Predictions of river flow dynamics provide vital information for many aspects of water management including water resource planning, climate adaptation, and flood and drought assessments. Many of the subjective choices that modellers make including model and criteria selection can have a significant impact on the magnitude and distribution of the output uncertainty. Hydrological modellers are tasked with understanding and minimising the uncertainty surrounding streamflow predictions before communicating the overall uncertainty to decision makers.

A novel nested flood model (MSN_Flood) is applied to simulate complex coastal-fluvial urban flooding in order to critically examine the model's capability to forecast evolution of urban inundation. The model demonstrates high accuracy of outputs without incurring the computational expense of high spatial resolution over the entire model domain. MSN_Flood provides full characteristics of water levels and flow regimes necessary for flood hazard identification and flood risk assessment.