Global hydrological models still run at tens of kilometers, too coarse to resolve local floods; hyperresolution, about 1 km globally and 100 m continentally, remains the goal. With the network already mapped, the cost has shifted from mapping it to computing over it: refining a 10 km grid to 1 km multiplies cells a hundredfold, and deeper, longer flow paths make routing climb several hundredfold. Yet how a flow network should be organized has rarely been studied, though that organization governs the cost as much as the hardware. On the global 90 m MERIT Hydro network of 22.2 billion land pixels, we show how three structural choices do so: ordering sets locality in a core's private cache, layering in the shared cache, and partitioning balances load across processors without communication. Because the network is static, each structure is precomputed once from D8 flow directions and released as MERIT-FlowTopo v1.0, a reusable foundation any model traversing the network in drainage order reads instead of rebuilding. Across four terrain kernels (the elementary memory-bound step shared by routing computations on the fixed network), depth-first ordering cuts serial runtime up to 5.1-fold and lowers the simulated L3 cache-miss rate from about 37 % to 6 %, while a conflict-free downstream layering introduced here keeps converging flows lock-free and deterministic without atomic operations. MERIT-FlowTopo extends MERIT Hydro from a map to a basis for computing on it, fixing in the data the locality, scheduling, and load balance hyperresolution would otherwise demand as parallel-computing expertise.