Modeling data for burn severity of the East Troublesome and Grizzly Creek for integration with post-fire debris flow in the upper Colorado River basin, USA

These data were compiled for/to provide an example and assess methods and results of pre-fire estimation of predicted differenced normalized burn ration (dNBR) for predicting post-fire debris flow hazard classification. Objective(s) of our study were to develop predictive models for burn severity, using variables of pre-fire conditions, for two large wildfires from 2020 in Colorado, USA. These data represent pre-fire predictions of post-fire differenced normalized burn ratio (dNBR) as a proxy of burn severity and further understand pre-fire modeling of burn severity. These data were collected/created in the fire perimeters the East Troublesome Fire (10/14/2020 – 11/30/2020) and the Grizzly Creek Fire (8/10/2020...

Tags: Arapaho National Forest, Botany, Colorado, East Fork Troublesome Creek, East Troublesome Fire, All tags... Ecology, Estes Park, Forestry, Garfield County, Geography, Glenwood Canyon, Glenwood Springs, Granby, Grand County, Grand Lake, Grizzly Creek, Grizzly Creek Fire, Information Sciences, Kremmling, Remote Sensing, Rocky Mountain National Park, Soil Sciences, USGS Science Data Catalog (SDC), Water Resources, White River National Forest, biota, burn severity, dNBR, data release, debris flow, differenced normalized burn ratio, environment, fire damage, fire perimeters, fires, fuel, hazards, landslides, machine learning, modeling, post-fire differenced normalized burn ratio, post-fire hazard, pre-fire conditions, pre-fire estimation, pre-fire modeling, pre-fire predictions, predicted differenced normalized burn ration, predictive models, random forest modeling, remote sensing, volume of debris flow, water quality, wildfire, wildland fire, Fewer tags