No abstract

In August 2016, a magnitude 6.0 earthquake struck Central Italy, starting a devastating seismic sequence, aggravated by other two events of magnitude 5.9 and 6.5, respectively. After the first mainshock, four Italian institutions installed a dense temporary network of 50 seismic stations in an area of 260 km2. The network was registered in the International Federation of Digital Seismograph Networks with the code 3A and quoted with a Digital Object Identifier (10.13127/SD/ku7Xm12Yy9). Raw data were converted into the standard binary miniSEED format, and organized in a structured archive. Then, data quality and completeness were checked, and all the relevant information was used for creating the metadata volumes. Finally, the 99 Gb of continuous seismic data and metadata were uploaded into the INGV node of the European Integrated Data Archive repository. Their use was regulated by a Memorandum of Understanding between the institutions. After an embargo period, the data are now available for many different seismological studies.

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 (H r ), the density of stream hierarchical anomaly (D a ), and the stream channel slope ratio (∆S s ) are significantly linked to the SSY. The derived linear regression model equation was proven to be satisfactory (r 2 -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.

No abstract