As populations continue to move into more mountainous terrain, a greater understanding of the processes controlling debris flows has become important for the protection of human life and property. The potential volume of an expected debris flow must be known to effectively mitigate any hazard it may pose, yet an accurate estimate of this parameter has to this point been difficult to model. To this end, a probabilistic method for the prediction of debris flow volumes using a database of 1351 yield rate measurements from 33 post-wildfire, runoff-generated debris flows in the Western USA is presented herein. A number of geomorphological, climatic, and geotechnical basin characteristics were considered for inclusion in the model, and correlation analysis was conducted to identify those with the greatest influence on debris flow yield rates. Groupings within the database were then clustered based on their similarity levels; a total of six clusters were identified with similar slope angle and burn intensity characteristics. For each of these six clusters, a probability density function detailing the distribution of yield rates within the cluster was developed. The model uses a Monte Carlo simulation to combine each of these distributions into a single probabilistic model for any basin in which a debris flow is expected to occur. This approach was validated by applying the model to ten basins that experienced debris flows of known volumes throughout the Western USA. The model predicted nine of the ten debris flow volumes to within the 95% confidence interval of the final distribution; a regression analysis for the ten volumes resulted in an R 2 of 0.816. These results compared favorably with those generated by an existing volume model. This approach provides accurate results based on easily obtainable data, encouraging widespread use in land planning and development.