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Abstract. Hydrologic events can be characterized as particular combinations ofhydrological processes on a hillslope scale. To configure hydrologicalmechanisms, we analyzed a dataset using an unsupervised machine learningalgorithm to cluster the hydrologic events based on the dissimilaritydistances between the weighting components of a self-organizing map (SOM).The time series of soil moisture was measured at 30 points (at 10 locationswith three different depths) for 356 rainfall events on a steep, forestedhillslope between 2007 and 2016. The soil moisture features for hydrologicevents can be effectively represented by the antecedent soil moisture, soilmoisture difference index, and standard deviation of the peak-to-peak timebetween rainfall and soil moisture response. Five clusters were delineatedfor hydrologically meaningful event classifications in the SOMrepresentation. The two-dimensional spatial weighting patterns in the SOMprovided more insights into the relationships between rainfallcharacteristics, antecedent wetness, and soil moisture response at differentlocations and depths. The distinction of the classified events could beexplained by several rainfall features and antecedent soil moistureconditions that resulted in different patterns attributable to combinationsof hillslope hydrological processes, vertical flow, and lateral flow alongeither surface or subsurface boundaries for the upslope and downslope areas.

Characterization of soil moisture response patterns and hillslope hydrological processes through a self-organizing map