The 1989 Comprehensive Plan of Action (CPA) and refugee policy in Southeast Asia : twenty years forward what has changed?

In 2012, the only South East Asian countries that have ratified the 1951 Convention relating to the Status of Refugees and the 1967 Protocol Relating to the Status of Refugees (hereafter referred to as the 1951 Convention and 1967 Protocol) is Philippines (signed 1954), Cambodia (signed 1995) and Timor Leste (signed 2001). Countries such as Indonesia, Malaysia and Thailand have annual asylum seeking populations from Myanmar, South Asia and Middle East, that are estimated to be at 15 000-20 000 per country (UNHCR 2012). The lack of a permanent and formal asylum processing process in these countries means that that asylum-seeking populations in the region are reliant on the local offices of the United Nations High Commission for Refugees based in the region to process their claims. These offices rely upon the good will of these governments to have a presence near detection camps and in capital cities to process claims of those who manage to reach the UNHCR representative office. The only burden sharing mechanism within the region primarily exists under the Bali Process on People Smuggling, Trafficking in Persons and Related Transnational Crime (the Bali Process), introduced in 2002. The Bali Process refers to an informal cooperative agreement amongst the states from the Asia-Pacific region, with Australia and Indonesia as the co-chairs, which discusses its namesake: primarily anti-people smuggling activities and migration protocols. There is no provision within this process to discuss the development of national asylum seeking legislation, processes for domestic processing of asylum claims or burden sharing in contrast to other regions such as Africa and South America (i.e. 2009 African Union Convention for the Protection and Assistance of the Internally Displaced, 1969 African Union Convention Governing the Specific Aspects of Refugee Problems in Africa and 1984 Cartagena Declaration on Refugees [Americas]) (PEF 2010: 19).

Weather variability, tides, and Barmah Forest Virus Disease in the Gladstone region, Australia

In this study we examined the impact of weather variability and tides on the transmission of Barmah Forest virus (BFV) disease and developed a weather-based forecasting model for BFV disease in the Gladstone region, Australia. We used seasonal autoregressive integrated moving-average (SARIMA) models to determine the contribution of weather variables to BFV transmission after the time-series data of response and explanatory variables were made stationary through seasonal differencing. We obtained data on the monthly counts of BFV cases, weather variables (e.g., mean minimum and maximum temperature, total rainfall, and mean relative humidity), high and low tides, and the population size in the Gladstone region between January 1992 and December 2001 from the Queensland Department of Health, Australian Bureau of Meteorology, Queensland Department of Transport, and Australian Bureau of Statistics, respectively. The SARIMA model shows that the 5-month moving average of minimum temperature (β = 0.15, p-value < 0.001) was statistically significantly and positively associated with BFV disease, whereas high tide in the current month (β = −1.03, p-value = 0.04) was statistically significantly and inversely associated with it. However, no significant association was found for other variables. These results may be applied to forecast the occurrence of BFV disease and to use public health resources in BFV control and prevention.

Cross-correlations between weather variables in Australia

The information on climate variations is essential for the research of many subjects, such as the performance of buildings and agricultural production. However, recorded meteorological data are often incomplete. There may be a limited number of locations recorded, while the number of recorded climatic variables and the time intervals can also be inadequate. Therefore, the hourly data of key weather parameters as required by many building simulation programmes are typically not readily available. To overcome this gap in measured information, several empirical methods and weather data generators have been developed. They generally employ statistical analysis techniques to model the variations of individual climatic variables, while the possible interactions between different weather parameters are largely ignored. Based on a statistical analysis of 10 years historical hourly climatic data over all capital cities in Australia, this paper reports on the finding of strong correlations between several specific weather variables. It is found that there are strong linear correlations between the hourly variations of global solar irradiation (GSI) and dry bulb temperature (DBT), and between the hourly variations of DBT and relative humidity (RH). With an increase in GSI, DBT would generally increase, while the RH tends to decrease. However, no such a clear correlation can be found between the DBT and atmospheric pressure (P), and between the DBT and wind speed. These findings will be useful for the research and practice in building performance simulation.