EAGER: Wildfire Modeling and Prevention Initiative: Developing a Technical Framework for Integrating Research with Public Policy Decision Support

在美国西南部，野火的绝对数量和严重程度都在增加，预计这种趋势将在未来几十年内持续下去。 因此，需要描述性和预测性的模拟工具，以支持野火预防，或抑制在未来的野火事件，变得至关重要。作为第一步，该项目旨在开发一个技术框架，用于集成三维景观模型，实时环境数据，模拟代码套件和野火管理协议。 这项研究将涉及确定如何最好地合并高程和地面分类数据集，耦合火灾传播，大气和水文模拟代码，并验证耦合计算对历史野火数据的准确性。 制定技术框架的关键组成部分包括： 1)识别并获得对创建圣地亚哥县地形和景观的高分辨率数字模型所需的各种数据集的访问权限。2)研究如何使用GIS软件系统将这些数据集无缝地“缝合在一起”。 正在开发的整合方法将使用圣地亚哥县未烧毁的地区进行调查？的圣玛格丽塔生态保护区，这是一个理想的快速原型和验证网站为这个项目。 它很容易受到我们希望研究的强烈圣安娜风事件的影响，并且已经配备了通过NSF的高性能无线研究和教育网络（HPWREN）连接的密集的实时无线地面传感器。

Wildfires are increasing in both absolute number and severity in the American southwest and this trend is predicted to continue over decades to come. Therefore, the need for descriptive and predictive simulation tools to support wildfire prevention, or suppression during future wildfire events, is becoming critical. As a first step, this project aims to develop a technical framework for integrating three dimensional landscape models, real-time environmental data, and suite of simulation codes, and wildfire management protocols. This research will involve determining how best to merge elevation and ground classification datasets, couple fire propagation, atmospheric, and hydrologic simulation codes, and verify the accuracy of the coupled computations against historical wildfire data. Key components in the development of the technical framework include: 1) Identifying and obtaining access permissions to the wide variety of datasets needed to create the high-resolution digital model of the topography and landscape of San Diego County. 2) Investigating how these datasets can be seamlessly "sewn together" using GIS software systems. The integration methodology being developed will be investigated using an unburned area of the San Diego County?s Santa Margarita Ecological Reserve, which is an ideal rapid prototyping and validation site for this project. It is prone to the strong Santa Ana wind events we wish to study and is already equipped with intensive real-time wireless ground sensors connected through the NSF's High Performance Wireless Research and Education Network (HPWREN).