Comparing MCDM methods with RUSLE and EPM models for erosion risk assessment and sub-watershed prioritization in semi-arid mountainous terrain

Abstract Soil erosion threatens the sustainability of semi-arid watersheds. Sub-watersheds are routinely ranked for intervention, yet the reliability of those ranks is rarely quantified. This study assesses erosion risk across the 3,288 km² Al Khazer River Watershed in northern Iraq and measures the stability of the resulting priorities. Fifteen morphometric parameters were derived for 32 sub-watersheds from a 12.5 m ALOS PALSAR DEM, Sentinel-2 imagery and climate-station records. Criterion weights were obtained by the Analytical Hierarchy Process (consistency ratio = 0.0537). These weights were applied within four ranking models: VIKOR, TOPSIS, SAW and WASPAS. Two erosion models were then applied independently: the Erosion Potential Method (EPM) and the Revised Universal Soil Loss Equation (RUSLE). RUSLE agreed more closely with field observations (Observed-to-Estimated Agreement Ratio (AR) 78.1% and 86.96%) than EPM (49.16% and 53.0%). It was therefore adopted as the internal benchmark for validating the ranking models. All four models identified sub-watersheds 6, 4, 2 and 1 as the highest-priority zones. These watersheds combine steep slopes, high relief and elevated orographic precipitation in the northern mountains. Ordinary least squares regression showed the distance-based methods to correspond most closely with RUSLE (VIKOR R² = 0.763, TOPSIS R² = 0.754), with the two statistically indistinguishable (ΔAIC = 1.21). Input uncertainty was then propagated through the prioritization using 100 Monte Carlo iterations at perturbation levels of ± 10%, ± 20% and ± 30%. Classification stability fell from 96.9% to 84.4% for RUSLE and from 87.5% to 75.0% for TOPSIS. The four highest-priority sub-watersheds retained their classification at every perturbation level. The framework identifies erosion hotspots using only openly available terrain, satellite and climate data. It therefore offers a practical basis for targeting soil conservation investment, for routine environmental monitoring of erosion risk, and for guiding land management and land-use planning in ungauged semi-arid watersheds where field measurement is scarce.

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Journal
Scientific Reports
Published
2026-09-18
DOI
https://doi.org/10.1038/s41598-026-71677-x
Primary Topic
Groundwater and Watershed Analysis
Type
article
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article

Comparing MCDM methods with RUSLE and EPM models for erosion risk assessment and sub-watershed prioritization in semi-arid mountainous terrain

Zakariya Nafi Shehab, Raid Mahmood Faisal, Nadhir Al‐Ansari
Scientific Reports
Groundwater and Watershed Analysis
article

Comparing MCDM methods with RUSLE and EPM models for erosion risk assessment and sub-watershed prioritization in semi-arid mountainous terrain

Zakariya Nafi Shehab, Raid Mahmood Faisal, Nadhir Al‐Ansari
article en

Abstract

Abstract Soil erosion threatens the sustainability of semi-arid watersheds. Sub-watersheds are routinely ranked for intervention, yet the reliability of those ranks is rarely quantified. This study assesses erosion risk across the 3,288 km² Al Khazer River Watershed in northern Iraq and measures the stability of the resulting priorities. Fifteen morphometric parameters were derived for 32 sub-watersheds from a 12.5 m ALOS PALSAR DEM, Sentinel-2 imagery and climate-station records. Criterion weights were obtained by the Analytical Hierarchy Process (consistency ratio = 0.0537). These weights were applied within four ranking models: VIKOR, TOPSIS, SAW and WASPAS. Two erosion models were then applied independently: the Erosion Potential Method (EPM) and the Revised Universal Soil Loss Equation (RUSLE). RUSLE agreed more closely with field observations (Observed-to-Estimated Agreement Ratio (AR) 78.1% and 86.96%) than EPM (49.16% and 53.0%). It was therefore adopted as the internal benchmark for validating the ranking models. All four models identified sub-watersheds 6, 4, 2 and 1 as the highest-priority zones. These watersheds combine steep slopes, high relief and elevated orographic precipitation in the northern mountains. Ordinary least squares regression showed the distance-based methods to correspond most closely with RUSLE (VIKOR R² = 0.763, TOPSIS R² = 0.754), with the two statistically indistinguishable (ΔAIC = 1.21). Input uncertainty was then propagated through the prioritization using 100 Monte Carlo iterations at perturbation levels of ± 10%, ± 20% and ± 30%. Classification stability fell from 96.9% to 84.4% for RUSLE and from 87.5% to 75.0% for TOPSIS. The four highest-priority sub-watersheds retained their classification at every perturbation level. The framework identifies erosion hotspots using only openly available terrain, satellite and climate data. It therefore offers a practical basis for targeting soil conservation investment, for routine environmental monitoring of erosion risk, and for guiding land management and land-use planning in ungauged semi-arid watersheds where field measurement is scarce.

Scientific ReportsVol. 16(1)
University of Mosul (IQ), Luleå University of Technology (SE)
Life in Land
Openalex Percentile: Top 18%
Groundwater and Watershed Analysis
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