Abstract. Hydrologic events can be characterized as particular combinations of hydrological processes on a hillslope scale. To configure hydrological mechanisms, we analyzed a dataset using an unsupervised machine learning algorithm to cluster the hydrologic events based on the dissimilarity distances between the weighting components of a self-organizing map (SOM). The time series of soil moisture was measured at 30 points (in 10 locations with 3 varying depths) for 356 rainfall events on a steep, forested hillslope between 2007 and 2016. Soil moisture features for hydrologic events can be effectively represented by the antecedent soil moisture, maximum variation, and standard deviation of peak-to-peak time between rainfall and soil moisture response. Five clusters were delineated for hydrologically meaningful event classification in the SOM representation. The two-dimensional spatial weighting patterns in the SOM provided greater insight on relationships between rainfall characteristics, antecedent wetness, and soil moisture response at different locations and depths. The distinction of the classified events can be explained by several rainfall features and antecedent soil moisture conditions that resulted in different patterns made by combinations of hillslope hydrological processes, vertical flow, and lateral flow along either surface or subsurface boundaries for the upslope and downslope areas.
Supplementary material to "Characterization of Hillslope Hydrologic Events through a Self-Organizing Map"