Predicting drought occurrence accurately still remains a challenging task. To fill research gaps, this study identified and analysed meteorological and hydrological droughts using the Standardized Precipitation Index (SPI) and Streamflow Drought Index (SDI), respectively, in the upper Lam Pao watershed in Thailand. The study also focused on investigating the relationships between both droughts. The SPI and SDI were computed based on observed long-term precipitation and streamflow data during the period of 1988-2017. The drought analysis was carried out by using the R packages. The location, period and severity level of drought events were graphically presented. On the basis of trend analysis, the SPI series showed slightly increasing trends, whereas no trend was found for the SDI series. This implied that the hydrological drought was influenced by not only precipitation but also other factors. The key findings indicated that there was a positive relationship between meteorological and hydrological droughts. In addition, there was a specific lag time, which may depend on physical characteristics of a basin, in drought propagating from meteorological drought to hydrological drought. Overall, the drought indices can help to predict hydrological drought events, which could be valuable information for drought monitoring and early warning systems.
Climate is a major determinant of global vegetation patterns and has a significant influence on the distribution and structure of forest ecosystems. Dong PraYa Yen-KhaoYai Forest Complex has been a UNESCO natural world heritage site since 2007, but little is known about its plant community. Our study aims to identify each plant community within the world heritage area and calculate its potential for carbon content. We determine both the relationship between forest type and both physio-chemical soil properties and climate change impact. We employed allometric equations to calculate aboveground biomass and both cluster analysis and canonical correspondence analysis (CCA) to examine the relationship between forest type and physiochemical soil properties. An equation for each physical parameter was used to predict the forest model. The climate scenario under A2 and B2 was applied to calculate future predominant forest types. Our results reveal that the forest ecosystems at Tab Lan (TL) have the highest species count (332 species) followed by Pang Srida (PD), KhaoYai (KY), Dong Yai (DY), and Tapraya (TY), with 293, 271, 169, and 99 species, respectively. We found KY to have the highest recorded carbon storage value at 2507.6 tC/ha followed by TL, PD, TY, and DY (1613.8, 1269.1, 844 and 810.7 tC/ha, respectively). Cluster analysis results indicated that the dominant species in each forest type is different. Moreover, CCA revealed that soil organic matter (SOM) and soil acid-base indicators are the best parameters to establish correlation for each forest type. Based on our results, future climate predictions show a negative impact on evergreen forests, but a positive one on deciduous ones.
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