Rethinking disaster risk management and climate change adaptation

AbstractAustralian governments face the twin challenges of dealing with extreme weather-related disasters (such as floods and bushfires) and adapting to the impacts of climate change. These challenges are connected, so any response would benefit from a more integrated approach across and between the different levels of government.This report summarises the findings of an NCCARF-funded project that addresses this problem.The project undertook a three-way comparative case study of the 2009 Victorian bushfires, the 2011 Perth Hills bushfires, and the 2011 Brisbane floods. It collected data from the official inquiry reports into each of these events, and conducted new interviews and workshops with key stakeholders. The findings of this project included recommendations that range from the conceptual to the practical. First, it was argued that a reconceptualization of terms such as ‘community’ and ‘resilience’ was necessary to allow for more tailored responses to varying circumstances. Second, it was suggested that the high level of uncertainty inherent in disaster risk management and climate change adaptation requires a more iterative approach to policymaking and planning. Third, some specific institutional reforms were proposed that included: 1) a new funding mechanism that would encourage collaboration between and across different levels of government, as well as promoting partnerships with business and the community; 2) improving community engagement through new resilience grants run by local councils; 3) embedding climate change researchers within disaster risk management agencies to promote institutional learning; and, 4) creating an inter-agency network that encourages collaboration between organisations.Please cite this report as: Howes, M, Grant-Smith, D, Reis, K, Bosomworth, K, Tangney, P, Heazle, M, McEvoy, D, Burton, P 2013 Rethinking disaster risk management and climate change adaptation, National Climate Change Adaptation Research Facility, Gold Coast, pp. 63.Australian governments face the twin challenges of dealing with extreme weather-related disasters (such as floods and bushfires) and adapting to the impacts of climate change. These challenges are connected, so any response would benefit from a more integrated approach across and between the different levels of government.This report summarises the findings of an NCCARF-funded project that addresses this problem.The project undertook a three-way comparative case study of the 2009 Victorian bushfires, the 2011 Perth Hills bushfires, and the 2011 Brisbane floods. It collected data from the official inquiry reports into each of these events, and conducted new interviews and workshops with key stakeholders. The findings of this project included recommendations that range from the conceptual to the practical. First, it was argued that a reconceptualization of terms such as ‘community’ and ‘resilience’ was necessary to allow for more tailored responses to varying circumstances. Second, it was suggested that the high level of uncertainty inherent in disaster risk management and climate change adaptation requires a more iterative approach to policymaking and planning. Third, some specific institutional reforms were proposed that included: 1) a new funding mechanism that would encourage collaboration between and across different levels of government, as well as promoting partnerships with business and the community; 2) improving community engagement through new resilience grants run by local councils; 3) embedding climate change researchers within disaster risk management agencies to promote institutional learning; and, 4) creating an inter-agency network that encourages collaboration between organisations.&nbsp

Causally Regularized Learning with Agnostic Data Selection Bias

Most of previous machine learning algorithms are proposed based on the i.i.d. hypothesis. However, this ideal assumption is often violated in real applications, where selection bias may arise between training and testing process. Moreover, in many scenarios, the testing data is not even available during the training process, which makes the traditional methods like transfer learning infeasible due to their need on prior of test distribution. Therefore, how to address the agnostic selection bias for robust model learning is of paramount importance for both academic research and real applications. In this paper, under the assumption that causal relationships among variables are robust across domains, we incorporate causal technique into predictive modeling and propose a novel Causally Regularized Logistic Regression (CRLR) algorithm by jointly optimize global confounder balancing and weighted logistic regression. Global confounder balancing helps to identify causal features, whose causal effect on outcome are stable across domains, then performing logistic regression on those causal features constructs a robust predictive model against the agnostic bias. To validate the effectiveness of our CRLR algorithm, we conduct comprehensive experiments on both synthetic and real world datasets. Experimental results clearly demonstrate that our CRLR algorithm outperforms the state-of-the-art methods, and the interpretability of our method can be fully depicted by the feature visualization.Comment: Oral paper of 2018 ACM Multimedia Conference (MM'18

Good to the Last Drop: The Emergence of Coffee Ringspot Virus

Two and a half billion times per day a human hand reaches for a fresh cup of coffee. Although arguably dispensable for life per se, with an industry value of US$174 billion, coffee provides the lifeblood that sustains economies of producing countries located in the “coffee belt” situated between the Tropics of Cancer and Capricorn. As a “solvent” in which many human interactions take place, coffee is witness to the broad spectrum of human activities from the mundane to the pleasurable and personal. However, in opposition to its economic, cultural, and physiological importance, diseases such as coffee rust (caused by the fungus Hemileia vastatrix) dictate activity on stock markets with their periodic epidemics, which in turn affects the migration patterns of displaced farm workers [1]. Other diseases, such as those caused by coffee ringspot virus (CoRSV), currently fly mostly under the radar of many integrated pest management systems. The unique biology of this and related viruses offers exciting research opportunities ranging from cell biology, plant pathology and physiology, conservation ecology, to climate change-related epidemiology. This review highlights important aspects of CoRSV, including its unique features, and examines the potential role of climate change in its emergence (Fig 1)

Twisting instabilities in elastic ribbons with inhomogeneous pre-stress: a macroscopic analog of thermodynamic phase transition

We study elastic ribbons subject to large, tensile pre-stress confined to a central region within the cross-section. These ribbons can buckle spontaneously to form helical shapes, featuring regions of alternating chirality (phases) that are separated by so-called perversions (phase boundaries). This instability cannot be described by classical rod theory, which incorporates pre-stress through effective natural curvature and twist; these are both zero due to the mirror symmetry of the pre-stress. Using dimension reduction, we derive a one-dimensional (1D) 'rod-like' model from a plate theory, which accounts for inhomogeneous pre-stress as well as finite rotations. The 1D model successfully captures the qualitative features of torsional buckling under a prescribed end-to-end displacement and rotation, including the co-existence of buckled phases possessing opposite twist, and is in good quantitative agreement with the results of numerical (finite-element) simulations and model experiments on elastomeric samples. Our model system provides a macroscopic analog of phase separation and pressure-volume-temperature state diagrams, as described by the classical thermodynamic theory of phase transitions.Comment: 29 pages; 11 figure