Post-wildfire curve number estimates for the southern Rocky Mountains in Colorado, USA
The curve number method first developed by the US Department of Agriculture Soil Conservation Service (now the Natural Resources Conservation Service) is often used for post-wildfire runoff assessments. These assessments are critical for land and emergency managers making decisions on life and property risks following a wildfire event. Three approaches (i.e., historical event observations, linear regression model, and regression tree model) were used to help estimate a post-wildfire curve number from watershed and wildfire parameters. For the first method, we used runoff events from 102 burned watersheds in Colorado, southern Wyoming, northern New Mexico, and eastern Utah to quantify changes in curve number values from pre- to post-wildfire conditions. The curve number changes from the measured runoff events vary substantially between positive and negative values. The measured curve number changes were then associated with watershed characteristics (e.g., slope, elevation, northness, and eastness) and land cover type to develop prediction models that provide estimates of post-wildfire curve number changes. Finally, we used a regression tree method to demonstrate that accurate predications can be developed using the measured curve number changes from our study domain. These models can be used for future post-wildfire assessments within the region.
Citation Information
| Publication Year | 2024 |
|---|---|
| Title | Post-wildfire curve number estimates for the southern Rocky Mountains in Colorado, USA |
| DOI | 10.21079/11681/48652 |
| Authors | Jeremy Giovando, Wyatt Reis, Rose Shillito, Elizabeth Shaloka, Christina Chow, Michael S. Kohn, Natalie Memarsadeghi |
| Publication Type | Report |
| Publication Subtype | Federal Government Series |
| Series Title | Technical Report |
| Series Number | ERDC-TR-24-12 |
| Index ID | 70256149 |
| Record Source | USGS Publications Warehouse |
| USGS Organization | Colorado Water Science Center |