APPLICATION OF THE ACASA MODEL AT A DRY DIPTEROCARP FOREST IN THAILAND

Abstract

Climate change is associated with increasingly frequent disasters such as floods and droughts, and/or changes in the timing and duration of seasons. Forest ecosystems play an important role in mitigating greenhouse gas emissions to the atmosphere with their large storage of carbon (carbon sequestration). Understanding the energy balance, heat flux, and net ecosystem exchange in forests is important for developing approaches to cope with the effects of climate change via management, mitigation, and adaptation. This study utilized the Advanced Canopy–Atmosphere–Soil Algorithm (ACASA), a land surface model (LSM) which is used here to examine a dry dipterocarp tropical forest in Thailand’s Phayao province. Half-hourly averaged data from 2015 were used to calibrate the model, while data from 2014 and 2016 were used to validate the model. The results showed that the ACASA model can simulate net radiation, incoming and outgoing shortwave radiation, and outgoing longwave radiation parameters very accurately, with R² values greater than 0.9 and root mean square errors of  4.49–38.02 W/m². In addition, the model can achieve reasonable estimates of sensible heat flux and latent heat flux, with R² values of 0.57–0.68. The results from this LSM have potential implications for developing and validating climate models as well as analyzing forest sensitivity and adaptation under a changing climate.