Comparative Evaluation of Soil Erosion and Sediment Yield Estimation in the Dowlatabad Watershed Using the EPM, MPSIAC, and IntErO Models

Document Type : Original Article

Authors

1 Kurdistan Agricultural and Natural Resources Research and Education Center, Kurdistan, Iran

2 Ph.D., Department of Watershed Management, Faculty of Natural Resources, Sari Agricultural Sciences and Natural Resources University, Sari, Iran

3 Faculty of Zanjan Agriculture and Natural Resources Research and Education Center, Zanjan

4 Associate Professor Department of Watershed Management, Faculty of Natural Resources, Sari Agricultural Sciences and Natural Resources University, Sari, Iran

5 Associate Professor, Department of Watershed Management, Faculty of Natural Resources, Tarbiat Modares University, Noor, Iran

‎10.22052/deej.2025.256485.1099

Abstract

Introduction: Soil is a vital, non-renewable resource fundamental to ecosystem stability and human subsistence. However, this resource is under constant threat from soil erosion—the detachment, transport, and deposition of earth materials by water or wind. This process degrades agricultural productivity by stripping fertile topsoil, impairs water resources through reservoir siltation and pollutant transport, and inflicts significant socio-economic costs. Given its status as a critical global environmental challenge, the accurate quantification of soil erosion is a prerequisite for effective watershed management and conservation planning. Empirical models offer a practical and efficient methodology for such assessments. This study therefore aims to conduct a comparative evaluation of three prominent empirical models—the EPM, MPSIAC, and IntErO for estimating soil erosion and sediment yield within the Dowlatabad Dehgalan watershed.
 
Materials and Methods: This study employed three empirical modelsof -the MPSIAC, EPM, and IntErO- to estimate soil erosion and sediment yield in the Dowlatabad watershed. The Modified Pacific Southwest Inter-Agency Committee (MPSIAC) model was applied by systematically quantifying its nine governing factors, which include surface geology, soil, climate, runoff, land cover, and land use. The overall erosion and sedimentation status of the watershed was subsequently determined based on the cumulative scores assigned to these parameters. The Erosion Potential Method (EPM) was then implemented. This required the evaluation of four key coefficients across distinct land units: the coefficient of watershed erosion and rock permeability (Ψ), the land use coefficient (Xa), the average land slope (I), and the soil and rock erodibility coefficient (Y). These coefficients were integrated to calculate the erosion intensity and resultant sediment yield. Finally, the IntErO model was utilized. As a comprehensive algorithm for predicting erosion and sediment transport, it integrates topographic, soil, geological, and land use data, supplemented by meteorological inputs such as precipitation and temperature, to simulate erosion processes and quantify potential sediment yield.
 
Results and Discussion: The evaluation of the governing parameters within the MPSIAC model revealed that land use (Factor Y7, score: 12.04) exerted the most substantial influence on soil erosion within the watershed. In contrast, surface runoff (Factor Y4, score: 1.78) was identified as the least significant factor. A comparative analysis of the three models yielded distinct estimates for the average annual soil erosion rate. The MPSIAC model estimated a rate of 9.034 t ha-1 yr-1, while the IntErO model produced a closely aligned estimate of 10.01  t ha-1 yr-1. The EPM model, which reports its output in volumetric terms, estimated 1068 m³ km-2 yr-1 (equivalent to approximately 10.68  t ha-1 yr-1, assuming a standard bulk density). Despite these differing erosion rates, the models demonstrated a notable convergence in their predictions of specific sediment yield, with values of 2.74, 2.88, and 2.65  t ha-1 yr-1 for the MPSIAC, IntErO, and EPM models, respectively.
A key point of divergence among the models was the calculated Sediment Delivery Ratio (SDR). The EPM model predicted a substantially higher SDR of 0.420, indicating a more efficient transport of eroded material from its source to the watershed outlet. Conversely, the MPSIAC and IntErO models yielded lower and more conservative SDR estimates of 0.247 and 0.210, respectively. This discrepancy highlights the differing theoretical approaches and structural assumptions of each model regarding sediment transport and deposition processes within the catchment.
 
