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- W4234788672 abstract "<strong class=journal-contentHeaderColor>Abstract.</strong> 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 30Â 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." @default.
- W4234788672 created "2022-05-12" @default.
- W4234788672 creator A5005487766 @default.
- W4234788672 date "2021-05-20" @default.
- W4234788672 modified "2023-10-14" @default.
- W4234788672 title "Reply on RC1" @default.
- W4234788672 doi "https://doi.org/10.5194/hess-2021-107-ac1" @default.
- W4234788672 hasPublicationYear "2021" @default.
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