Testing the Efficiency of Parameter Disaggregation for Distributed Rainfall-Runoff Modelling

A variety of hydrological models is currently available. Many of those employ physically based formulations to account for the complexity and spatial heterogeneity of natural processes. In turn, they require a substantial amount of spatial data, which may not always be available at sufficient quality. Recently, a top-down approach for distributed rainfall-runoff modelling has been developed, which aims at combining accuracy and simplicity. Essentially, a distributed model with uniform model parameters (base model) is derived from a calibrated lumped conceptual model. Subsequently, selected parameters are disaggregated based on links with the available spatially variable catchment properties. The disaggregation concept is now adjusted to better account for non-linearities and extended to incorporate more model parameters (and, thus, larger catchment heterogeneity). The modelling approach is tested for a catchment including several flow gauging stations. The disaggregated model is shown to outperform the base model with respect to internal catchment dynamics, while performing similarly at the catchment outlet. Moreover, it manages to bridge on average 44% of the Nash-Sutcliffe efficiency difference between the base model and the lumped models calibrated for the internal gauging stations. Nevertheless, the aforementioned improvement is not necessarily sufficient for reliable model results.

Automated summary

There are various hydrological models available, with many using physically based formulations that require a substantial amount of spatial data. A new top-down approach for distributed rainfall-runoff modelling has been developed to combine accuracy and simplicity by deriving a distributed model with uniform parameters from a calibrated lumped conceptual model. The approach has shown improved performance in capturing internal catchment dynamics compared to the base model, although further improvements are needed for reliable results.