A Censored Bayesian Hierarchical Model For Precipitation

Modelling of precipitation, including extremes, is important for hydrological and agricultural applications. Traditionally, because of large sample properties for data over a large threshold value, generalised Pareto (GP) distributions are often used for modelling extreme rainfall. It can be shown that under certain conditions the generalised hyperbolic (GH) distributions can approximate the power law decay of the GP distribution in the tails. Given their flexible form, this raises the possibility that distributions from the GH family serve as a model for the entire rainfall distribution thus avoiding the need to select a threshold. In this paper, we use a flexible censored hierarchical model that leverages the GH distribution to accommodate data subject to heavy tails and an excessive number of zeros. The fitted model allows estimation of probabilities and return periods of the rainfall extremes, and it produces narrower credible intervals in the tails than the traditional GP method. The model not only fits the tails of the rainfall distribution, but fits the whole distribution very well. It also efficiently represents short-term dependencies in the data so it is suitable for evaluating duration over and below thresholds as well as duration of zero rainfall.Comment: Under review at Environmentric

Spatio-temporal quantitative links between climatic extremes and population flows: a case study in the Murray-Darling Basin, Australia

A growing body of research shows that extreme climatic events, e.g. heatwave, rainstorms and droughts, are becoming more frequent and intensified across various regions of the world. Australia is not isolated from these changes with marked increase in both rainfall and temperature extremes. Inherently, we understand that exposure to these extreme events could encourage decisions about population flow, and quantifying this linkage is challenging, especially for communities in small areas with an average of 10,000 people. Using spatio-temporal statistical techniques, this paper examines the possible environmental and socio-economic drivers associated with population flows of small communities as well as the possible predictive scenarios due to the effects introduced by climatic extremes. The analysis has been undertaken for a case-study region in the Murray-Darling Basin, Australia, where the economy is underpinned by agriculture and is sensitive to climate variability and extremes. The analysis reveals that in addition to the socio-economic factors, the environmental variables have a statistically significant association on shaping the distribution of the population flows in the study area. This statistical analysis can direct further data collection and causality analysis and be beneficial for policy makers, stakeholders and local communities to work together to adapt the Basin to climate extremes and changes.The work was partially funded by the CSR&M, ANU, CSIRO DigiScape future science platform, and the Digital Agriculture initiative

Potential future scenarios for Australia's native biodiversity given on-going increases in human population

Most natural assets, including native biodiversity (our focus), are under increasing threat from direct (loss of habitat, hunting) and indirect (climate change) human actions. Most human impacts arise from increasing human populations coupled with rises in per capita resource use. The rates of change of human actions generally outpace those to which the biota can respond or adapt. If we are to maintain native biodiversity, then we must develop ways to envisage how the biota may be affected over the next several decades to guide management and policy responses. We consider the future for Australia's native biodiversity in the context of two assumptions. First, the human population in Australia will be 40million by 2050, which has been mooted by federal government agencies. Second, greenhouse gas emissions will track the highest rates considered by the Intergovernmental Panel on Climate Change. The scenarios are based on major drivers of change, which were constructed from seven key drivers of change pertinent to native biodiversity. Five scenarios deal with differing distributions of the human population driven by uncertainties in climate change and in the human responses to climate change. Other scenarios are governed largely by global change and explore different rates of resource use, unprecedented rates of technological change, capabilities and societal values. A narrative for each scenario is provided. The set of scenarios spans a wide range of possible future paths for Australia, with different implications for the future of native biodiversity.The work was supported by an Australian Research Council Discovery Grant (DP120100797) and by funds from the Institute for Applied Ecology, The University of Canberr

Bayesian spatio–temporal modeling of urban air pollution dynamics

This work deals with the spatio-temporal analysis of urban air pollution dynamics from the town of Perugia, Italy, using high-frequency and size resolved data on particular matter. Hierarchical Bayesian models are used that allow for an autoregressive term in time. Some preliminary results show that there is a significant spatio–temporal structure with a large first–order temporal correlation coefficient. Future analysis will concern the use of higher–order temporal auto–correlation structures and the introduction of the effect of some covariates