Performance Evaluation of Spatially Distributed, CN-based Rainfall-Runoff Model Configurations for Implementation in Spatial Land Use Optimization Analyses

Due to their large computational burden, spatially distributed hydrological models are unsuitable for iterative spatial optimization analyses, which need to take into account location-specific runoff water accumulation and re-infiltration processes. Therefore, a computationally efficient, raster-based and spatially-explicit RR-model was conceptualized. For this purpose, the SCS-CN approach was implemented in a spatially distributed manner, adjusting CN values to antecedent soil moisture conditions (AMC) and routing runoff to the catchment outlet. To take into account spatial interaction along the overland flow paths, the SCS-CN approach was extended with a reinfiltration algorithm. Several model configurations were evaluated, based on combinations of two AMC correction methods and three re-infiltration algorithms. These model configurations were applied in three catchments in Flanders, Belgium. The results indicate a poor performance of the default SCS-CN method, which was, however, considerably improved by the implementation of AMC correction and re-infiltration. Though calibration of the RR-model configurations was limited, several model configurations achieved model performances adequate for use in spatial optimization analyses. The most performant RR-model configuration (NSE of 0.57, 0.56 and 0.64 for the three catchments) implements a lambda of 0.05, the AMC correction method proposed by Neitsch et al. (2011), and the re-infiltration method of Van Loo (2018), calculating overland flow velocity with Manning's n adjusted to seasonal vegetation cover variations. As this model allows for spatial interaction through re-infiltration, but with limited computation times, it is well applicable for iterative spatial optimization analyses, although a more extensive calibration and validation exercise on catchments with a wider range of characteristics is required to provide a general assessment of its applicability and accuracy.