The increasing threat of post-wildfire hazards creates an imperative for improved post-wildfire flooding and debris flow prediction capabilities. Because rainfall is a primary driver of predictive hydrology and debris flow initiation and inundation models, recent efforts have emphasized the need for interdisciplinary collaboration between meteorology and post-wildfire hazard science that develops more accurate rainfall estimates with longer lead times. In this work, we identified critical knowledge gaps for developing rainfall estimates and filled those gaps by reviewing recent literature and synthesizing pre-existing datasets. Gap areas were organized into the following general topics: a) rainfall intensity-duration-frequency relations, b) time-varying rainfall, c) spatially varying rainfall, and d) rainfall regimes.

Recent key research advances include the increasing availability of gridded quantitative rainfall estimates, the expanded use of distributed hydrologic and erosion models that incorporate spatial and temporal variability in rainfall, and the linking of concepts and modeling from the atmospheric and climate sciences with post-wildfire hazard science. We prototype a rainfall regime regionalization schema that captures self-similar properties of rainfall intensity (k, the maximum rainfall intensity) and temporal scaling (n, the decay rate). Our k-n relations schema could serve as a framework for organizing, interpreting, and predicting post-wildfire hydrologic and erosional responses. Finally, we summarize salient gaps for implementing spatiotemporally varying rainfall as the driver of post-wildfire hydrologic models designed to improve the prediction of flooding and debris flow hazards to the built environment for emergency managers.