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Abstract
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 (at 10 locations with three different depths) for 356 rainfall events on a steep, forested hillslope between 2007 and 2016. The soil moisture features for hydrologic events can be effectively represented by the antecedent soil moisture, soil moisture difference index, and standard deviation of the peak-to-peak time between rainfall and soil moisture response. Five clusters were delineated for hydrologically meaningful event classifications in the SOM representation. The two-dimensional spatial weighting patterns in the SOM provided more insights into the relationships between rainfall characteristics, antecedent wetness, and soil moisture response at different locations and depths. The distinction of the classified events could be explained by several rainfall features and antecedent soil moisture conditions that resulted in different patterns attributable to combinations of hillslope hydrological processes, vertical flow, and lateral flow along either surface or subsurface boundaries for the upslope and downslope areas.
Highlights
Soil moisture information is critical for assessing water storage, for estimating the quantity of runoff generated, and for determining the slope stability of hillslopes during rainfall (Angermann et al, 2017; Lu and Godt, 2008; Penna et al, 2011; Tromp Van Meerveld and McDonnell, 2005)
Rainfall characteristics and responses of soil moisture at the hillslope scale were explored by applying self-organizing map (SOM) to a dataset comprising information on a considerable number of hydrologic events
Hydrologic events were characterized using rainfall and soil moisture data collected over a period of 10 years from a steep hillside
Summary
Soil moisture information is critical for assessing water storage, for estimating the quantity of runoff generated, and for determining the slope stability of hillslopes during rainfall (Angermann et al, 2017; Lu and Godt, 2008; Penna et al, 2011; Tromp Van Meerveld and McDonnell, 2005). Hillslope hydrological processes are affected by several factors, including topography, soil texture, and eco-hydrological parameters (Baroni et al, 2013; Liang et al, 2011; RodriguezIturbe et al, 2006; Rosenbaum et al, 2012; Western et al, 1999), which result in highly nonstationary and heterogeneous spatiotemporal distributions of soil moisture (Penna et al, 2009; Wilson et al, 2004). The functional relationship between rainfall events and soil moisture depends on several factors, such as soil texture, depth, topography, and vegetation cover (Bachmair et al, 2012; Gwak and Kim, 2016; Liang et al, 2011). Rainfall characteristics, including the total quantity, duration, intensity, and dry period duration, have been explored to understand the soil mois-
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