five

Data for developing fuel consumption models for pine flatwoods fuel types in the southeastern United States

收藏
Figshare2015-01-02 更新2026-04-28 收录
下载链接:
https://figshare.com/articles/dataset/Data_for_developing_fuel_consumption_models_for_pine_flatwoods_fuel_types_in_the_southeastern_United_States/27006280
下载链接
链接失效反馈
官方服务:
资源简介:
Modeling fire effects, including terrestrial and atmospheric carbon fluxes and pollutant emissions during wildland fires, requires accurate predictions of fuel consumption. Data were collected in order to develop empirical models which predict fuel consumption. Collected data included fuel and environmental measurements on a series of operational prescribed fires in pine flatwoods ecosystems in the southeastern United States from 2004-2006. Total prefire fuel loading ranged from 4.6 to 23.7 megagrams per hectare (2.1 to 10.6 tons per acre); between 12 and 69% of the total loading was composed of shrub species, including saw palmetto (Serenoa repens), gallberry (Ilex glabra), and other common associates. Fuel consumption ranged from 1.3 to 15.7 megagrams per hectare (0.6 to 7.0 tons per acre). On average, 76% of the prefire fuel loading was consumed, although fuel consumption as a percentage of prefire loading was somewhat variable (range: 28-93%).In this study, empirical models were developed to predict fuel consumption in pine flatwoods forest ecosystems from measurements of shrubs and other fuels before and after fires and day-of-burn environmental conditions. The models developed as part of this study will be programmed into Consume and its successor programs. Shrub fuel consumption estimates based on field observations will allow for more informed and effective fire planning and fire use for southern pine forests in which the understory is dominated by shrubby vegetation. Pine flatwoods were selected for study due to their large acreage, wide geographic range, likelihood of fire and emissions to impact populated areas, and extensive annual prescribed burning in the type.Original metadata date was 05/22/2015. Minor metadata updates on 12/15/2016 and 07/21/2022.
创建时间:
2015-01-02
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作