Conclusion and Suggestions: Accurately quantifying soil erosion and sediment yield through direct measurement is often prohibitive due to technical, environmental, and economic constraints. Consequently, empirical models are indispensable tools, though their application necessitates a robust understanding of erosional processes and a critical evaluation of their accuracy under specific regional conditions. This study applied the EPM, MPSIAC, and IntErO models to the Dowlatabad watershed, revealing a significant methodological divergence in assessing erosion severity. While both the MPSIAC and IntErO models classified the region within a moderate erosion class, the EPM model indicated a severe erosion class. This discrepancy underscores the inherent uncertainty in model selection and the urgent need for proactive watershed management and soil conservation measures in the area. Regarding sediment yield, the estimated values were 451.026, 144.179, and 142.59 m³ km-2 yr-1 for the EPM, MPSIAC, and IntErO models, respectively. The close agreement between the MPSIAC and IntErO models suggests a higher degree of reliability for these estimates in this context. In contrast, the EPM model's substantially higher sediment yield and previously noted high sediment delivery ratio (SDR) point to its more extreme and less conservative predictive behavior. Based on these findings, the following recommendations are proposed: Promote the Application of the IntErO Model: Given its performance, which closely aligns with the established MPSIAC model, the IntErO algorithm is recommended for further application and validation in watersheds of varying scales across Iran. This would help broaden the national toolkit for rapid and reliable erosion assessment. Prioritize Conservation Planning: The consensus among the models that the area falls within at least a moderate erosion class necessitates the immediate planning and execution of integrated soil and water conservation programs to mitigate ongoing land degradation. Implement Ground-Truthing Studies: To calibrate and validate these empirical models for local conditions, future work must correlate model outputs with direct sediment measurement techniques, such as systematic field monitoring and sediment sampling.

Keywords

Main Subjects

References

  1. Agbo, K.C. (2021). Integrated assessment of soil erosion processes and policy in Oguta watershed Imo state southes, Nigeria. School of Civil Engineering and Geosciences. Newcastle University, UK.
  2. Baggaley, N., & Potts, J. (2017). Sensitivity of the PESERA soil erosion model to terrain and soil inputs. Geoderma Regional, no. 11, 104-112.
  3. Bayat, R., Arab Khedri, M., Behnam, N., & Grami, Z. (2019). Investigating the effectiveness of EPM and MPSIAC models in determining the erosion status of Shahriari watershed. Spatial analysis of environmental hazards, 7(3), 1-16. (In persian)
  4. Cebecauer, T., & Hofierka, J. (2008). The consequences of land-cover changes on soil erosion distribution in Slovakia. Geomorphology, 98(3–4), 187-198.
  5. Detailed watershed management studies of Dowlatabad watershed, Dehgolan, geology, hydrology, meteorology, vegetation, soil science. 1392. (In Persian).
  6. Didone, E.J., Minella, J.G., & Evrard, O. (2017). Measuring and modelling soil erosion and sediment yields in a large cultivated catchment under no-till of Southern Brazil. Soil and Tillage Research, 174, 24-33.
  7. El Mouatassime, S., Boukdir, A., Karaoui, I., Skataric, G., Nacka, M., Khaledi Darvishan, A., & Spalevic, V. (2019). Modelling of soil erosion processes and runoff for sustainable watershed management: Case study Oued el Abid Watershed, Morocco. Poljoprivreda i Sumarstvo, 65(4), 241-250.
  8. Esmaliouri, A., Ahmadi, H., & Tahmourth, M. (2014). Quantitative evaluation of the intensity of water erosion using a regional model for estimation of erosion and sediment production (Nir watershed, Ardabil). Pasture and Watershed management, Journal of Natural Resources of Iran, 67(3), 407-417. (In Persian)
  9. Ghazaei, R., Wali, A., Maqami, Y., Abdi, Zh., & shrafi, S. (2013). Comparison of EPM, MPSIAC and PSIAC models in estimating erosion and sedimentation using GIS. Geography and Development, 27, 117-126. (In Persian)
  10. Ghorghi, H., & Osati, Kh. (2016). Evaluation of runoff and sediment values from natural rainstorms "Case study: field plots of Khamsan Jovi representative basin". The second national hydrology conference of Iran. on July 20 and 21. (In Persian)
  11. Ghahari, Sh., & Hosseini, M. (2019). Technical report on EPM model efficiency evaluation through sediment measurement of small dam reservoirs. scientific-research journal of watershed engineering and management, 12(4), 1133-1145. (In Persian)
  12. Hazbavi, Z., Azizi, E., Sharifi, Z., Alaei, N., Mostafazadeh, R., Behzadfar, M., & Spalevic, V. (2020). Comprehensive estimation of erosion and sediment components using IntErO model in the KoozehTopraghi Watershed, Ardabil Province. Environmental Erosion Research Journal, 10(1), 92-110.

