PRAFOR: Probabilistic drought Risk Analysis for FORested landscapes

本研究旨在扩展连续系统的概率风险分析理论，测试其对森林数据的使用，使用过程模型来预测未来的风险，并开发决策支持工具。风险通常被定义为损失的期望值。大多数风险理论都是针对离散的危险，如事故、灾难和其他形式的突然系统故障而开发的。较少的理论已经开发的系统中的危险变量总是存在和连续变化，与匹配的连续系统响应。我们可以考虑动态系统，其性能随不断变化的资源可用性或其他动态约束而变化，例如作物生长取决于供水，或城市健康取决于空气污染物浓度。这种连续性危害（水的水平、污染物）的风险与突然的离散事件无关，而是与危害变量超过阈值的长时间段有关。为了管理这种风险，我们需要知道我们是否应该以降低危险阈值的概率或系统的脆弱性为目标。在早期的工作中（货车Oijen et al. 2013，http：//iopscience.iop.org/1748-9326/8/1/015032），我们证明了只有一种可能的脆弱性定义，允许将风险正式分解为危险概率和系统脆弱性的乘积（R = p[H] V）。我们使用这种方法分析了在当前和未来气候条件下夏季干旱对整个欧洲植被生产力的风险（货车Oijen et al. 2014，http://www.biogeosciences.net/11/6357/2014/bg-11-6357- 2014.html）。这表明，气候变化可能会导致南欧面临最大的干旱风险，主要是因为灾害概率增加，而不是脆弱性发生重大变化。我们计划以不同的方式改进这一初步的理论工作：-在分析中增加一个主要的风险成分：暴露于危险，使风险成为三项的乘积。这将允许区分只影响少数个人或空间点的危害和影响较大人口和地区的危害。推导出用于量化我们对风险及其组成部分的估计中的不确定性的方程。只有量化的不确定性，估计才能在决策支持中发挥合法的作用。放松以前工作的假设，并发展任何类型的联合概率分布的危险，暴露和脆弱性的理论。这可能需要使用极值理论和贝叶斯分层模型的数值估计。在本研究中，我们的方程和数值算法在观测数据和模拟数据上进行了测试。观测数据将来自英国的森林，西班牙和芬兰。模拟数据将通过基于过程的森林对气候变化的响应建模产生。分析脆弱性的根本原因，以基于过程的森林模型的参数和过程为代表。通过在贝叶斯决策理论中嵌入方程来识别最佳干旱管理措施，展示风险分解和不确定性量化的更广泛含义。开发一个交互式网络应用程序，作为初步探索风险及其组成部分的工具，以支持决策，这项工作将由CEH爱丁堡与苏格兰生物数学和统计局（BioSS，阿伯丁詹姆斯赫顿研究所的一部分）和英国森林研究所（爱丁堡阿伯丁爱丽丝霍尔特）密切合作进行。西班牙和芬兰的数据和专门知识将由两个项目伙伴提供：阿尔卡拉大学（西班牙马德里）和自然资源研究所（芬兰卢克-赫尔辛基）。

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).