Monitoring and predicting sediment yield in a small Sicilian basin

Citation
V. Ferro et al., Monitoring and predicting sediment yield in a small Sicilian basin, T ASAE, 44(3), 2001, pp. 585-595
Citations number
44
Categorie Soggetti
Agriculture/Agronomy
Journal title
TRANSACTIONS OF THE ASAE
ISSN journal
00012351 → ACNP
Volume
44
Issue
3
Year of publication
2001
Pages
585 - 595
Database
ISI
SICI code
0001-2351(200105/06)44:3<585:MAPSYI>2.0.ZU;2-C
Abstract
Identifying areas of a basin that are most sensitive to erosion have stimul ated the study of within-basin variability of the sediment-delivery process es and the use of spatially distributed models. To verify the reliability o f a sediment-delivery distributed model applicable at the morphological uni t scale (i.e., the area of clearly defined aspect, length, and steepness), experiments were carried out at mean annual and event scales in a small Sic ilian basin. A Geographical Information System is briefly presented into wh ich the measurements carried out at the basin outlet (runoff, sediment yiel d, etc.) and other point and areal information (soil erodibility, digital t errain model, etc.) were entered. For validating the model at mean annual temporal scale, the sediment yield spatial distribution calculated by the model was compared with the net soil erosion spatial distribution obtained by cesium-137 activity measurements. At morphological unit scale, the agreement between measured and calculated sediment yield values showed a good predictive ability of the model at mea n annual temporal scale. Finally, the model was calibrated and tested using five rainfall-runoff eve nts measured at the outlet of the experimental basin. The analysis showed t hat the coefficient beta, appearing in the expression of the sediment deliv ery ratio of each morphological unit, is independent of subdivision criteri on and can be estimated by the hydrological characteristics of the rainfall -runoff event. The comparison between measured and calculated sediment yiel d values showed that the sediment delivery distributed approach has a good predictive ability at event scale, too.