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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 19, issue 9
Hydrol. Earth Syst. Sci., 19, 3845–3856, 2015
https://doi.org/10.5194/hess-19-3845-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 19, 3845–3856, 2015
https://doi.org/10.5194/hess-19-3845-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 11 Sep 2015

Research article | 11 Sep 2015

Use of satellite and modeled soil moisture data for predicting event soil loss at plot scale

F. Todisco1, L. Brocca2, L. F. Termite1, and W. Wagner3 F. Todisco et al.
  • 1Department of Agricultural, Food and Environmental Sciences, Hydraulic and Forestry Division, University of Perugia, Perugia, Italy
  • 2Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Italy
  • 3Department of Geodesy and Geoinformation, Vienna University of Technology, 10 Gusshausstr. 27–29, Vienna, Austria

Abstract. 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 2008–2013. The results showed that including soil moisture observations in the event rainfall–runoff 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 ha−1. 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.

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We developed a new formulation of USLE, named Soil Moisture for Erosion (SM4E), that directly incorporates soil moisture information. SM4E is applied here by using modeled data and satellite observations obtained from the Advanced SCATterometer (ASCAT). SM4E is found to outperform USLE and USLE-MM models in silty–clay soil in central Italy. Through satellite data, there is the potential of applying SM4E for large-scale monitoring and quantification of the soil erosion process.
We developed a new formulation of USLE, named Soil Moisture for Erosion (SM4E), that directly...
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