Drought risk analysis for forested landscapes: Project PRAFOR

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Description:	Brewer, M Tuesday 8th September 2020 - 13:30 to 13:50


Created:	2020-09-08 21:14
Collection:	Mathematical and statistical challenges in landscape decision making
Publisher:	Isaac Newton Institute
Copyright:	Brewer, M
Language:	eng (English)


Abstract:	This project 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 and not for systems where the hazard variable is always present and continuously varying, with matching continuous system response. 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, 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. We have used this approach to analyse risks from summer droughts to the productivity of vegetation across Europe under current and future climatic conditions; 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 earlier work by: adding exposure to hazard; quantifying uncertainties in our risk estimates for risk; relaxing assumptions via Bayesian hierarchical modelling; testing our approach on both observational data from forests in the U.K., Spain and Finland and on simulated data from process-based modelling of forest response to climate change; embedding the approach in Bayesian decision theory; and developing an interactive web application as a tool for preliminary exploration of risk and its components to support decision-making.

Abstract:

This project 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 and not for systems where the hazard variable is always present and continuously varying, with matching continuous system response. 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, 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. We have used this approach to analyse risks from summer droughts to the productivity of vegetation across Europe under current and future climatic conditions; 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 earlier work by: adding exposure to hazard; quantifying uncertainties in our risk estimates for risk; relaxing assumptions via Bayesian hierarchical modelling; testing our approach on both observational data from forests in the U.K., Spain and Finland and on simulated data from process-based modelling of forest response to climate change; embedding the approach in Bayesian decision theory; and developing an interactive web application as a tool for preliminary exploration of risk and its components to support decision-making.

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