MEDiate - Multi-hazard and Risk-informed System for Enhanced Local and Regional Disaster Risk Management

本项目将开发一个灾害风险管理决策支持系统，考虑到多种相互作用的自然灾害和级联影响，采用一种新的知情和面向服务的方法，考虑到灾害的预测变化（例如，气候变化）、脆弱性/复原力（例如，老化结构和人口）和暴露（例如，人口减少/增加）。MEDiate的主要交付成果将是一个决策支持框架，其形式为面向服务的网络工具和附带的灾害风险管理框架，使最终用户（地方当局、企业等）能够建立准确的情景，以模拟其缓解和适应风险管理行动的潜在影响。情景，可以定制，以反映当地的条件和需求（例如，（人口、贫困、自然资源等），将以历史记录和未来气候变化预测相结合为基础，预测与气候有关的灾害事件的地点和强度，并预测其对当地自然、经济和社会系统脆弱性的影响，包括连带影响。这些情景将使最终用户能够评估不同风险管理战略对减少脆弱性和增强社区复原力的潜在影响。该项目将包括分析相关数据，并与决策者共同开发决策支持系统的试验平台，以便能够进行更可靠的复原力评估，考虑到风险缓解和适应能力，从而减少未来气候相关和地球物理灾害造成的损失（人力、财政、环境等）。该项目将涉及一个由地球物理和气象科学家、风险工程师、社会科学家、信息技术专家和最终用户组成的多学科小组，共同努力确保该系统由用户主导，并得到适当技术的支持

This project will develop a decision-support system (DSS) for disaster risk management by considering multiple interacting natural hazards and cascading impacts using a novel resilient-informed and service-oriented approach that accounts for forecasted modifications in the hazard (e.g., climate change), vulnerability/resilience (e.g., aging structures and populations) and exposure (e.g., population decrease/increase). The primary deliverable from MEDiate will be a decision support framework in the form of service-orientated web tool and accompanying disaster risk management framework providing end users (local authorities, businesses etc) with the ability to build accurate scenarios to model the potential impact of their mitigation and adaptation risk management actions. The scenarios, which can be customised to reflect local conditions and needs (e.g., demographics, deprivation, natural resources etc), will be based on a combination of the historical record and future climate change projections to forecast the location and intensity of climate related disaster events and to predict their impacts, including cascading impacts, on the vulnerability of the local physical, economic and social systems. The scenarios will allow end users to evaluate the potential impact of different risk management strategies to reduce vulnerability and enhance community resilience. The project will consist of analysis of relevant data and co-development with testbed decision-makers of a DSS to enable more reliable resilience assessments, accounting for risk mitigation and adaptive capabilities, to be made, therefore reducing losses (human, financial, environmental etc) from future climate-related and geophysical disasters. The project will involve a multi-disciplinary team of geophysical and meteorological scientists, risk engineers, social scientists, information technologists and end users, working together to ensure that the system is user-led and supported by appropriate technology