Titelaufnahme

Titel
Use of satellite and modeled soil moisture data for predicting event soil loss at plot scale
VerfasserTodisco, F. ; Brocca, L. ; Termite, L. F. ; Wagner, Wolfgang In der Gemeinsamen Normdatei der DNB nachschlagen
Erschienen in
Hydrology and earth system sciences, 2015, Jg. 19, H. 9, S. 3845-3856
Erschienen2015
Ausgabe
Published version
SpracheEnglisch
DokumenttypAufsatz in einer Zeitschrift
URNurn:nbn:at:at-ubtuw:3-2178 Persistent Identifier (URN)
DOI10.5194/hess-19-3845-2015 
Zugriffsbeschränkung
 Das Werk ist frei verfügbar
Dateien
Use of satellite and modeled soil moisture data for predicting event soil loss at plot scale [2.14 mb]
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Zusammenfassung (Englisch)

The potential of coupling soil moisture and a Universal Soil Loss Equation-based (USLE-based) model for event soil loss estimation at plot scale is carefully investigated at the Masse area, in central Italy. The derived model, named Soil Moisture for Erosion (SM4E), is applied by considering the unavailability of in situ soil moisture measurements, by using the data predicted by a soil water balance model (SWBM) and derived from satellite sensors, i.e., the Advanced SCATterometer (ASCAT). The soil loss estimation accuracy is validated using in situ measurements in which event observations at plot scale are available for the period 20082013. The results showed that including soil moisture observations in the event rainfallrunoff erosivity factor of the USLE enhances the capability of the model to account for variations in event soil losses, the soil moisture being an effective alternative to the estimated runoff, in the prediction of the event soil loss at Masse. The agreement between observed and estimated soil losses (through SM4E) is fairly satisfactory with a determination coefficient (log-scale) equal to 0.35 and a root mean square error (RMSE) of 2.8 Mg ha1. These results are particularly significant for the operational estimation of soil losses. Indeed, currently, soil moisture is a relatively simple measurement at the field scale and remote sensing data are also widely available on a global scale. Through satellite data, there is the potential of applying the SM4E model for large-scale monitoring and quantification of the soil erosion process.