On Using Clustering for the Optimization of Hydrological Simulations

Accurate water-related predictions and decision-making require a simulation of hydrological systems in high spatio-temporal resolution. However, the simulation of such a large-scale dynamical system is compute-intensive, and hence time consuming. One approach to circumvent these issues is to use landscape properties to reduce model redundancies and computation complexities. This work shows an ongoing project that applies existing clustering methods to identify functionally similar model units and runs the model only on representative model units. The proposed approach consists of several steps, in particular the reduction of dimensionality of the hydrological time series, application of clustering methods, choice of cluster representative, and study of the balance between the uncertainty of the simulation output and the computational effort.