Journal of Research and Rural Planning

Journal of Research and Rural Planning

Integrated Assessment of Soil Degradation Sensitivity and Dust Storm Susceptibility in Al-Anbar Governorate, Iraq, Using Geospatial Techniques and Environmental Indices

Document Type : Original Article

Authors
1 Isf.C., Islamic Azad University, Isfahan, Iran
2 University of Technology , Iraq.
Abstract
Purpose- Land degradation and severe dust storm dynamics represent major environmental threats in arid and semi-arid zones across the Middle East. Previous spatial modeling efforts often conflated baseline soil detachment with atmospheric wind erosion mechanisms. In this study, we present a re-framed conceptual framework that explicitly decouples baseline water-induced topsoil degradation sensitivity, quantified using the Revised Universal Soil Loss Equation (RUSLE) from wind-driven dust storm susceptibility modeled via ERA5-Land wind velocity vectors, Aerosol Optical Depth (AOD), and Normalized Difference Dust Index (NDDI).
Design/methodology/approach- The entire analysis was systematically executed across the Al-Anbar Governorate, Iraq (total area ≈ 138,500 km²). Input datasets including ASTER Global Digital Elevation (GDEM) with a 30m resolution, Landsat 8/9 OLI, MODIS AOD, and ERA5-Land climate reanalysis were resampled to a standardized 30 m spatial resolution grid. Model validation was conducted using empirical soil loss observations from seven (7) field soil sampling profiles across diverse geomorphic units, yielding robust goodness-of-fit statistics (R² = 0.78, RMSE = 1.12 t/ha/year, MAE = 0.89 t/ha/year).
Finding- The standardized results indicate a governorate-wide mean soil erosion rate of 5.33 t/ha/year. High to very high erosion susceptibility zones (covering 28.4% of the governorate) are concentrated along degraded plateau margins and unmanaged alluvial terraces. Furthermore, eco-hydrological analysis validates while lower annual precipitation reduces RUSLE water erosivity (R-factor), it severely exacerbates topsoil desiccation and degrades vegetation cover (elevating C-factor to 0.60 in bare lands), thus fueling wind-driven dust emissions. A dedicated geoconservation assessment emphasizes that severe soil degradation hotspots directly threaten agricultural oases (Al-Bouhyat), historical archaeological sites, and fragile riverine soil banks along the Euphrates River.
Keywords
Subjects

1. Abdelsamie, E. A., Abdellatif, M. A., Hassan, F. O., El Baroudy, A. A., Mohamed, E. S., Kucher, D. E., & Shokr, M. S. (2022). Integration of RUSLE model, remote sensing and GIS techniques for assessing soil erosion hazards in arid zones. Agriculture, 13(1), 35. https://doi.org/10.3390/agriculture13010035
2.   Abdullahi, S., Mahmud, A. R. b., & Pradhan, B. (2014). Spatial modelling of site suitability assessment for hospitals using geographical information system-based multicriteria approach at Qazvin city, Iran. Geocarto International, 29(2), 164-184. https://doi.org/10.1080/10106049.2012.752531
3. Abdullahi, S., Pradhan, B., & Jebur, M. N. (2015). GIS-based sustainable city compactness assessment using integration of MCDM, Bayes theorem and RADAR technology. Geocarto International, 30(4), 365-387. https://doi.org/10.1016/j.catena.2023.107130
4. Abuzaid, A. S., El-Shirbeny, M. A., & Fadl, M. E. (2023). A new attempt for modeling erosion risks using remote sensing-based mapping and the index of land susceptibility to wind erosion. Catena, 227, 107130. https://doi.org/10.3390/app12157776
5. Allafta, H., & Opp, C. (2022). Soil erosion assessment using the RUSLE model, remote sensing, and GIS in the Shatt Al-Arab basin (Iraq-Iran). Applied Sciences, 12(15), 7776. https://doi.org/10.1080/10106049.2022.2105407
