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

Abstract

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.

Predicting plot soil loss by empirical and process-oriented approaches. A review

“…for a very different condition from that corresponding to event runoff estimation at the plot scale. Notwithstanding this, Todisco et al (2015) concluded that the MISDc model provided fairly accurate results even at the smaller plot scale.…”

“…For example, an important problem in central Italy is that high soil losses can occur when the soil is initially dry but simulated runoff increases with the antecedent soil moisture conditions. Therefore, explicitly considering the soil's response to wetting in these cases (e.g., crusting phenomena) appears necessary to obtain reliable QR predictions (Todisco et al, 2015). With respect to this point, the runoff prediction method by Vandervaere et al (1998) appears to be a good candidate method deserving further investigation.…”

“…According to Todisco et al (2015), the pre-event soil water content, θ, could be used instead of the runoff coefficient to correct the rainfall-runoff erosivity factor, because obtaining soil moisture data is easier and less expensive than estimating surface runoff. Todisco et al (2015) tested their approach at Masse (Italy), considering both satellite-derived and modelled soil water content but not direct measurements of θ.…”

“…Another attempt to predict the runoff coefficient with the specific objective to simplify application of the USLE-MM was carried out by Todisco et al (2015). In particular, the continuous rainfall-runoff model MISDc (Modello Idrologico Semi-Distribuito in continuo; Brocca et al, 2011) was used to estimate runoff volumes.…”

“…This model incorporates a limited number of parameters and it is characterised by low computational requirements. However, it needs parameterisation, which was carried out by maximising the Nash-Sutcliffe efficiency index between the estimated and the observed runoff volume values for a set of calibration events (Todisco et al, 2015). The semi-distributed MISDc model was originally developed for flood simulation over large areas (>100 km 2 ; Brocca et al, 2011), i.e.…”