What does this research mean for the field?

Geographically Weighted Regression (GWR) significantly outperforms Ordinary Least Squares (OLS) in modeling the spatial variability of soil erosion, revealing that topography is the primary driver in mountainous areas while land cover is the primary driver in agricultural zones. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The study aims to analyze spatial variations in soil erosion and identify key drivers affecting erosion in the Qara-Su watershed.

April 8, 2026Open Access

Spatial Pattern of Soil Erosion Drivers and Prioritizing Soil Conservation Areas Using Ordinary Least Squares and Geographically Weighted Regression

Key Points

The study aims to analyze spatial variations in soil erosion and identify key drivers affecting erosion in the Qara-Su watershed.
Collected soil erosion factors using the RUSLE model across 13,845 points in the watershed.
Examined general relationships with Ordinary Least Squares (OLS) regression.
Analyzed spatial relationships and importance of factors using Geographically Weighted Regression (GWR).
Compared model performance using fit indices such as R2 and Akaike Information Criterion (AICc).
Average annual soil erosion was approximately 7.64 tons per hectare, indicating moderate erosion risk.
GWR model demonstrated better performance with higher R2 (0.62) compared to OLS (0.50).
Local GWR coefficients revealed spatial non-stationarity, highlighting LS (topography) as critical in mountainous areas.
The C factor was notably protective in agricultural land-use areas.

Abstract

The spatial assessment of soil erosion drivers provides essential information for prioritizing soil conservation areas. This study aims to compare the performance of the Ordinary Least Squares (OLS) regression model and the Geographically Weighted Regression (GWR) model in explaining and analyzing the spatial variations of soil erosion in the Qara-Su watershed (Ardabil Province, Iran) and identifying the relative roles of the driving factors affecting erosion. To determine the relative importance of factors influencing soil erosion in the Qara-Su watershed, potential soil erosion (A) data and RUSLE model factors, including R, K, LS, C, and P, were collected at 13,845 points within the watershed. Initially, general relationships between erosion and contributing factors were examined using the OLS regression model. Subsequently, to analyze the spatial variability of relationships and identify the relative importance of factors at different locations within the watershed, the GWR model with an adaptive kernel and optimal bandwidth selection based on AICc was employed. The performance of the OLS and GWR models was compared based on fit indices such as R2 and Akaike Information Criterion corrected (AICc), and the relative importance of erosion factors was determined based on the mean local GWR coefficients. Results from the RUSLE model indicated an average annual soil erosion of approximately 7.64 tons per hectare, suggesting that the watershed falls into the moderate erosion risk category. According to the GWR model, significant improvements in explaining variations and reducing errors were observed, with higher R2 and adjusted R2 values (0.62 vs. 0.50) and lower AICc values (3687 vs. 97,848) compared to the OLS model. The local GWR coefficients confirmed spatial non-stationarity and revealed that LS (topography) has the highest importance in mountainous areas. The C factor showed a stronger protective effect in agricultural land-use areas. These results provide a basis for developing targeted strategies to mitigate and manage erosion drivers with higher relative importance and facilitate a better understanding of the causes and mechanisms of soil erosion across the watershed.

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