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Water erosion is a current issue, especially in hilly and areas, where driving force such as surface runoff and subsurface flow can mobilize large amounts of sediment to rivers. In fact, how and at which timescale, seasonality precipitation is turned into runoff or streamflow (Q) it is difficult to be predicted without calibrating site-specific models. The potential soil erosion can be assessed through the study of the relationships between sediment sources and sinks in a watershed (i.e., sediment connectivity assessment) and associated suspended sediment (SS) transport in rivers. On the other hand, sediment connectivity, defined as structural (from a geomorphological point of view) and functional connectivity (considering forcing processes), can be evaluated by the using of specific indexes (e.g., Index of connectivity IC). SS transport processes are intermittent processes fluctuating over a large range of temporal and spatial scales, making it challenging to develop predictive models applicable across timescales and rivers. While temporal variability in sediment transport is explained by the concept of effective timescale of connectivity; the mechanism behind this variability remains unknown. Here we used a data-driven approach considering two years of monitoring Q and SS to develop and demonstrate a proof of concept for automating the classification of event-based sediment dynamics by using a machine learning approach. For each storm event we i) calculate the sediment connectivity (extreme rainfall events also are considered) and ii) define the link between sediment transport and deposition by considering SS transport as a fractal system (i.e. fractal storage time distributions in streams). Fractals are here used to describe and predict patterns over different temporal scales of dynamics in SS The statistic and dynamics of Q, SSCs and associated grain size distribution, at event based, were considered by assessing their probability distribution function, Fourier power spectra, and the machine-learning classification of hysteresis index. Indeed, by approaching SS transport dynamics as a fractal system, it is assumed that patterns of variation in SS transport exist over different timescales, while linkages across those temporal scales are expressed as fractal power-laws. The study site, located near Florence in the Chianti area, is a 1 Km2 agricultural watershed with different types of land cover and characterize by a first-order mixed bedrock and alluvial stream channels. The area was mapped at high resolution with a Drone LIDAR scanner and equipped with a submersible laser diffraction particle size analyser (LISST) for long-term measuring suspended particle size and its volume concentration. Preliminary results showed a robust correlation between sediment connectivity, land cover, and sediment connectivity. Q-SS information flows exhibit seasonally varying behaviour consistent with dominant runoff generation mechanisms (catchment connectivity in wet to dry season). However, the timing and the magnitude of runoff also reflect considerable catchment heterogeneity, likely attributable to differences in baseflow contributions from different lithologies, and variation in of preferential flow paths (land use/land cover). In conclusion, this study allowed to analyse a small catchment area in term of sediment connectivity and related sediment transport to identify potential areas of (dis)connectivity in the basin.
Barbadori et al. (Mon,) studied this question.