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

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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.

Figures

  • Table 1. Summary statistics of the 22 m long plot data available at the Masse site.
  • Figure 1. View of the Masse experimental station for monitoring water soil loss at plot scale in Umbria (central Italy).
  • Figure 2. Time series of satellite-derived and estimated (through the SWBM) soil moisture at the beginning of 63 erosive events in the study period 2008–2013.
  • Figure 3. Regression models between measured soil loss Aue and the erosivity indices θ Re and QrRe of the calibration subset. Linear models (a), (b), (c): SM4E model and satellite soil moisture (a); SM4E model and estimated soil moisture (b); USLE-M model and estimated runoff coefficient (c). Power models (d), (e), (f): SM4E model and satellite soil moisture (d); SM4E model and estimated soil moisture (e); USLE-MM model and estimated runoff coefficient (f).
  • Table 2. Calibration parameters and validation root mean square error for the SM4E models (Eq. 4).
  • Figure 4. Testing of the Aue vs. θ Re and the Aue vs. QrRe models with the validation subset. Linear models (a), (b), (c): SM4E model and satellite soil moisture (a); SM4E model and estimated soil moisture (b); USLE-M model and estimated runoff coefficient (c). Power models (d), (e), (f): SM4E model and satellite soil moisture (d); SM4E model and estimated soil moisture (e); USLE-MM model and estimated runoff coefficient (f).
  • Figure 5. Comparison of the results obtained by the power SM4E model with both satellite and estimated soil moisture, the USLEMM including predicted runoff, and the original USLE, in terms of root mean square error (RMSE) and coefficient of determination (R2).
  • Figure 6. Comparison of the results obtained by the power SM4E model with both satellite and estimated soil moisture, the USLE-MM including predicted runoff, and the original USLE, in terms of deviations between estimated, Aue,est, and observed, Aue, soil losses. The values of the estimated runoff and of the mean soil moisture computed as the mean between the estimated and the satellite-retrieved values are also given.

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CITATION STYLE

APA

Todisco, F., Brocca, L., Termite, L. F., & Wagner, W. (2015). Use of satellite and modeled soil moisture data for predicting event soil loss at plot scale. Hydrology and Earth System Sciences, 19(9), 3845–3856. https://doi.org/10.5194/hess-19-3845-2015

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