Spatial estimation of soil erosion in Quetta Region, Pakistan: a GIS and Remote Sensing integrated RUSLE model-based approach

Ali, I. and Khatibi, B.M. and Karimzadeh, S. (2025) Spatial estimation of soil erosion in Quetta Region, Pakistan: a GIS and Remote Sensing integrated RUSLE model-based approach. Malaysian Journal of Soil Science (MJSS) (Malaysia), 29. pp. 88-101. ISSN 1394-7990

Soil erosion is a prevalent issue causing land degradation worldwide. This study aims to determine the spatial distribution of annual soil erosion through the utilization of the Revised Universal Soil Loss Equation (RUSLE) model in Quetta sub-basin, situated in the southwestern region of Pakistan. To accomplish this, numerous data mining techniques were employed, along with machine learning algorithms, to produce thematic layers (R, K, LS, C, and P) that served as input parameters for the RUSLE model. According to the resultant model, soil erosion in the study area ranged from 0.00 to 866 tons per hectare per year. The estimated values for rainfall-runoff erosivity (R), soil erodibility (K), topography (LS), and cover management (C), factors ranged from 147 to 191 (MJ.mm.ha⁻¹.h⁻¹year⁻¹), 0.0229 to 0.0259 (t.ha.MJ⁻¹mm⁻¹), 0.002 to 360.77, and 0.001 to 1, respectively. The statistics revealed that 58% of the land in the study area experiences a very low degree of soil erosion, with an erosion rate of less than 13.58 t/ha/year. About 24% of the study area faces low erosion, with an erosion rate spanning from 13.58-44.16 t/ha/year. 13% of the area is demarcated as moderate soil erosion severity, at an erosion rate ranging from 44.16-81.53.14 t/ha/year. On the other hand, 5% of the study area experienced high to very high soil erosion, with an erosion rate of 81.53-866.34 t/ha/year. The northern part (Takatu range), north-eastern part (Zarghoon range) eastern-central (Murdar range), and western-southern (Chiltan range), which are characterized by steep slopes and barren land, experience high to very high severity of soil erosion. Decisively Remote Sensing and GIS, in combination with the RUSLE model, are significant for identifying input factors for modeling soil erosion and resource management. This study will provide firsthand guidance to assist policy/decision-makers and planners in pinpointing the erosion-prone areas that urgently need soil conservation measures.

Full text available from: Official URL: https://www.msss.com.my/mjss/Full%20Text/vol29/V29...

Abstract

Soil erosion is a prevalent issue causing land degradation worldwide. This study aims to determine the spatial distribution of annual soil erosion through the utilization of the Revised Universal Soil Loss Equation (RUSLE) model in Quetta sub-basin, situated in the southwestern region of Pakistan. To accomplish this, numerous data mining techniques were employed, along with machine learning algorithms, to produce thematic layers (R, K, LS, C, and P) that served as input parameters for the RUSLE model. According to the resultant model, soil erosion in the study area ranged from 0.00 to 866 tons per hectare per year. The estimated values for rainfall-runoff erosivity (R), soil erodibility (K), topography (LS), and cover management (C), factors ranged from 147 to 191 (MJ.mm.ha⁻¹.h⁻¹year⁻¹), 0.0229 to 0.0259 (t.ha.MJ⁻¹mm⁻¹), 0.002 to 360.77, and 0.001 to 1, respectively. The statistics revealed that 58% of the land in the study area experiences a very low degree of soil erosion, with an erosion rate of less than 13.58 t/ha/year. About 24% of the study area faces low erosion, with an erosion rate spanning from 13.58-44.16 t/ha/year. 13% of the area is demarcated as moderate soil erosion severity, at an erosion rate ranging from 44.16-81.53.14 t/ha/year. On the other hand, 5% of the study area experienced high to very high soil erosion, with an erosion rate of 81.53-866.34 t/ha/year. The northern part (Takatu range), north-eastern part (Zarghoon range) eastern-central (Murdar range), and western-southern (Chiltan range), which are characterized by steep slopes and barren land, experience high to very high severity of soil erosion. Decisively Remote Sensing and GIS, in combination with the RUSLE model, are significant for identifying input factors for modeling soil erosion and resource management. This study will provide firsthand guidance to assist policy/decision-makers and planners in pinpointing the erosion-prone areas that urgently need soil conservation measures.

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Item Type:	Article
AGROVOC Term:	soil erosion models
AGROVOC Term:	soil
AGROVOC Term:	soil conservation
AGROVOC Term:	spatial analysis
AGROVOC Term:	Gissar sheep
AGROVOC Term:	remote sensing
AGROVOC Term:	machine learning
AGROVOC Term:	topography
AGROVOC Term:	land degradation
Geographical Term:	Pakistan
Depositing User:	Mr. Khoirul Asrimi Md Nor
Date Deposited:	21 Jul 2026 14:14
Last Modified:	21 Jul 2026 14:14
URI:	http://webagris.upm.edu.my/id/eprint/4961

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