High-resolution mapping of pluvial flooding in ungauged agricultural catchments

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 recorded at two nested outlets (84 and 111 ha) of a single experimental catchment. The framework decouples the rainfall-runoff process to systematically compare adjusted Soil Conservation Service Curve Number (SCS-CN) formulations against two spatially explicit routing algorithms (SCS velocity method vs. Simulator for Water Resources in Rural Basins, 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 Kling-Gupta Efficiency (KGE) values of 0.35 vs. 0.34). Crucially, while the routing phase acts as a mechanical propagator of volumetric uncertainty, it effectively synchronizes the overall runoff window. Applied to 25- and 100-year design storms, this framework illustrates hydrodynamic attenuation and kinematic synchronization at confluences. While primarily validated on a single well-monitored catchment, these findings illustrate the approach's potential to pinpoint hydraulic hotspots, with initial benchmarks indicating promising scalability to larger agricultural landscapes.