Modelling runoff and erosion effects of wildland fires
Millions of dollars are spent annually in California on erosion control measures after wildland fires despite the lack of quantitative evidence that such measures are needed or beneficial. Application of grass seeding and other measures after a fire are often justified on the basis of a perceived concern about the risk of debris and mud flows endangering property and lives. While the effectiveness of such measures can be seriously questioned, the risk may nonetheless be significant. We have conducted a two year study to test and expand a recently developed physically-based model for predicting the spatial pattern of landslide potential. This model can be applied at any time and serve as part of planning document to prepare for the effects of fire. The model uses digital elevation data and is based on simple assumptions about runoff and slope stability mechanisms; in it's simplest form the model is parameter free and easily calibrated.
We first tested the model in the Oakland firestorm area. Here high resolution digital elevation data were available and 78 landslide scars had been mapped in a 12.12 km2 area. A simple ratio of effective precipitation rate to the ability of the soil to transmit the water is calculated from the model for the condition of instability. The smaller the ratio, the higher the potential instability. As we have found elsewhere, there is threshold value which delineates all observed scars in the Oakland hill. During the study, the Highway 41 fire occurred in the San Luis Obispo area, consuming 196 km2 of landslide prone topography. Aerial photographs were taken, and a detailed survey using global positioning techniques was accomplished to guide the construction of a detailed digital elevation model of the site. This past winter after the fire was one of the wettest of record and a total of 467 shallow landslides were mapped, with 82% occurring in the unburned areas and most of the landslides in the burned area occurring where the 11 grass seeding was most effective. Once we obtain the digital data we will use the landslide model to test various hypotheses for this unexpected, but important result.