Accurate pluvial flood mapping in ungauged agricultural catchments is often constrained by a lack of calibration data. This study evaluates a parsimonious, high-resolution (1 m) distributed framework assessing peak discharge along concentrated flow paths, validated against 39 events in two nested experimental catchments (84 and 111 ha). The framework decouples the rainfall-runoff process to systematically compare adjusted SCS-CN formulations against two spatially explicit routing algorithms (SCS vs. SWRRB). Within this specific local context, findings demonstrate that the Jain initial abstraction method significantly reduces volumetric bias, with the distributed approach statistically outperforming lumped modelling. However, performance remains strictly regime-dependent, driven by rainfall intensity rather than total depth. This exposes the structural limits of static Curve Number (CN) parameterizations in capturing rapid Hortonian dynamics, causing the model to dampen minor events while amplifying high-intensity storms. Regarding hydraulic transfer, both routing strategies yield statistically equivalent performance (median KGE 0.40 vs. 0.37). Crucially, while the routing phase acts as a mechanical propagator of volumetric uncertainty, it consistently synchronizes the overall runoff window. Applied to 25- and 100-year design storms, this framework successfully identifies hydrodynamic attenuation and kinematic synchronization at confluences. Based on these empirical observations, we propose this approach as a transferable blueprint to pinpoint hydraulic hotspots and strategically allocate proactive mitigation measures in vulnerable, unmonitored regions.