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 also 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 peak rainfall intensity and
hydraulic roughness. We further use this approach to examine variations
in debris flow inundation patterns at different stages of postfire
recovery. Sensitivity analysis indicates that constraints on temporal
changes in hydraulic roughness, saturated hydraulic conductivity, 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 scenario-based planning for postfire hazard
mitigation.