We introduce and compare two approaches to consistently combine release and runout in GIS-based landslide susceptibility modeling. The computational experiments are conducted on data from the Schnepfau investigation area in western Austria, which include a high-quality landslide inventory and a landslide release susceptibility map. The two proposed methods use a constrained random walk approach for downslope routing of mass points and employ the probability density function (PDF) and the cumulative density function (CDF) of the angles of reach and the travel distances of the observed landslides. The bottom-up approach (A) produces a quantitative spatial probability at the cost of losing the signal of the release susceptibility, whereas the top-down approach (B) retains the signal and performs better, but results in a semi-quantitative score. Approach B also reproduces the observed impact area much better than a pure analysis of landslide release susceptibility. The levels of performance and conservativeness of the model results also strongly depend on the choice of the PDF and CDF (angle of reach, maximum travel distance, or a combination of both).