A geospatial approach to assessing soil erosion in a watershed by integrating socio-economic determinants and the RUSLE model
Bhandari, KP and Aryal, J and Darnsawasdi, R, A geospatial approach to assessing soil erosion in a watershed by integrating socio-economic determinants and the RUSLE model, Natural Hazards, 75, (1) pp. 321-342. ISSN 0921-030X (2015) [Refereed Article]
The amount, degree of severity, and risk of soil erosion in managed landscapes mainly depend on human activities such as vegetation removal, grazing, urbanisation, poor agricultural management, and planned burning. However, the underlying mechanisms that ultimately drive the activities causing soil erosion for a particular location are less obvious. We address this issue by integrating stakeholder perceptions of socio-economic determinants of soil erosion and the revised universal soil loss equation (RUSLE) for the Phewa watershed, Pokhara, Nepal. A RUSLE model was applied to estimate the soil erosion status of the watershed based on socioeconomic-topographical factors. The output of the model indicated that the current annual rate of soil erosion in the Phewa watershed varies from 0 to 206.8 t ha−1 year−1, with a mean annual soil loss rate of 14.7 t ha−1 year−1. We used a structured questionnaire to collect socio-economic variables related to soil erosion in the watershed. Bivariate correlation and stepwise multiple regression analyses revealed ten socio-economic variables that were predictors of soil erosion. The analysis generated five predictive models: the first (R2 = 0.65), second (R2 = 0.71), third (R2 = 0.79), and fourth model (R2 = 0.85) significantly (p < 0.01) explained the variability of soil erosion rate across the watershed, while the fifth (full) model significantly (p < 0.01) explained 89% of the variability of soil erosion rate (R2 = 0.89). Our study identified socio-economic variables such as household size, farm labour availability, level of education, conservation cost, training, membership of organisation committees, distance, farm size, migration, and farm income as predictor variables of soil erosion.