http://dx.doi.org/10.14195/978-989-26-0884-6_85 Chapter 3 - Fire Management Advances in Forest Fire Research – Page 766 Gaining benefits from adversity: the need for systems and frameworks to maximise the data obtained from wildfires Thomas J. Duff a , Derek M. Chong a , Brett A. Cirulis a , Sean F. Walsh a , Trent D. Penman b , Kevin G. Tolhust b a University of Melbourne, Burnley, Victoria Australia 3121, tjduff@unimelb.edu.au, derekmoc@unimelb.edu.au, sean.walsh@unimelb.edu.au b University of Melbourne, Creswick, Victoria Australia 3363, trent.penman@unimelb.edu.au, kgt@unimelb.edu.au Abstract The organisations that manage wildfires are expected to deliver scientifically defensible decisions. However, the limited availability of high quality data restricts the rate at which research can advance. The nature of wildfires contributes to this; they are infrequent, complex events and occur rapidly. While some information about wildfires is usually collected, it is often not of an appropriate standard for research. Valuable information may be discarded or not collected as it is not seen as operationally useful. The harmonisation of fire data management worldwide could increase the availability and quality of information for research. We propose a three tiered approach where agreements are created to standardise data quality, define the scope of information to be collected and establish access protocols for sharing. Standardisation of data collection would facilitate the aggregation of data throughout the world, providing leverage on data collected and reduce unnecessary duplication. If the scope of collection can be expanded, there are a wide range of research fields that stand to benefit. Appropriate data sharing between agencies would increase the value of the data and enable robust conclusions to be reached. It is imperative that the losses caused by severe fires are not in vain; losses should be offset by efforts to maximise the information obtained, helping to prevent a repeat of such events in the future. Keywords: bushfire, data, enquiry, experimental design, framework, investigation, observations Introduction In the last decade there has been a high number of catastrophic wildfires throughout the world. In Australia alone, fires burning under extreme weather conditions have resulted in mass house loss in the state Victoria in 2009 (Cruz et al. 2012), Western Australia in 2011 and New South Wales and Tasmania in 2013 (Kepert et al. 2013). These fires have resulted in substantial social, economic and environmental impacts and recovery activities may take many years. In response to the increasing threat of fire, land managers in vulnerable jurisdictions have made substantial investments in mitigation, preparedness, response and recovery capabilities (McLoughlin 1985; Gebert and Black 2012). Due to the complexity of managing fires, it is not always clear how best to make investments to reduce fire impacts. As a consequence, there has recently been a rapid development of tools to aid decision support for landscape managers in fire prone environments. These include the development of systems that can evaluate complex information to optimise investment levels (Prestemon et al. 2008), resource placement (Chow and Regan 2011) or provide information on fire behaviour to aid suppression planning (Sullivan 2009). The development of systems that can support decisions for complex landscape scale events requires research that can differentiate the contributions of numerous interacting factors to an outcome. The analysis of such ‘noisy’ events requires large amounts of data in order for models to have adequate predictive power. However, the nature of wildfires means that obtaining such data is challenging.