Cazanacli, Dan2021-10-252021-10-252021-09https://hdl.handle.net/11299/225121University of Minnesota Ph.D. dissertation. September 2021. Major: Earth Sciences. Advisors: Chris Paola, Vaughan Voller. 1 computer file (PDF); vii, 94 pages.This research is centered on developing simplified models that can reproduce natural deltaic distributary networks and deltaic stratigraphy at channel scale resolution. Modeling distributary channel networks poses unique challenges due to the complexity and limited understanding of the main genetic processes responsible for channel branching and network evolution. We explore to what extent a model based on a minimal set of rules can replicate channel arrangements that are representative of field deltas. The proposed model is based on a partly correlated random walk algorithm for generating individual branches coupled with a bifurcation probability that translates into channel branching generating networks. Systematic and heuristic exploration of the input indicates that if the probability distributions and the proportions between terms are properly constrained the model can predictably generate realistically looking networks that, by several metrics (reach lengths distribution, relative location of bifurcation nodes, dispersion of the outlets, and overall delta shape) are comparable to a number of natural distributary networks. To model deltaic stratigraphy a novel approach based stochastic superposition of deltaic networks is introduced as an alternative approach to forward process modeling. Stratigraphic sequences are realized through guided (i.e., rule based) superposition of topographic surfaces sampled from a large (104) database of distributary networks that are topologically similar to field deltas. The approach is well suited for subaerial deltas which are characterized by stochastic processes that are difficult to implement numerically (i.e., avulsion, bifurcation, and channel migration).enDeltasNetworksRule-basedStratigraphyThesis or Dissertation