6. Aswathi, J., Sajinkumar, K., Rajaneesh, A., Oommen, T., Bouali, E., Binoj Kumar, R., . . . Ajin, R. (2022). Furthering the precision of RUSLE soil erosion with PSInSAR data: An innovative model. Geocarto International, 37(27), 16108-16131. https://doi.org/10.1007/s10668-019-00578-z
7. Awadh, S. M., Al-Mimar, H., & Yaseen, Z. M. (2021). Groundwater availability and water demand sustainability over the upper mega aquifers of Arabian Peninsula and west region of Iraq. Environment, Development and Sustainability, 23(1), 1-21. https://doi.org/10.1007/s12594-023-2324-y
8. Behera, D. K., Jamal, S., Ahmad, W. S., Taqi, M., & Kumar, R. (2023). Estimation of soil erosion using RUSLE Model and GIS tools: A study of chilika lake, Odisha. Journal of the Geological Society of India, 99(3), 406-414. https://doi.org/10.1007/s12594-023-2324-y
9. Bolouk Heidari, F., & Arfania, R. (2022). Wildfire susceptibility mapping using NBR index and frequency ratio model. Geoconservation Research, 5(1), 240-260. https://doi.org/10.30486/gcr.2022.1961153.1107
10. Crisp, J., Ellison, J., & Fischer, A. (2022). Digital coalescence and consolidated geoconservation outcomes: a case study using ArcGIS mobile applications at Tasmanian coastal geoconservation sites. Geoconservation Research, 5(1), 1-28. https://doi.org/10.30486/gcr.2021.1920096.1079
11. Das, S., Manoj Kumar Jain, and Vivek Gupta. (2022). A step towards mapping rainfall erosivity for India using high-resolution GPM satellite rainfall products. Catena 212, 106067. https://doi.org/10.1016/j.catena.2022.106067
12. Fattah, M. Y., Joni, H. H., & Abood, A. S. (2020). Erosion of dune sands stabilised by grouting with lime–silica fume mix. Proceedings of the Institution of Civil Engineers-Ground Improvement, 173(1), 3-18. https://doi.org/10.1680/jgrim.17.00026
13. Fattah, M. Y., Joni, H. H., & Al-Dulaimy, A. S. (2018). Strength characteristics of dune sand stabilized with lime-silica fume mix. International Journal of Pavement Engineering, 19(10), 874-882. https://doi.org/10.1080/10298436.2016.1215687  
14. Favis‐Mortlock, D. T., Boardman, J., Parsons, A. J., & Lascelles, B. (2000). Emergence and erosion: a model for rill initiation and development. Hydrological Processes, 14(11‐12), 2173-2205. https://doi.org/10.1002/1099-1085
15. Ghosal, K., & Das Bhattacharya, S. (2020). A review of RUSLE model. Journal of the Indian Society of Remote Sensing, 48, 689-707. https://doi.org/10.1007/s12524-019-01097-0
16. Gyeltshen, S., Adhikari, R., Bahadur Budha, P., Thapa, G., Kumar Subedi, K., & Kumar Singh, B. (2022). Remote sensing and GIS based soil loss estimation for Bhutan, using RUSLE model. Geocarto International, 37(21), 6331-6350. https://doi.org/10.1080/10106049.2021.1936210
17. Hussien, B. M., Rabeea, M. A., & Mukhlif, H. N. (2020). Estimation of corrosion and encrustation from groundwater chemistry of the aquifers: a case study of Al Hammad zone. Environmental nanotechnology, monitoring & management, 14, 100334. https://www.sciencedirect.com/science/article/abs/pii/S2215153220300416
18. Jackson, N. L., & Nordstrom, K. F. (2011). Aeolian sediment transport and landforms in managed coastal systems: a review. Aeolian research, 3(2), 181-196. https://www.sciencedirect.com/science/article/abs/pii/S1875963711000309
19. Katra, I. (2020). Soil erosion by wind and dust emission in semi-arid soils due to agricultural activities. Agronomy, 10(1), 89. https://doi.org/10.3390/agronomy10010089
20. Lazzari, M., et al. (2015). Sediment yield and erosion rate estimation in the mountain catchments of the Camastra artificial reservoir (Southern Italy): A comparison between different empirical methods. Catena 127, 323-339. https://www.sciencedirect.com/science/article/abs/pii/S0341816215000028  
21. Long, S., Gao, J., Shao, H., Wang, L., Zhang, X., & Gao, Z. (2024). Developing SWAT‐S to strengthen the soil erosion forecasting performance of the SWAT model. Land Degradation & Development, 35(1), 280-295. https://doi.org/10.1002/ldr.4915 
