Coupling wildfire spread and erosion models to quantify post-fire erosion before and after fuel treatments
Michele Salis A G , Liliana Del Giudice B , Peter R. Robichaud C , Alan A. Ager D , Annalisa Canu A , Pierpaolo Duce A , Grazia Pellizzaro A , Andrea Ventura A , Fermin Alcasena-Urdiroz E , Donatella Spano B F and Bachisio Arca A
+ Author Affiliations
- Author Affiliations
A Institute for the BioEconomy, National Research Council of Italy (CNR IBE), Traversa La Crucca 3, 07100 Sassari, Italy.
B University of Sassari, Department of Agriculture, Via De Nicola, 07100 Sassari, Italy.
C USDA Forest Service, Rocky Mountain Research Station, Forestry Sciences Laboratory, 1221 South Main Street, Moscow, ID 83843, USA.
D USDA Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory, 5775 US Highway 10W, Missoula, MT 59808, USA.
E University of Lleida, Department of Agricultural and Forest Engineering, Avenida da Rovira Roure 191, 25198 Lleida, Spain.
F Euro-Mediterranean Centre on Climate Change (CMCC) Foundation, Via De Nicola 9, 07100 Sassari, Italy.
G Corresponding author. Email: michele.salis@ibe.cnr.it
International Journal of Wildland Fire 28(9) 687-703 https://doi.org/10.1071/WF19034
Submitted: 12 March 2019 Accepted: 10 July 2019 Published: 15 August 2019
Abstract
Wildfires are known to change post-fire watershed conditions such that hillslopes can become prone to increased erosion and sediment delivery. In this work, we coupled wildfire spread and erosion prediction modelling to assess the benefits of fuel reduction treatments in preventing soil runoff. The study was conducted in a 68 000-ha forest area located in Sardinia, Italy. We compared no-treatment conditions v. alternative strategic fuel treatments performed in 15% of the area. Fire behaviour before and after treatments was estimated by simulating 25 000 wildfires for each condition using the minimum travel time fire-spread algorithm. The fire simulations replicated historic conditions associated with severe wildfires in the study area. Sediment delivery was then estimated using the Erosion Risk Management Tool (ERMiT). Our results showed how post-fire sediment delivery varied among and within fuel treatment scenarios. The most efficient treatment alternative was that implemented near the road network. We also evaluated other factors such as exceedance probability, time since fire, slope, fire severity and vegetation type on post-fire sediment delivery. This work provides a quantitative assessment approach to inform and optimise proactive risk management activities intended to reduce post-fire erosion.
Additional keywords: fire behaviour, fire management, fire prevention, post-fire impacts.
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