PRAFOR: Probabilistic drought Risk Analysis for FORested landscapes

本研究旨在扩展连续系统概率风险分析的理论，测试其对森林数据的使用，使用过程模型预测未来风险，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诸如事故、灾难和其他形式的突然系统故障等离散危险而发展起来的。对于具有匹配的连续系统响应的危险变量总是存在且连续变化的系统，已经开发了较少的理论。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。这种连续危害（水、污染物水平）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较长时间有关。为了管理这些风险，我们需要知道我们的目标是降低超出危险阈值的概率还是降低系统的脆弱性。在早期的工作中（Van Oijen et al. 2013, http://iopscience.iop.org/1748-9326/8/1/015032），我们表明只有一种可能的脆弱性定义允许将风险正式分解为危害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当前和未来气候条件下欧洲夏季干旱对植被生产力的影响（Van 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，而不是脆弱性的显著变化。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：暴露于危害，使风险成为三个术语的产物。这将有助于区分只影响少数个人或空间点的危害与影响更多人口和地区的危害。-推导公式，以量化我们对风险及其组成部分的估计中的不确定性。只有量化了不确定性，评估才能在决策支持中发挥合理的作用。-放宽先前工作的假设，并发展任何类型的危害、暴露和脆弱性联合概率分布的理论。这可能需要使用极值理论和使用贝叶斯分层建模的数值估计。-在本研究的观察和模拟数据上测试我们的方程和数值算法。观测数据将来自英国、西班牙和芬兰的森林。模拟数据将通过基于过程的森林对气候变化的反应模型生成。-分析易受伤害的根本原因，如以过程为基础的森林模型的参数和过程所表示的。-通过在贝叶斯决策理论中嵌入方程，以确定最佳干旱管理措施，显示风险分解和不确定性量化的更广泛影响。-开发一个互动的网页应用程序，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-Edinburgh与苏格兰生物数学与统计中心（BioSS，詹姆斯·赫顿研究所的一部分，阿伯丁）和英国森林研究中心（Alice Holt，阿伯丁，爱丁堡）密切合作进行。来自西班牙和芬兰的数据和专门知识将由两个项目伙伴提供：阿尔卡尔<e:1>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).