Unravelling spatiotemporal patterns of event-based surface rainfall-runoff response using a cellular automata approach

Abstract

Understanding surface water flow is critical for hydrological modeling and water resource management. Distributed hydrological models capture spatial heterogeneity in surface runoff, yet their full potential, especially in simulating surface flow complexities, requires further exploration. To bridge this gap, we developed the Surface Rainfall-Runoff Cellular Automata (SRRCA) model, a distributed hydrological framework featuring a Local-Finer Iteration (LFI) strategy to mitigate evolution errors from varying iteration steps. Implemented in a watershed in Wharfedale, England, the SRRCA model leverages multi-scale capabilities to resolve catchment-scale runoff dynamics and grid-scale flow interactions. Results indicated significant infiltration fluctuations in early rainfall due to spatial heterogeneity, alongside a strong correlation between peak flow and catchment size. Cells near confluence points exhibit delayed peaks, highlighting the influence of spatial position on runoff. This study systematically evaluates the strengths and limitations of cellular automata in hydrological modeling, and introduces a novel paradigm for investigating spatial heterogeneity in hydrology.