Quantifying fire effects on debris flow runout using a morphodynamic model and stochastic surrogates

Fire affects soil and vegetation, which in turn can promote the initiation and growth of runoff-generated debris flows in steep watersheds. Postfire hazard assessments often focus on identifying the most likely watersheds to produce debris flows, quantifying rainfall intensity-duration thresholds for debris flow initiation, and estimating the volume of potential debris flows. This work seeks to expand on such analyses and forecast downstream debris flow runout and peak flow depth. Here, we report on a high-fidelity computational framework that enables debris flow simulation over two watersheds and the downstream alluvial fan, although at significant computational cost. We then develop a Gaussian Process surrogate model, allowing for rapid prediction of simulator outputs for untested scenarios. We utilize this framework to explore model sensitivity to rainfall intensity and sediment availability as well as parameters associated with saturated hydraulic conductivity, hydraulic roughness, grain size, and sediment entrainment. Simulation results are most sensitive to and grain size. Further, we use this approach to examine variations in debris flow inundation patterns at different stages of postfire recovery. Sensitivity analysis indicates that constraining temporal changes in hydraulic roughness and grain size following fire would be particularly beneficial for forecasting debris flow runout throughout the postfire recovery period. The emulator methodology presented here also provides a means to compute the probability of a debris flow inundating a specific downstream region, consequent to a forecast or design rainstorm. This workflow could be employed in prefire scenario-based planning or postfire hazard assessments.