PRAFOR: Probabilistic drought Risk Analysis for FORested landscapes

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This research aims to extend theory for probabilistic risk analysis of continuous systems, test its use against forest data, use process models to predict future risks, and develop decision-support tools.Risk is commonly defined as the expectation value for loss. Most risk theory is developed for discrete hazards such as accidents, disasters and other forms of sudden system failure. Less theory has been developed for systems where the hazard variable is always present and continuously varying, with matching continuous system response. We can think of dynamic systems whose performance varies with ever-changing resource availability or other dynamic constraints, e.g. crop growth depending on water supply, or urban health as a function of air pollutant concentration. Risks from such continuous hazards (levels of water, pollutants) are not associated with sudden discrete events, but with extended periods of time during which the hazard variable exceeds a threshold. To manage such risks, we need to know whether we should aim to reduce the probability of hazard threshold exceedance or the vulnerability of the system. In earlier work (Van Oijen et al. 2013, http://iopscience.iop.org/1748-9326/8/1/015032), we showed that there is only one possible definition of vulnerability that allows formal decomposition of risk as the product of hazard probability and system vulnerability (R = p[H] V). We have used this approach to analyse risks from summer droughts to the productivity of vegetation across Europe under current and future climatic conditions (Van Oijen et al. 2014, http://www.biogeosciences.net/11/6357/2014/bg-11-6357- 2014.html). This showed that climate change will likely lead to greatest drought risks in southern Europe, primarily because of increased hazard probability rather than significant changes in vulnerability. We plan to improve on this preliminary theoretical work in different ways:- Add one more major risk component to the analysis: exposure to the hazard, so that risk becomes the product of three terms. That will allow distinguishing between hazards that only affect few individuals or points in space to those that affect larger populations and areas.- Derive equations for quantifying the uncertainties in our estimates for risk and its components. Only with quantified uncertainties can the estimates play a legitimate role in decision-support.- Relax assumptions underlying previous work and develop the theory for any type of joint probability distribution for hazard, exposure and vulnerability. This will likely require the use of extreme value theory and numerical estimation using Bayesian hierarchical modelling.- Test our equations and numerical algorithms on both observed and simulated data in this research. Observational data will be from forests in the U.K., Spain and Finland. Simulated data will be generated by process-based modelling of forest response to climate change.- Analyse the underlying causes of vulnerability, as represented by the parameters and processes of the process-based forest model.- Show the wider implications of the risk decomposition and the uncertainty quantification, by embedding the equations in Bayesian decision theory to allow identification of optimal drought management measures.- Develop an interactive web application as a tool for preliminary exploration of risk and its components to support decision-making.The work will be carried out by CEH-Edinburgh in close collaboration with Biomathematics and Statistics Scotland (BioSS, part of the James Hutton Institute, Aberdeen) and Forest Research UK (Alice Holt, Aberdeen, Edinburgh). Data and expertise from Spain and Finland will be provided by two Project Partners: the University of Alcalá (Madrid, Spain) and the Natural Resources Institute (Luke-Helsinki, Finland).