Spatial modeling of relationship between soil erosion factors and land-use changes at sub-watershed scale for the Talar watershed, Iran

Fahimeh Mirchooli (), Maziar Mohammadi () and Seyed Hamidreza Sadeghi ()
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Fahimeh Mirchooli: Tarbiat Modares University
Maziar Mohammadi: Tarbiat Modares University
Seyed Hamidreza Sadeghi: Tarbiat Modares University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2023, vol. 116, issue 3, No 39, 3703-3723

Abstract: Abstract Soil erosion is one of the most common types of land degradation. To provide useful information for proper management, quantitative soil erosion evaluation and identifying influential factors are needed. However, rare studies have been reported on spatial modeling of soil erosion in connection with affective factors to prioritize the locality and the type of erosion control measures. Hence, the aim of this study was to (1) assess erosion-prone areas in the Talar watershed, Iran, using the revised universal soil loss equation (RUSLE) model and (2) investigate the relationship between soil erosion variability and land-use changes. Toward that, the ordinary least squares (OLS), geographically weighted regression (GWR) models, and principal component analysis (PCA) were used to analyze spatial relationships between soil erosion, land-use, and the RUSLE factors. The results of the OLS and GWR models indicated that these relationships are spatially non-stationary. GWR models had a good predictive performance than OLS with lower Akaike’s Information Criterion (from 254.31 to 276.81 in OLS and from 247.87 to 269.42 in GWR) and higher adjusted R2 values (from 0.12 to 0.54 in OLS, and from 0.36 to 0.66 in GWR). Among the variables mentioned above, LS factor, P factor, forest, and irrigated land were the most influential variables in GWR models. The results of PCA showed that PC1 and PC2 explained 66.2% of the variation in soil erosion concerning land-use and the RUSLE factors. These results provided appropriate references for managers and experts properly planning the study watershed. Graphical abstract

Keywords: Albourzian watershed; Soil degradation; Regression analysis; Remote sensing; Spatial non-stationarity (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11069-023-05832-2

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