## 1 Introduction  
Rockfalls have long been recognized as ubiquitous hazards in mountainous regions [1; 2; 3; 4; 5; 6; 7; 8]. Although rockfall volumes are typically small when compared to other landslide processes, their high frequency has often been associated with recurrent losses [5; 9; 10]. As a response, a variety of rockfall management approaches have been developed to assess rockfall hazard and risk, and to support decision-making for rockfall risk mitigation strategies [10; 11; 12; 13; 14; 15; 16; 17; 18; 19]. Quantifying rockfall hazard and risk, and the design of rockfall mitigation structures, require information about the falling block volumes (and mass), their fragmentation during motion, and their frequencies [13; 20; 21; 22; 23; 24]. Information on rockfall volumes and fragmentation can be gathered from comprehensive records or estimated through surveying fallen blocks along known rockfall trajectories [6; 11; 13; 16; 20; 23]. However, these methods present challenges where rockfall records are scarce and block surveys are not possible due to the steepness of the terrain or falling blocks entering waterbodies downslope from the rock fall sources. Recognizing this challenge, a number of researchers have performed fragmentation field testing [25; 26], evaluation of impact energy thresholds for fragmentation [27], and numerical simulation of fragmentation [28; 29]. These have provided valuable insights that allowed the development of rockfall trajectory simulations capable of modeling block fragmentation based on the power-law volume distribution typically observed in rockfall deposits [9; 30; 31; 32]. However, these models still require validation of the parameters of the power-law distributions of block volumes used for fragmentation. The rock block volume distribution at the rockfall source, or In situ Rock Block Distribution (IRBD), can be calculated based on information on the rock mass discontinuities. Detailed 3-dimensional surface models of the rockfall sources obtained through Light Detection and Ranging (LiDAR) and photogrammetric techniques allow for discontinuity mapping and direct calculation of potential rockfall volumes [33; 34; 32; 35]. Further, rock mass discontinuity information can be used to develop 3-dimensional computational realizations of the rock mass structure (Discrete Fracture Networks (DFNs)). Accounting for the stochastic characteristics of discontinuity orientation, spacing and finite persistence in developing DFNs has shown enhanced capability for estimating IRBD of potential rockfall events [33; 34; 36; 37]. However, IRBD from direct measurements in surface models or DFN realizations of the rockfall source are not compatible with rock block distributions of fragmented rockfalls [31; 35]. This paper presents a method to estimate the fragmented rockfall volume distribution in the absence of rockfall records or fallen block surveys, which could be a direct input into rockfall trajectory models that consider fragmentation. The method takes advantage of photogrammetric techniques to build 3-dimensional surface models of rockfall sources for virtual structural mapping of discontinuities. This method is based on the hypothesis that fragmentation in strong rocks will tend to occur through weak planes and by growth of non-persistent discontinuities as the in situ blocks detach, fall, bounce, and roll. It is assumed that the resulting fragmented blocks can be approximated by increasing the persistence of mapped discontinuities in DFN realizations. This implies that other breakage mechanisms in strong rocks would have less influence in the fragmented volume distribution of rockfalls, and could be neglected for the purpose of dimensioning rockfall protection structures without being overly conservative. The method is tested at two limestone rock cliffs in the Canadian Cordillera: the Tornado Mountain site adjacent to a railway line owned and operated by Canadian Pacific Railway (CP) and Site S042, adjacent to Highway 742 in Alberta which is maintained by Alberta Transportation (AT).