This paper presents a cost-efficient and reliable supply chain network design model for biomass to be delivered to biofuel plants. Biomass is bulky, so transportation modes such as rail and barge can be used to deliver this product. For this reason, this study focuses on multimodal supply chain designs for biofuel. Biomass supply is highly seasonal, but the high production seasons for biomass in the Southeast United States often coincide with or are followed by hurricanes, and drought seasons, both of which impact transportation. The dynamic multimodel transportation network design model this paper presents enables this supply chain to cope with biomass supply fluctuations and to hedge against natural disasters. The mixed-integer nonlinear programming model proposed is an -hard problem, and we develop an accelerated Benders decomposition algorithm and a hybrid rolling horizon algorithm to solve this problem. We tested the performance of the algorithm on a case study using data from the Southeast United States. The numerical experiments show that this proposed algorithm can solve large-scale problem instances to a near optimal solution in a reasonable time. Numerical analyses indicate that, under normal conditions, the minimum cost model outperforms the reliable models. However, under disaster scenarios, the minimum cost model is 2.65% to 9.20% more expensive than the reliable and static model and 6.28% to 17.73% more expensive than the reliable and dynamic model. Thus, the reliable and dynamic multimodal network design decisions can aid biofuel supply chain management decisions, especially when considering the potential impacts of natural disasters.