Infrastructure network systems support society and the economy by facilitating the distribution of essential services across broad spatial extents, at a range of scales. The complex and interdependent nature of these systems provides the conditions for which localised failures can dramatically cascade, resulting in disruptions that are widespread and very often unforeseen. This systemic vulnerability has been highlighted multiple times over the previous decades in infrastructures systems from around the world. In the future, the hazards to which infrastructure systems are exposed are set to grow with increasing extreme event risks caused by climate change. The aim of this thesis is to develop methodology and analysis for understanding and reducing the risk of failure of national interdependent infrastructure network systems. This study introduces multi-scale, system-of-systems based methodology and applied analysis that provides important new insights into interdependent infrastructure network risk and adaptation. Adopting a complex network based approach; real-world asset data is integrated from the energy, transport, water, waste and digital communications sectors to represent the physical interconnectivity that exists within and between interdependent infrastructure systems. Given the often limited scope of real-world datasets, an algorithm is presented that is used to synthesise missing network data, providing continuous network representations that preserve the most salient spatial and topological properties of real multi-level infrastructure systems. Using the resultant network representations, the criticality of individual assets is calculated by summing the direct and indirect customer disruptions that can occur in the event of failure. This is achieved by disrupting sets of functional service flow pathways that transcend sectorial and operational boundaries, providing long-range connectivity between service originating source nodes and customer allocated sink nodes. Kernel density estimation is used to integrate discrete asset criticality values into a continuous surface from which statistically significant infrastructure geographical criticality hotspots are identified. Finally, a business case is presented for investment in infrastructure adaptation, where adaptation costs are compared to the reduction in expected damages that arise from interdependency related failures over an assets lifetime. By representing physical and geographic interdependence at a range of scales, this analysis provides new evidence to inform the targeting of investments to reduce risks and enhance system resilience. It is concluded that the research presented within this thesis provides new theoretical insights and practical techniques for a range of academic, industrial and governmental infrastructure stakeholders, from the UK and beyond.