Probabilistic Wildfire Risk Flame Length Probability 2 (Image Service)

<div style='text-align:Left;'><div><div><p><span>National data on burn probability (BP) and conditional flame-length probability (FLP) were generated for the conterminous United States (CONUS), Alaska, and Hawaii using a geospatial Fire Simulation (FSim) system developed by the USDA Forest Service Missoula Fire Sciences Laboratory. The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current (end of 2020) landscape conditions and fire management practices. The data presented here represent modeled BP and FLPs for the United States (US) at a 270-meter grid spatial resolution. Flame-length probability is estimated for six standard Fire Intensity Levels. The six FILs correspond to flame-length classes as follows: FLP1 = &lt; 2 feet (ft); FLP2 = 2 &lt; 4 ft.; FLP3 = 4 &lt; 6 ft.; FLP4 = 6 &lt; 8 ft.; FLP5 = 8 &lt; 12 ft.; FLP6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FLP data must be used in conjunction with the BP data for risk assessment.</span></p><p><span>These data are a newer edition of the Short et al. (2016, 2020) data publications. This third edition is based on circa 2020 landscape data, which were the most current LANDFIRE products available at the time of production. The methods used to generate these data generally followed the same process used in previous editions, with improvements made at specific steps. The process steps outlined in the Data Quality, Lineage section of this metadata document are expanded from previous editions to more fully explain each step and provide additional details on methods for this edition. Beyond the newer input landscape data from LANDFIRE, we also used updated datasets for other inputs such as fire occurrence, observed gridded daily weather, and wind data from weather stations. To better capture recent climate conditions, we also shortened the time period of historical weather records used to inform the generation of simulated weather streams for simulation runs, using the most recent 15 years this time (2006-2020) rather than full record from 1972-2012 in the second edition. See the process steps described in the Data Quality, Lineage section for more details.</span></p></div></div></div>

美国农业部林务局（USDA Forest Service）密苏拉火灾科学实验室开发的地理空间火灾模拟（Fire Simulation, FSim）系统，针对美国本土（conterminous United States, CONUS）、阿拉斯加州与夏威夷州生成了火灾概率（burn probability, BP）与条件火焰长度概率（conditional flame-length probability, FLP）的全国性数据集。FSim系统涵盖天气生成、野火发生、火行为蔓延与火灾扑救四大模块，旨在模拟数万次假设的当代火灾季场景下的野火发生与蔓延过程，以此估算当前（2020年末）景观条件与火灾管理模式下，指定栅格像元发生火灾的概率。本数据集呈现了美国全境270米网格空间分辨率下的模拟BP与FLP数据。研究针对6个标准火灾强度等级（Fire Intensity Levels, FILs）估算了火焰长度概率，6个FIL对应的火焰长度分级如下：FLP1 = 小于2英尺（ft）；FLP2 = 2~4英尺；FLP3 = 4~6英尺；FLP4 = 6~8英尺；FLP5 = 8~12英尺；FLP6 = 12英尺及以上。由于FLP数据属于条件概率范畴（即表示火灾发生时，达到特定强度等级的可能性），因此开展火灾风险评估时，需将其与BP数据结合使用。

本数据集为Short等人2016年、2020年发布的相关数据出版物的最新修订版。本次第三版基于2020年前后的景观数据，即该数据制作时可获取的最新LANDFIRE产品。本次数据生成方法大体沿用了过往版本的流程，并在特定环节进行了优化升级。本元数据文档的“数据质量、溯源”章节中列出的流程步骤，相较过往版本进行了扩充，以更全面地阐释每个环节，并补充了本版数据的方法细节。除了采用LANDFIRE更新后的输入景观数据外，研究团队还更新了其他输入数据集，如火灾发生观测数据、网格化逐日天气数据，以及源自地面气象站的风速数据。为更好地反映近期气候条件，本次研究将用于生成模拟天气序列的历史天气记录时段缩短为最近15年（2006-2020年），而非第二版中使用的1972-2012年完整观测记录。更多细节可参见“数据质量、溯源”章节中描述的流程步骤。