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Geosciences 2018, 8(7), 249; https://doi.org/10.3390/geosciences8070249

Investigating the Sediment Yield Predictability in Some Italian Rivers by Means of Hydro-Geomorphometric Variables

1
ENEA (Italian National Agency for New Technologies, Energy and Sustainable Economic Development), Laboratory of Technologies for Structure Dynamics and the Prevention of Seismic and Geohydrological Risk, Casaccia Research Centre, Via Anguillarese 301, 00123 Santa Maria di Galeria, Roma, Italy
2
ENEA, Laboratory of Technologies for Structure Dynamics and the Prevention of Seismic and Geohydrological Risk, Portici Research Centre, Piazzale E. Fermi 1, 80055 Portici, Italy
3
ENEA, Laboratory of Technologies for Structure Dynamics and the Prevention of Seismic and Geohydrological Risk, Frascati Research Centre, Via E. Fermi 45, 00044 Frascati, Roma, Italy
*
Author to whom correspondence should be addressed.
Received: 30 May 2018 / Revised: 27 June 2018 / Accepted: 3 July 2018 / Published: 5 July 2018
(This article belongs to the Special Issue Quantitative Geomorphology)
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Abstract

In the present work, preliminary results are reported from an ongoing research study aimed at developing an improved prediction model to estimate the sediment yield in Italian ungauged river basins. The statistical correlations between a set of hydro-geomorphometric parameters and suspended sediment yield (SSY) data from 30 Italian rivers were investigated. The main question is whether such variables are helpful to explain the behavior of fluvial systems in the sediment delivery process. To this aim, a broad set of variables, simply derived from digital cartographic sources and available data records, was utilized in order to take into account all the possible features and processes having some influence on sediment production and conveyance. A stepwise regression analysis pointed out that, among all possibilities, the catchment elevation range (Hr), the density of stream hierarchical anomaly (Da), and the stream channel slope ratio (ΔSs) are significantly linked to the SSY. The derived linear regression model equation was proven to be satisfactory (r2-adjusted = 0.72; F-significance = 5.7 × 10−8; ME = 0.61), however, the percentage standard error (40%) implies that the model is still affected by some uncertainties. These can be justified, on one hand, by the wide variance and, on the other hand, by the quality of the observed SSY data. Reducing these uncertainties will be the effort in the follow-up of the research. View Full-Text
Keywords: geomorphometrics; river sediment yield; multivariate regression analysis; prediction model geomorphometrics; river sediment yield; multivariate regression analysis; prediction model
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Grauso, S.; Pasanisi, F.; Tebano, C.; Grillini, M.; Peloso, A. Investigating the Sediment Yield Predictability in Some Italian Rivers by Means of Hydro-Geomorphometric Variables. Geosciences 2018, 8, 249.

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