13. Hessel, R., Jetten, V. G., Liu, B., & Qiu, Y. (2011). Evaluating effects of soil and water management and land use change on the Loess Plateau of China using LISEM, Handbook of Erosion Modelling.

  1. Johnson, CW., & Gebhart, K. A. (1982). MPSIAC Predicting sediment yields from sagebrush rangelands. In: ARS (Ed.), Estimating Soil Erosion and Sediment Yield on Rangelands, 26(13), 145-156.
  2. Kazemi, M., Zabihi Silabi, M., Jafarpour, A., Qaramahmoodli, S., & Mohammadi, F. (2022). Estimation of erosion and sedimentation components using IntErO model (Case study: Kohoristan watershed). Environmental Erosion Research, 13: 2(50), 177-191. (In Persian).
  3. Kerr, J., & Chung, K. (2006). Environment and Production Technology Division International Food Policy, Research Institute, Washington, D. C. 65PP.
  4. Khaledi Darvishan, A., Derikvandi, M., Aliramaee, R., Khorsand, M., Spalevic, V., Gholami, L., & Vujacic, D. (2018). Efficiency of IntErO Model to Predict Soil Erosion Intensity and Sediment Yield in Khamsan Representative Watershed (West of Iran). Agrofor, 3(2), 22-31.
  5. Khaledi Darvishan, A., Mohammadi, M., Skataric, G., Popović, S. G., Behzadfar, M., Sakuno, N.R. R., & Spalevic V. (2019). Yield and maximum outflow, using IntErO model (Case study: S8-intA Shirindarreh Watershed, Iran). Agriculture & Forestry, 65(4), 203-210.
  6. Lawler, D. M., West, J. R., Couperthwaite, J. S., & Mitchell, S. B. (2001). Application of a novel automatic erosion and deposition monitoring system at a channel bank site on the Tidal River Trent, U.K. Estuarine. Coastal and Shelf Science, 53(2), 237-247.
  7. Lawler, DM. (2008). Advances in the continuous monitoring of erosion and deposition dynamics: Developments and applications of the new PEEP-3T system. Geomorphology, 93(1–2), 17-39.
  8. Mano, V., Nemery, J., Belleudy, P., & Poirel, A. (2009). Assessment of suspended sediment transport in four alpine watersheds. Hydrological Processes, 23(5), 777–792.

22. Martinez- Carreras, N., Soler, M., Hernandez, E., & Gallaret, F. (2007). Simulating badland erosion with KINEROS2 in a small Mediterranean mountain basin (Vallcebre, Eastern Pyrenees). Catena, 17(1), 145-154.

  1. Marttila, H., & Klove, B. (2010). Dynamic of erosion and suspended sediment transport from drained peatland forestry. Journal of Hydrology, 388(3–4): 414-425.
  2. Mohammadi, M., Khaledi Darvishan, A. V., Spalevic, V., Dudic, B., & Billi, P. (2021). Analysis of the Impact of Land Use Changes on Soil Erosion Intensity and Sediment Yield Using the IntErO Model in the Talar Watershed of Iran. Water, no. 13, 881.

25. Morgan, R.P.C. (2009). Modified MMF (Morgan–Morgan–Finney) model for evaluating effects of crops and vegetation cover on soil erosion. Earth Surface Process and Landforms, 33(1), 90-106.

