No abstract

Deforestation, followed by abandonment and forest regeneration, has become one of the dominant types of land cover changes in the tropics. This study applied the eddy covariance (EC) technique to quantify the energy budget and evapotranspiration in a regenerated secondary dry dipterocarp forest in Western Thailand. The mean annual net radiation was 126.69, 129.61, and 125.65 W m −2 day −1 in 2009, 2010, and 2011, respectively. On average, fluxes of this energy were disaggregated into latent heat (61%), sensible heat (27%), and soil heat flux (1%). While the number of energy exchanges was not significantly different between these years, there were distinct seasonal patterns within a year. In the wet season, more than 79% of energy fluxes were in the form of latent heat, while during the dry season, this was in the form of sensible heat. The energy closure in this forest ecosystem was 86% and 85% in 2010 and 2011, respectively, and varied between 84-87% in the dry season and 83-84% in the wet season. The seasonality of these energy fluxes and energy closure can be explained by rainfall, soil moisture, and water vapor deficit. The rates of evapotranspiration also significantly varied between the wet (average 6.40 mm day −1 ) and dry seasons (3.26 mm day −1 ).

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Soil respiration as a major component of the carbon cycle has received considerable attention because of its role in amplifying global warming and in climate feedbacks of ecosystems. This makes it important for us to devise reliable methods in order to measure soil CO 2 effluxes accurately. In this study, we investigated the variations of CO 2 effluxes for 93 days in sweet sorghum plots and a dry dipterocarp forest by closed chamber and soil gradient methods. The results show that both sites had similar patterns of soil CO 2 emission but CO 2 emission from the sweet sorghum plots was 4 times higher than from the dry dipterocarp forest. Over the study period, the average soil CO 2 efflux and accumulative emission from the dry dipterocarp forest were 360 ± 129 mg CO 2 m −2 h −1 and 34 g CO 2 m −2 and from the sweet sorghum plots they were 2456 ± 614 mg CO 2 m −2 h −1 and 235 g CO 2 m −2 , respectively. Continuous and high temporal-resolution measurements based on the soil gradient method also enabled us to detect the response of soil CO 2 efflux to environmental drivers. We found that rainfall and irrigation events in a short time period could significantly enhance the magnitude of soil CO 2 effluxes. In addition, we also found that an appropriate time for daily soil CO 2 measurements was around noon.

Soil respiration (Rs) plays a key role in regulating the terrestrial carbon cycle. The nature of this role is determined by the different responses of root respiration (Rr) and microbial respiration (Rm) to environmental factors such as precipitation, soil moisture and temperature. Understanding these responses is fundamental to improving our predictions of climate change impacts on carbon cycling processes. In this study, the ratio of root respiration to soil respiration (Rr/Rs) was studied to improve our understanding of soil CO2emissions. The study aimed to improve our knowledge of Rr in relation to rainy season soil environmental factors in a dry dipterocarp forest in northern Thailand. With values of Rrranging from 41.04-61.97 mgCO2m-2h-1, with a Rr/Rsratio from 23-48%,the results suggest that soil moisture was a main driver for emitted CO2from Rr while soil temperature was only weakly related with Rr during the rainy season. However, longer-term studies are needed, including measurements of root biomass to improve accuracy and understanding of the dynamics of root respiration and their linkages with CO2emissions.

No abstract

Climate change and global warming have serious adverse impacts on tropical forests. In particular, climate change may induce changes in leaf phenology. However, in tropical dry forests where tree diversity is high, species responses to climate change differ. The objective of this research is to analyze the impact of climate variability on the leaf phenology in Thailand’s tropical forests. Machine learning approaches were applied to model how leaf phenology in dry dipterocarp forest in Thailand responds to climate variability and El Niño. First, we used a Self-Organizing Map (SOM) to cluster mature leaf phenology at the species level. Then, leaf phenology patterns in each group along with litterfall phenology and climate data were analyzed according to their response time. After that, a Long Short-Term Memory neural network (LSTM) was used to create model to predict leaf phenology in dry dipterocarp forest. The SOM-based clustering was able to classify 92.24% of the individual trees. The result of mapping the clustering data with lag time analysis revealed that each cluster has a different lag time depending on the timing and amount of rainfall. Incorporating the time lags improved the performance of the litterfall prediction model, reducing the average root mean square percent error (RMSPE) from 14.35% to 12.06%. This study should help researchers understand how each species responds to climate change. The litterfall prediction model will be useful for managing dry dipterocarp forest especially with regards to forest fires.