Optical characteristics of southeast Asia's regional aerosols and their sources

The dominant optical characteristics of Southeast Asia (SEA)'s regional aerosols were determined from the cluster analysis of the 26 AERONET aerosol inversion products, including aerosol light scattering/absorption indicators and aerosol size/shape parameters retrieved from 2003 to 2007. The data sets were acquired from four stations: Bac Giang in Vietnam and Mukdahan, Pimai, and Silpakorn University in Thailand. The cluster analysis showed agreement among the aerosol optical characteristics, land cover/uses, season as the surrogate of the prevailing winds, and observations from the literature. The results of this study showed that during the northeast prevailing winds from mid-September to December, the high aerosol exposure events were most frequently observed over the upwind station and less often over the downwind stations. This aerosol exhibited a single scattering albedo (SSA) of approximately 0.95 (440 nm), a relatively low refractive index, and a larger fine-mode size, suggesting it had the characteristics of urban/industrial aerosols reported in the literature. These aerosol sources were upwind from Bac Giang, probably in eastern China. From January to April, the aerosol exhibited a lower SSA of approximately 0.90, a higher refractive index, and a smaller fine-mode size, suggesting biomass burning smoke reported in the literature. The SEA urban aerosol exhibited a mean SSA of approximately 0.90 (440 nm) or lower, and the coarse-mode aerosol, possibly road dust or soil dust, played a role from October to January when seasonal winds are strongest. The results from a canonical discriminant function analysis suggest that the dominant SEA aerosol clusters tended to be separated by a canonical function positively correlated with SSA, the fine-mode asymmetry factor, and the overall fine-mode size and negatively correlated with the refractive index.     

Strategic directions for agent-based modeling: avoiding the YAAWN syndrome

In this short communication, we examine how agent-based modeling has become common in land change science and is increasingly used to develop case studies for particular times and places. There is a danger that the research community is missing a prime opportunity to learn broader lessons from the use of agent-based modeling (ABM), or at the very least not sharing these lessons more widely. How do we find an appropriate balance between empirically rich, realistic models and simpler theoretically grounded models? What are appropriate and effective approaches to model evaluation in light of uncertainties not only in model parameters but also in model structure? How can we best explore hybrid model structures that enable us to better understand the dynamics of the systems under study, recognizing that no single approach is best suited to this task? Under what circumstances – in terms of model complexity, model evaluation, and model structure – can ABMs be used most effectively to lead to new insight for stakeholders? We explore these questions in the hope of helping the growing community of land change scientists using models in their research to move from ‘yet another model’ to doing better science with models.     

Estimation of Southeast Asian rice paddy areas with different ecosystems from moderate-resolution satellite imagery

Multi-temporal satellite imagery from the Moderate Resolution Imaging Spectrometer (MODIS) was used to map the different ecosystems of Southeast Asian (SEA) rice paddies. The algorithm was based on temporal profiles of vegetation strength and/or water content, using MODIS surface reflectance in visible to near-IR range. The results obtained from the analysis were compared to national statistics. Estimated SEA regional rice area was 42 × 10⁶ ha, which agrees with published values. The model performance was dependent on rice ecosystems. Good linear relationships between the model results and the national statistics were observed for rainfed rice. High linear coefficients of determination, R², were also found for irrigated rice and upland rice, but the model tended to underestimate irrigated rice and overestimate upland rice. However, these high R² values indicated that the model effectively simulated spatial distribution of these rice areas. These R² values were either of similar magnitude or larger than those reported in literature, regardless of the rice ecosystem. Poor correlation was observed for deepwater rice.