Bioenergy has attracted considerable attention within the scientific community, industries, consumers, and policymakers due to expanded demand for low-carbon renewable fuels. Further, it can reduce greenhouse gas (GHG) emission and mitigation of climate change, enhance energy security, and create new jobs to enable rural economy. However, these goals can be achieved by sustainable production of bioenergy through robust supply chains, which require (i) reliable and precise assessment of the availability of sustainable biomass/feedstock, (ii) appropriate design to optimize strategic decisions in the supply chain, and (iii) reduce uncertainties in the tactical/operational decisions. This dissertation presented an efficient and robust spatial modeling framework to quantify precisely the availability of sustainable crop residues for a large-scale area, identify potential and optimal sites to locate biorefineries and develop biomass supply curves with the delivered cost of crop residues. Moreover, the watershed model was used to estimate energycrop yield, annual availability of sustainable biomass in the strip-mined lands and impacts of growing energy crops, i.e., Miscanthus on water quality. Various biomass supply logistics and storage options were analyzed to identify the most economical way to deliver feedstock to biorefineries. A GIS integrated discrete-event simulation model was developed to identify optimal locations of a biorefinery and simulate the biomass supply chain to estimate delivered cost of biomass, energy usage, and carbon footprint. The lifecycle assessment (LCA) and techno-economic assessment (TEA) approaches were used to quantify carbon footprint and minimum selling price (MSP) of delivering renewable fuel (i.e. BioCNG) produced from co-digestion of animal and food waste and energy crops via aneraobic digestion (AD) technologies. The generilized and integrated bioenergy supply chain model can be used for developing and managing a location-specific sustainable biorefinery.