Soil Loss Estimation Using Models and Field Database in Lateritic Badlands, Eastern India: Evaluation and Validation

2021 ◽  
pp. 131-176
Author(s):  
Sandipan Ghosh
Keyword(s):  
Soil Loss ◽  
Loss Estimation ◽  
Eastern India ◽  
Field Database

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

2015 ◽  
Vol 19 (9) ◽  
pp. 3845-3856 ◽  
Author(s):  
F. Todisco ◽  
L. Brocca ◽  
L. F. Termite ◽  
W. Wagner
Keyword(s):  
Soil Moisture ◽  
Soil Loss ◽  
Large Scale ◽  
Central Italy ◽  
Remote Sensing Data ◽  
Loss Estimation ◽  
Balance Model ◽  
Estimation Accuracy ◽  
Soil Losses

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.

scholarly journals Soil loss estimation using rusle model to prioritize erosion control in KELANI river basin in Sri Lanka

2019 ◽  
Vol 7 (2) ◽  
pp. 130-137 ◽  
Author(s):  
Cassim Mohamed Fayas ◽  
Nimal Shantha Abeysingha ◽  
Korotta Gamage Shyamala Nirmanee ◽  
Dinithi Samaratunga ◽  
Ananda Mallawatantri
Keyword(s):  
Sri Lanka ◽  
River Basin ◽  
Soil Loss ◽  
Erosion Control ◽  
Loss Estimation ◽  
Rusle Model ◽  
Kelani River
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