Comment on egusphere-2022-1078

Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> The assessment of the response of slopes to precipitation is important for various applications, from water supply management to hazard assessment due to extreme rainfall events. It is well known that the 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 and modelled to predict the response of the slope to precipitations. A case study consisting of a loose pyroclastic coarse grained soil cover overlaying a karstic bedrock located in the southern Apennines (Italy) is described, where field monitoring has been carried out, comprising stream level recordings, meteorological recordings, and soil water content among others. Nevertheless, to enhance the field dataset, the slope hydraulic behaviour of the case study has been simulated with a physically based model linked to a synthetic rainfall time series, getting a consistent hourly timeseries dataset of 1000 years, containing information on rainfall, aquifer water level and soil volumetric water content at different depths. Machine Learning techniques have been used to unwrap the relationships amongst the studied variables, which relations are commonly non-linear. The Random Forest technique has been used to assess the way the slope response could be addressed and the importance of each variable on the slope response and the k-means clustering technique has been used to explore the geometrical disposition of data, and so the identification of seasonally recurrent different scenarios linked to the slope response. It has been shown that the slope response in terms of the rainwater being stored in the soil cover is naturally highly dependent on the rainfall amount, but water drainage and storage processes can be identified by normalizing the change in water storage with the rainfall depth. Indeed, with the methodology presented here, different hydrometeorological scenarios controlling major hydrological processes have been identified not only from the meteorological and seasonal behaviour but also from the underground conditions prior to the rainfall initiation, weighting the role, on one hand, of the field capacity value on the ease of the water to flow in and out of the soil cover and, on the other hand, of the ground water level, the increase of which gives evidence of the activation of slope drainage even during relatively intense rainfall events.