ”āđ‰āļ§āļĒāļ„āđˆāļēāļĢāļēāļāļ—āļĩāđˆāļŠāļ­āļ‡āļ‚āļ­āļ‡āļ„āđˆāļēāļ„āļ§āļēāļĄāļ„āļĨāļēāļ”āđ€āļ„āļĨāļ·āđˆāļ­āļ™āļāļģāļĨāļąāļ‡āļŠāļ­āļ‡āđ€āļ‰āļĨāļĩāđˆāļĒ (RMSE) āļžāļšāļ§āđˆāļēāļ§āļīāļ˜āļĩāļāļēāļĢāļ›āļĢāļąāļšāđāļāđ‰āļ„āļ§āļēāļĄāļ„āļĨāļēāļ”āđ€āļ„āļĨāļ·āđˆāļ­āļ™ EOF āļĒāļąāļ‡āđ„āļĄāđˆāļŠāļēāļĄāļēāļĢāļ–āļĨāļ”āļ„āđˆāļēāļ„āļ§āļēāļĄāļ„āļĨāļēāļ”āđ€āļ„āļĨāļ·āđˆāļ­āļ™āļ‚āļ­āļ‡āļ›āļĢāļīāļĄāļēāļ“āļ™āđ‰āļģāļāļ™āđ„āļ”āđ‰ āđāļ•āđˆāļ­āļĒāđˆāļēāļ‡āđ„āļĢāļāđ‡āļ•āļēāļĄāļˆāļēāļāļāļēāļĢāļ•āļĢāļ§āļˆāļŠāļ­āļšāļ„āļ§āļēāļĄāļ–āļđāļāļ•āđ‰āļ­āļ‡āļ”āđ‰āļ§āļĒāļ„āđˆāļēāļŠāļąāļĄāļ›āļĢāļ°āļŠāļīāļ—āļ˜āļīāđŒāļŠāļŦāļŠāļąāļĄāļžāļąāļ™āļ˜āđŒ (r) āļžāļšāļ§āđˆāļēāļ§āļīāļ˜āļĩ EOF āļŠāļēāļĄāļēāļĢāļ–āļĢāļąāļāļĐāļēāļ„āļ§āļēāļĄāļ•āđˆāļ­āđ€āļ™āļ·āđˆāļ­āļ‡āđ€āļŠāļīāļ‡āļžāļ·āđ‰āļ™āļ—āļĩāđˆāļ‚āļ­āļ‡āļ›āļĢāļīāļĄāļēāļ“āļ™āđ‰āļģāļāļ™āļĢāļēāļĒāđ€āļ”āļ·āļ­āļ™āđ„āļ”āđ‰ āđ‚āļ”āļĒāđ€āļ‰āļžāļēāļ°āļāļēāļĢāļ›āļĢāļąāļšāđāļāđ‰āļ‚āđ‰āļ­āļĄāļđāļĨāđāļšāļšāļˆāļģāļĨāļ­āļ‡āļŠāļ āļēāļžāļ­āļēāļāļēāļĻāļĢāļ°āļ”āļąāļšāļ āļđāļĄāļīāļ āļēāļ„ WRF-CFSR āđāļĨāļ°āļ‚āđ‰āļ­āļĄāļđāļĨāļ•āļĢāļ§āļˆāļ§āļąāļ”āļāļĢāļīāļ” GPCP āļĄāļĩāļ„āđˆāļē r āļ­āļĒāļđāđˆāđƒāļ™āļŠāđˆāļ§āļ‡ 0.52 āļ–āļķāļ‡ 0.97 āļ‹āļķāđˆāļ‡āđ€āļ›āđ‡āļ™āļ„āđˆāļēāļ„āļ§āļēāļĄāļŠāļąāļĄāļžāļąāļ™āļ˜āđŒāļŦāļĨāļąāļ‡āļ›āļĢāļąāļšāđāļāđ‰āļ—āļĩāđˆāļ”āļĩāļ—āļĩāđˆāļŠāļļāļ”Climate modeling system is a challenging and difficult task. Because uncertainty of the model processing is caused by many factors that influence the discrepancy of model output in both spatial and time. Therefore, in this study, the objective of this study was to apply methods or techniques for precipitation bias correction method from the WRF-CFSR regional climate model and to evaluate the efficiency of precipitation bias correction methods from the WRF-CFSR regional climate model. This study was selected the Empirical Orthogonal Function (EOF) for the monthly precipitation bias correction method in the upper northern region of Thailand, all 18 stations covering from 1980-2010 (31 years) and use observation grids data (APHRODITE CRU and GPCP) to compare the results with the WRF-CFSR regional climate model data. The result that the EOF correction method can reduce the difference between the precipitation anomaly and mean precipitation to be closer to the difference of the observation data. For validation with the Root Mean Square Error (RMSE) was found that the EOF bias correction method was unable to reduce the precipitation error. However, the validation with correlation coefficient values, the EOF method can maintain the spatial continuity of monthly precipitation. In particular, the correction of the WRF-CFSR regional climate model data and the GPCP grid observation data had r values 0.52 to 0.97 which is the best correction correlation