22. Luvai, A., John Obiero, and Christian Omuto. (2022). Soil loss assessment using the revised universal soil loss equation (RUSLE) model. Applied and Environmental Soil Science 2022, 2022(1), 2122554. doi: https://doi.org/10.1155/2022/2122554   
23. Ma, X., and Mingguo Zheng. (2022). Statistical evaluation of proxies for estimating the rainfall erosivity factor. Scientific Reports 12.1, 12092. https://doi.org/10.1038/s41598-022-15271-x
24. Madhukar, A., et al. (2023). Spatial Estimation of Soil Erosion Using RUSLE Model: A Case Study of Sangareddy Telangna State, India. International Journal of Plant & Soil Science 35.18 490-498. https://doi.org/10.9734/ijpss/2023/v35i183314
25. Matviichuk, B., Ludmila Romantschuk, and Nataliia Matviichuk. (2023). Rainfall Erosivity Factor within the Volyn Region. Ecological Engineering & Environmental Technology (EEET) 24.6. https://doi.org/10.12912/27197050/168091
26. Mehri, A., Karimi, H., Mustafa, Y. T., Al-Quraishi, A. M. F., & Galalizadeh, S. (2024). Predicting Soil Erosion Using RUSLE Model in Duhok Governorate, Kurdistan Region of Iraq Natural Resources Deterioration in MENA Region: Land Degradation, Soil Erosion, and Desertification (pp. 171-187): Springer , Cham. https://doi.org/10.1007/978-3-031-58315-5_9
27. Merchán, L., Martínez-Graña, A. M., Alonso Rojo, P., & Criado, M. (2023). Water Erosion Risk Analysis in the Arribes del Duero Natural Park (Spain) Using RUSLE and GIS Techniques. Sustainability, 15(2), 1627. https://doi.org/10.3390/su15021627
28. Milentijević, N., et al. (2021). Assessment of Soil Erosion Rates Using Revised Universal Soil Loss Equation (RUSLE) and GIS in Bačka (Serbia). Polish Journal of Environmental Studies, 30.36. https://doi.org/10.15244/pjoes/135617  
29. Mokarram, M., & Zarei, A. R. (2023). Soil erosion prediction using Markov and CA-Markov chains methods and remote sensing drought indicators. Ecological Informatics, 78, 102386. https://doi.org/10.1016/j.ecoinf.2023.102386
30. Moore, I. D., & Burch, G. J. (1986). Physical Basis of the Length Slope Factor in the Universal Soil Loss Equation. Soil Sci. Soc. Am. J. https://doi.org/10.2136/sssaj1986.03615995005000050042x
31. Mudau, M., & Kori, E. (2022). Assessment of the geomorphological diversity of Komati Gorge, Mpumalanga province, South Africa. Geoconservation Research, 5(1), 195-208. https://doi.org/10.30486/GCR.2022.1955504.1103
32. Özşahin, E. (2023). Climate change effect on soil erosion using different erosion models: A case study in the Naip Dam basin, Türkiye. Computers and Electronics in Agriculture, 207, 107711. https://doi.org/10.1016/j.compag.2023.107711
33. Pangali Sharma, T. P., Zhang, J., Khanal, N. R., Prodhan, F. A., Nanzad, L., Zhang, D., & Nepal, P. A. (2021). Geomorphic Approach for Identifying Flash Flood Potential Areas in the East Rapti River Basin of Nepal. Int. J. Geo-In, 10, 247. https://doi.org/10.3390/ijgi10040247
34. Rajbanshi, J., & Bhattacharya, S. (2022). Modelling the impact of climate change on soil erosion and sediment yield: a case study in a sub-tropical catchment, India. Modeling Earth Systems and Environment, 8(1), 689-711. https://doi.org/10.1007/s40808-021-01117
35. Renard, K. (1997). Predicting soil erosion by water: A guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE). United States Government Printing. https://www.tucson.ars.ag.gov/unit/publications/PDFfiles/717.pdf
36. Renard, K. G., Yoder, D. C., et al. (1997). Predicting soil erosion by water: A guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE). USDA Agriculture Handbook No. 703. https://www.tucson.ars.ag.gov/unit/publications/PDFfiles/717.pdf
37. Scholand, D., and Britta Schmalz. (2021). Deriving the main cultivation direction from open remote sensing data to determine the support practice measure contouring. Land 10.11, 1279. https://doi.org/10.3390/land10111279
