Tools and Trainings
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The USGS webpage for Survey Data Series 690 provides access to livestock grazing data from 25 BLM offices in 13 states including spatial and tabular data related to BLM grazing allotments.
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The After Fire Toolkit and Information website is where managers, landowners, or communities can find guidance for assessing and preventing potential damage due to post-fire flooding and related events. Browse this site to find information on the research, methods, and tools available for measuring and reducing risks associated with post-fire flooding, debris flows and sedimentation.
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FOFEM (a First Order Fire Effects Model) is a computer program for predicting tree mortality, fuel consumption, smoke production, and soil heating caused by prescribed fire or wildfire.
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The relationship between climate and wildfire area burned suggests how fire regimes may respond to a changing climate. This West-wide data publication contains a 27-year record (1980-2006) of climatological variables used to develop statistical models of area burned that can be projected into the future. We provide a separate file for each of the 56 Bailey’s ecosections (Bailey 2016) across the West, with annual area burned and 112 climate predictor variables such as evapotranspiration, precipitation, relative humidity, soil moisture, snow-water equivalent, minimum and maximum temperature, and vapor pressure deficit. These historical and future hydroclimate projections and historical fire area burned data were derived for McKenzie and Littell (2016).
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A resource for firefighters, fire managers, the public, and anyone who may be interested in wildfire’s effect on the sagebrush-steppe ecosystem.
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The USGS has developed and released a website for data distribution and visualization.
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The MoD-FIS tool seasonally modulates fuel model data in the Great Basin and Southwest regions. MoD-FIS incorporates seasonal variability of herbaceous cover. These fine fuel measurements are then used to capture changes to fire behavior fuel models based on the current fire season herbaceous production.
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The USGS developed a dataset that estimates 2017 herbaceous annual percent cover predicted on May 1st with an emphasis on annual grasses. These data were developed to provide land managers and researchers with early-season, near-real-time predictions of spatially explicit percent cover predictions of herbaceous annual vegetation in the study area.
This data comes with several caveats. First, as an early-season dataset, it will not reflect the end-of-season estimated percent cover of annual grass in many areas. In fact, some areas with annual grass cover will reflect no cover at this early date. Second, these estimates should be viewed as relative abundances. Third, each pixel in the dataset represent 250-meters and can include a geolocation error of up to 125 meters. Comparing this dataset to similar datasets with different spatial resolutions can lead to substantial differences between datasets. Fourth, this dataset represents annual herbaceous for 2017 forecast on May 1. This dataset is a forecast, and mapping could improve with later map development dates (e.g., July 1). This forecast is considered accurate and reasonable given this early season of mapping.
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This dataset provides an estimate of 2015 cheatgrass percent cover in the northern Great Basin at 250 meter spatial resolution. The information is designed to provide a near-real-time estimate of cheatgrass in the northern Great Basin for 2015 to optimize land management efforts to control cheatgrass, preserve critical greater sage-grouse habitat, and inform fire control and prevention. Timely maps of dynamic cheatgrass percent cover are needed in early summer for these purposes. Research shows that cheatgrass percent cover is spatially and temporally highly variable in arid and semiarid environments because cheatgrass germination and growth is highly sensitive to annual weather, especially precipitation totals and timing. Precipitation totals and timing are also spatially and temporally highly variable in these environments; therefore, this dataset is only representative of cheatgrass percent cover during 2015 and does not represent any other time period.