    Chemical Fertilization Alters Soil Carbon in Paddy Soil through the Interaction of Labile Organic Carbon and Phosphorus Fractions

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    The influence of long-term chemical fertilization in paddy soils is based on the interaction between labile carbon and phosphorus fractions and the manner in which this influences soil organic carbon (SOC). Four soil depths (0–30 cm) were analyzed in this study. Easily oxidized organic carbon components, such as permanganate oxidized carbon (POXC) and dissolved organic carbon (DOC), and other physicochemical soil factors were evaluated. The correlation and principal component analyses were used to examine the relationship between soil depth and the parameter dataset. The results showed that Fe-P concentrations were greater in the 0–5 cm soil layer. DOC, inorganic phosphate fraction, and other soil physiochemical characteristics interacted more strongly with SOC in the 0–5 cm soil layer, compared to interactions in the 10–15 cm layer, influencing soil acidity. An increase in DOC in the 0–5 cm soil layer had a considerable effect on lowering SOC, consistent with P being positively correlated with POXC, but negatively with SOC and water-soluble carbon (WSC). The changes in SOC could be attributed to the relationship between DOC and inorganic phosphate fractions (such as Fe-P) under specific soil pH conditions. An increase in soil DOC could be caused by changes in the P fraction and pH. The DOC:Avai. P ratio could serve as a compromise for the C and P dynamic indicators. The soil depth interval is a critical element that influences these interactions. Agricultural policy and decision-making may be influenced by the P from chemical fertilization practices, considering the yields and environmental effects

    Evolution of Urban Haze in Greater Bangkok and Association with Local Meteorological and Synoptic Characteristics during Two Recent Haze Episodes

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    This present work investigates several local and synoptic meteorological aspects associated with two wintertime haze episodes in Greater Bangkok using observational data, covering synoptic patterns evolution, day-to-day and diurnal variation, dynamic stability, temperature inversion, and back-trajectories. The episodes include an elevated haze event of 16 days (14&ndash;29 January 2015) for the first episode and 8 days (19&ndash;26 December 2017) for the second episode, together with some days before and after the haze event. Daily PM2.5 was found to be 50 &micro;g m&minus;3 or higher over most of the days during both haze events. These haze events commonly have cold surges as the background synoptic feature to initiate or trigger haze evolution. A cold surge reached the study area before the start of each haze event, causing temperature and relative humidity to drop abruptly initially but then gradually increased as the cold surge weakened or dissipated. Wind speed was relatively high when the cold surge was active. Global radiation was generally modulated by cloud cover, which turns relatively high during each haze event because cold surge induces less cloud. Daytime dynamic stability was generally unstable along the course of each haze event, except being stable at the ending of the second haze event due to a tropical depression. In each haze event, low-level temperature inversion existed, with multiple layers seen in the beginning, effectively suppressing atmospheric dilution. Large-scale subsidence inversion aloft was also persistently present. In both episodes, PM2.5 showed stronger diurnality during the time of elevated haze, as compared to the pre- and post-haze periods. During the first episode, an apparent contrast of PM2.5 diurnality was seen between the first and second parts of the haze event with relatively low afternoon PM2.5 over its first part, but relatively high afternoon PM2.5 over its second part, possibly due to the role of secondary aerosols. PM2.5/PM10 ratio was relatively lower in the first episode because of more impact of biomass burning, which was in general agreement with back-trajectories and active fire hotspots. The second haze event, with little biomass burning in the region, was likely to be caused mainly by local anthropogenic emissions. These findings suggest a need for haze-related policymaking with an integrated approach that accounts for all important emission sectors for both particulate and gaseous precursors of secondary aerosols. Given that cold surges induce an abrupt change in local meteorology, the time window to apply control measures for haze is limited, emphasizing the need for readiness in mitigation responses and early public warning
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