Predicting post-fire hillslope erosion in forest lands of the western United States
Mary Ellen Miller A C , Lee H. MacDonald A , Peter R. Robichaud B and William J. Elliot B
+ Author Affiliations
- Author Affiliations
A Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, CO 80523-1476, USA.
B US Department of Agriculture, Forest Service, Rocky Mountain Research Station, Moscow, ID 83843, USA.
C Corresponding author. Present address: Michigan Technological University, Michigan Tech Research Institute, 3600 Green Court, Suite 100, Ann Arbor, MI 48105, USA. Email: mmaryellen@gmail.com
International Journal of Wildland Fire 20(8) 982-999 https://doi.org/10.1071/WF09142
Submitted: 12 December 2009 Accepted: 23 February 2011 Published: 25 October 2011
Abstract
Many forests and their associated water resources are at increasing risk from large and severe wildfires due to high fuel accumulations and climate change. Extensive fuel treatments are being proposed, but it is not clear where such treatments should be focussed. The goals of this project were to: (1) predict potential post-fire erosion rates for forests and shrublands in the western United States to help prioritise fuel treatments; and (2) assess model sensitivity and accuracy. Post-fire ground cover was predicted using historical fire weather data and the First Order Fire Effects Model. Parameter files from the Disturbed Water Erosion Prediction Project (WEPP) were combined with GeoWEPP to predict post-fire erosion at the hillslope scale. Predicted median annual erosion rates were 0.1–2 Mg ha–1 year–1 for most of the intermountain west, ~10–40 Mg ha–1 year–1 for wetter areas along the Pacific Coast and up to 100 Mg ha–1 year–1 for north-western California. Sensitivity analyses showed the predicted erosion rates were predominantly controlled by the amount of precipitation rather than surface cover. The limited validation dataset showed a reasonable correlation between predicted and measured erosion rates (R2 = 0.61), although predictions were much less than measured values. Our results demonstrate the feasibility of predicting post-fire erosion rates on a large scale. The validation and sensitivity analysis indicated that the predictions are most useful for prioritising fuel reduction treatments on a local rather than interregional scale, and they also helped identify model improvements and research needs.
Additional keywords: ground cover, modelling, sensitivity analysis, WEPP.
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