38. Selmy, S. A., Abd Al-Aziz, S. H., Jiménez-Ballesta, R., García-Navarro, F. J., & Fadl, M. E. (2021). Modeling and assessing potential soil erosion hazards using USLE and wind erosion models in integration with gis techniques. Dakhla oasis, Egypt. Agriculture, 11(11), 1124. https://doi.org/10.3390/agriculture11111124
39. Senanayake, S., Pradhan, B., Alamri, A., & Park, H.-J. (2022). A new application of deep neural network (LSTM) and RUSLE models in soil erosion prediction. Science of the Total Environment, 845, 157220. https://doi.org/10.1016/j.scitotenv.2022.157220
40. Serbaji, M. M., Moncef Bouaziz, and Okba Weslati. (2023). . "Soil Water erosion modeling in Tunisia using RUSLE and GIS integrated approaches and Geospatial Data. Land 12.3, 548. https://doi.org/10.3390/land12030548
41. Shihab, T. H., & Al-Hameedawi, A. N. (2020). Desertification hazard zonation in central Iraq using multi-criteria evaluation and GIS. Journal of the Indian Society of Remote Sensing, 48(3), 397-409. https://doi.org/10.1007/s12524-019-01079-2
42. Sidi Almouctar, M. A., Wu, Y., Zhao, F., & Dossou, J. F. (2021). Soil erosion assessment using the RUSLE model and geospatial techniques (Remote Sensing and GIS) in South-Central Niger (Maradi Region). Water, 13(24), 3511. https://doi.org/10.3390/w13243511
43. Tao, T., Hao Wang, and Kaiyong Zhao. (2021). Efficient simulation of fully non-stationary random wind field based on reduced 2D hermite interpolation. Mechanical Systems and Signal Processing 150 107265. https://doi.org/10.1016/j.ymssp.2020.107265
44. Tarek, Z., Elshewey, A. M., Shohieb, S. M., Elhady, A. M., El-Attar, N. E., Elseuofi, S., & Shams, M. Y. (2023). Soil erosion status prediction using a novel random forest model optimized by random search method. Sustainability, 15(9), 7114. https://doi.org/10.3390/su15097114
45. Teixeira, D. B. d. S. (2023). Rainfall erosivity in Brazil. doi: https://locus.ufv.br//handle/123456789/31039. 
46. Thapa, S., Shrestha, A., Lamichhane, S., Adhikari, R., & Gautam, D. (2020). Catchment-scale flood hazard mapping and flood vulnerability analysis of residential buildings: The case of Khando River in eastern Nepal. Journal of Hydrology: Regional Studies 30, 100704. https://doi.org/10.1016/j.ejrh.2020.100704
47. Tran, T. K. M., and Jen-Der Day. (2022). Set Up 5P Model to Figure out the Different Factors of Innovation between Employees from Developing and Developed Countries. International Journal of Research in Vocational Studies (IJRVOCAS) 2.3, 16-23. https://doi.org/10.53893/ijrvocas.v2i3.138
48. Williams, J. R. (1995). The EPIC model. In Computer models of watershed hydrology (pp. 909-1000). Water Resources Publications.
49. Wischmeier, W. H., and Dwight David Smith. (1978). Predicting rainfall erosion losses: a guide to conservation planning. No. 537. Department of Agriculture, Science and Education Administration. Department of Agriculture, Science and Education Administration.
50. Yang, G., Sun, R., Jing, Y., Xiong, M., Li, J., & Chen, L. (2022). Global assessment of wind erosion based on a spatially distributed RWEQ model. Progress in Physical Geography. Earth and Environment, 46(1), 28-42. https://doi.org/10.1177/03091333211030608
51. Yudhistira, A., C. Setyawan, and R. Tirtalistyani. (2021). DEM and land slope-based method of RUSLE LS factor calculation for soil erosion assessment. IOP Conference Series: Earth and Environmental Science. Vol. 686. No. 1. IOP Publishing, 2021. https://doi.org/10.1088/1755-1315/686/1/012033
52. Zuazo, V. H. D., & Pleguezuelo, C. R. R. (2009). Soil-erosion and runoff prevention by plant covers: a review. Sustainable agriculture, 785-811.  https://doi.org/10.1051/agro%3A2007062
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  • Receive Date 13 July 2026
  • Revise Date 04 September 2026
  • Accept Date 11 October 2026