A landscape scale model to predict post-fire debris flow impact zones

Abstract

Post-fire debris flows pose a significant hazard in mountainous regions, but accurate assessment of the associated risk at large scales, particularly in the context of mega-fires, is limited. Although numerous models exist, few are functional at the landscape scale, and none integrate assessments of downstream effects with the likelihood of flow initiation to define both the frequency and magnitude of potential flows. The aim of this work, therefore, was to develop a post-fire debris flow model that could characterise both the occurrence frequency and associated runout of debris flow events and which could be rapidly applied to fire events at the landscape scale. We achieve this using a model integration approach that included the development of a reduced-complexity debris flow runout model, which was calibrated and independently tested for its application at large scales with a dataset of 1377 individual post-fire flows across south-eastern Australia. This model was then integrated with previously published methods determining debris flow source areas and likelihood of initiation specific to our study area. The developed model is intentionally designed for rapid response hazard management. Therefore, it predominantly incorporates only the fundamental, lowest parameter form drivers of debris flow initiation and runout termination, thus minimising computational complexity while ensuring prediction accuracy sufficient for hazard assessment even at application scales as large as mega-fires. Accounting for debris flows initiated from single headwaters and cases where flows simultaneously converge from multiple initiation points, our integrated model effectively characterises debris flow hazard across a geomorphologically diverse landscape encompassing >200,000km2 (capturing ~640,000 potential debris flow initiation locations across Victoria, Australia). Model testing using an independent validation dataset resulted in debris flow runout predictions with root mean square error (RMSE) of 176 and 308 m for non-convergent and convergent flows respectively. Crucially, these predictions can be computed rapidly for large (up to landscape scale) fire events with minimal input requirements and the use of a remotely-sensed observation dataset for domain calibration. As such, the model provides a critical means of addressing the capacity gap in assessments of post-fire debris flow hazard by efficiently integrating measures of occurrence frequency and runout for functional application at large scales. This new work empowers land managers to rapidly predict the hazard arising from post-fire erosion processes and represents an important applied step towards better understanding and mitigating the impacts of wildfire-induced debris flows.