  1. Moradi, M., Ghanchepour, D., Nohegar, A., Mehmohudinejad, W. (2010). Comparison of EPM and MPSIAC models in estimation of erosion and sedimentation in Pourahmadi watershed (Hormozgan province). Environmental erosion research, 1(4), 53-67. (In Persian)

27.. Ouallali, A., Spalevic, V., Aassoumi, H., Moukhchane, M., & Berradm, F. (2016). The assessment of the soil erosion intensity and runoff in the river basin of Arbaa Ayacha, Western Rif. Morocco. International Journal of Scientific and Research Publication.

  1. PSIAC. (1968). Report of the Water Management Subcommittee on Factors Affecting Sediment Yield in the Pacific Southwest Area and Selection and Evaluation of Measures for Reduction of Erosion and Sediment Yield. American Society of Civil Engineers, 98, Report No. HY12.
  2. Quinton, J.N., Krueger, T., Freer, J., Brazier, R.E., & Bilotta, G.S. (2011). A case study of uncertainty: Applying GLUE to EUROSEM, pp. 80-97.
  3. Refahi, H. (2003). Water erosion and its control, Tehran University Press. 4th edition. (In Persian)
  4. Rodrigues Neto, M. R., Musselli, D. G., Lense, G. H. E., Servidoni, L. E., Stefanidis, S., Spalevic, V., & Mincato, R. L. (2022). Soil loss modelling by the IntErO model-erosion potential method in the Machado River Basin, Minas Gerais, Brezil. Agriculture & Forestry/ Poljoprivredai Sumarstvo, 68(2).
  5. Sadeghi, S.H.R., Azari, M., & GhaderiVangah, B. (2008). Field evaluation of the hill slope Erosion Model (HEM) in Iran. Biosystems Engineering, no. 99, 304–311.
  6. Shen, Z.Y., Gong, Y.W., Li, Y.H., Hong, Q., Xu, L., & Liu, R.M. (2009). A comparison of WEPP and SWAT for modeling soil erosion of the Zhangjiachong Watershed in the Three Gorges Reservoir Area. Agricultural Water Management, no. 96, 1435–1442.
  7. Sinha, N., Debasis, D., Khanindra, P., & Paramanik, R. (2018). Modified E30 Model for assessing soil erosion potential in a highly precipitated hilly watershed of North-East India: Remote Sensing Applications. Society and Environment. No. 10, 173-182.
  8. Spalevic, V. (2012). Impact of land use on runoff and soil erosion in Polimlje. Doctoral thesis, Faculty of Agriculture of the University of Belgrade, Serbia, 1-260.36. Tangestani, M.H. (2006). Comparison of EPM and PSIAC models in GIS for erosion and sediment yield assessment in a semi-arid environment: Afzar catchment, Fars proviance, Iran. Journal of Asian Earth Sciences, 27(5): 585-597. (In Persian)
  9. Tavares, A.S., Uagoda, R.E.S., Spalevic, V., & Mincato, R.L. (2021): Analysis of the erosion potential and sediment yield using the IntErO model in an experimental watershed dominated by karst in Brazil. Agriculture and Forestry, 67(2), 153-162.
  10. Walling, D. E. (2005). Tracing suspended sediment sources in catchments and river system. Science of the Total Environment, 344(1–3), 159–184.
  11. Yang, D., Kanae, S., Oki, T., Koike, T., & Musiake, K. (2003). Globale potential soil erosion with reference to land use and climate change, Hydrological process, 17(14), 2913-2928.
  12. 40. Zabihi Silabi, M., & Khalidi Darvishan A. (2021). Qualitative evaluation of IntErO, EPM, MPSIAC and RUSLE models in order to select the optimal models for different conditions in the description of watershed executive detailed studies services. Promotion and development of watershed management, 9(32), 52-66. (In Persian)
  13. Zapata, F. (2003). The use of environmental radionuclides as tracers in soil erosion and sedimentation investigations: recent advances and future developments. Soil Tillage and Research, 69, 3-13.
Desert Ecosystem Engineering
Volume 14, Issue 48
October 2025
Pages 25-40

Files

Share

How to cite

Statistics