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Predicting sediment delivery from debris flows after wildfire
Title | Predicting sediment delivery from debris flows after wildfire |
Publication Type | Journal Article |
Year of Publication | 2015 |
Authors | Nyman, P, Smith, HG, Sherwin, C, Langhans, C, Lane, PNJ, Sheridan, GJ |
Journal | Geomorphology |
Volume | 250 |
Start Page | 173 |
Date Published | 12/2015 |
Abstract | Debris flows are an important erosion process in wildfire-prone landscapes. Predicting their frequency and magnitude can therefore be critical for quantifying risk to infrastructure, people and water resources. However, the factors contributing to the frequency and magnitude of events remain poorly understood, particularly in regions outside western USA. Against this background, the objectives of this study were to i) quantify sediment yields from post-fire debris flows in southeast Australian highlands and ii) model the effects of landscape attributes on debris flow susceptibility. Sediment yields from post-fire debris flows (113–294 t ha− 1) are 2–3 orders of magnitude higher than annual background erosion rates from undisturbed forests. Debris flow volumes ranged from 539 to 33,040 m3 with hillslope contributions of 18–62%. The distribution of erosion and deposition above the fan were related to a stream power index, which could be used to model changes in yield along the drainage network. Debris flow susceptibility was quantified with a logistic regression and an inventory of 315 debris flow fans deposited in the first year after two large wildfires (total burned area = 2919 km2). The differenced normalised burn ratio (dNBR or burn severity), local slope, radiative index of dryness (AI) and rainfall intensity (from rainfall radar) were significant predictors in a susceptibility model, which produced excellent results in terms identifying channels that were eroded by debris flows (Area Under Curve, AUC = 0.91). Burn severity was the strongest predictor in the model (AUC = 0.87 when dNBR is used as single predictor) suggesting that fire regimes are an important control on sediment delivery from these forests. The analysis showed a positive effect of AI on debris flow probability in landscapes where differences in moisture regimes due to climate are associated with large variation in soil hydraulic properties. Overall, the results from this study based in the southeast Australian highlands provide a novel basis upon which to model sediment delivery from post-fire debris flows. The modelling approach has wider relevance to post-fire debris flow prediction both from risk management and landscape evolution perspectives. |
URL | http://www.sciencedirect.com/science/article/pii/S0169555X15301355 |