Calibration and application of an erosion and C redistribution model (SPEROS-C) to twelve small catchments in southeastern Spain

E. Nadeu, Kristof Van Oost, Joris de Vente, Carolina Boix-Fayos


This paper presents a new methodology to assess sediment connectivity at the catchment scale. The proposed index CCI (Catchment Connectivity Index) is based on a combination of factors in a GIS environment that determine the connectivity of sediment in different elements (slopes, basins, channels) of the river system. The factors evaluated are the transport capacity on hillslopes (TC), check-dams’ trap efficiency (TE), the presence of geomorphological barriers (GF), flow conditions (FC) and the sediment transport capacity in channels (SP). This index was applied to estimate connectivity in the Upper Taibilla catchment (314 km2) (SE Spain) for different land use (1956 and 2006) and management (check-dams) scenarios. This catchment has suffered major land use and land cover changes over the last 50 years. The intense agricultural abandonment process and the implementation of soil erosion control measures (reforestation and check-dams) have strongly affected sediment dynamics of the basin. Calculation of the CCI allows identifying which landscape elements have most impact on the sediment (dis)connectivity at catchment scale. The results show a significant reduction in the connectivity of 76% between 1956 and 2006. However, it is observed that the check-dams contribute only 3% to this reduction. Land use changes had a much higher impact on reducing sediment connectivity, except in some areas with steep slopes, or with the development of agriculture along the main channels of the drainage network. Altogether, CCI showed to be a relatively easy and effective method that can be used for spatio-temporal sediment connectivity analyses in areas with natural and human disturbances.


sediment transport, geomorphological factors, connectivity index, check-dams, land use changes,



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© Universidad de La Rioja, 2013

ISSN 0211-6820

EISSN 1697-9540