Abstract: To address the bottlenecks of information fragmentation, inadequate physical mechanism coupling, and low cross-medium sensing accuracy in traditional monitoring for digital twin water networks, this study constructs a space-air-ground-water-structure integrated LiDAR collaborative sensing framework spanning macro-basin, linear river channel, and micro-hydraulic structure scales. By systematically analyzing the transmission characteristics and attenuation mechanisms of laser pulses in complex media (atmosphere-vegetation-water-structure), this paper focuses on core technologies including multi-source heterogeneous data processing and cross-medium refraction correction, and proposes a physics-data dual-driven full-element inversion method. Integrating satellite laser altimetry, airborne green LiDAR, terrestrial scanning, and underwater acoustic-optic detection, the system achieves high-fidelity characterization of topographic evolution, water resource dynamics, structural defects, and flow capacity. In lake monitoring, engineering supervision, and flood disaster response, it significantly enhances the physical fidelity of hydrological simulations and the timeliness of emergency decision-making. Addressing challenges of high-turbidity signal attenuation and data assimilation, future research will focus on physics-informed neural networks (PINNs), edge-cloud collaborative architectures, and multi-spectral payloads to realize full-element dynamic mapping, thereby supporting the intelligent scheduling of national water networks.