Understanding hydrologic controls of sloping soil response to precipitation through machine learning analysis applied to synthetic data

Soil and underground conditions prior to the initiation of rainfall events control the hydrological processes that occur in slopes, affecting the water exchange through their boundaries. The present study aims at identifying suitable variables to be monitored to predict the response of sloping soil to precipitation. The case of a pyroclastic coarse-grained soil mantle overlaying a karstic bedrock in the southern Apennines (Italy) is described. Field monitoring of stream level recordings, meteorological variables, and soil water content and suction has been carried out for a few years. To enrich the field dataset, a synthetic series of 1000 years has been generated with a physically based model coupled to a stochastic rainfall model. Machine learning techniques have been used to unwrap the non-linear cause-effect relationships linking the variables. The k-means clustering technique has been used for the identification of seasonally recurrent slope conditions in terms of soil moisture and groundwater level, and the random forest technique has been used to assess how the conditions at the onset of rainfall controlled the attitude of the soil mantle to retain much of the infiltrating rainwater. The results show that the response in terms of the fraction of rainwater remaining stored in the soil mantle at the end of rainfall events is controlled by soil moisture and groundwater level prior to the rainfall initiation, giving evidence of the activation of effective drainage processes.