Performance Evaluation of Infiltration Equations with an Emphasis on the Effect of Reclamation Measures in Paired Watersheds of Gonbad

Subject Areas : Watershed management and water extraction

Saeedreza Moazeni Noghondar ¹ , Ali Salajegheh ^{2 *} , Shahram Khalighi Sigaroudi ³ , Ali Golkarian ⁴

1 - Ph.D. student, Department of reclamation of Arid and Mountainous Region, Faculty of Natural Resources, University of Tehran, Iran.
2 - Professor of the Department of reclamation of Arid and Mountainous Region, Faculty of Natural Resources, University of Tehran, Iran.
3 - Associate Professor, Department of reclamation of Arid and Mountainous Region, Faculty of Natural Resources, University of Tehran, Iran.
4 - Associate Professor, Department of Range and Watershed Management, Faculty of Natural Resources and Environment, Ferdowsi University, Mashhad, Iran.

Received: 2025-01-19 Accepted : 2025-09-15 Published : 2025-11-10

Keywords: Cumulative infiltration, Instantaneous infiltration rate, Infiltration models, Water resource management, Paired watersheds,

Abstract :

Background and Objectives: Modeling cumulative infiltration and instantaneous infiltration rate of water into the soil is a critical tool for water resource management and watershed project planning, especially in arid and semi-arid regions. These regions face significant challenges such as limited water resources and soil erosion, which hinder sustainable natural resource management. This study aims to evaluate the performance of Horton, Kostiakov, Kostiakov-Lewis, Philip, and SCS infiltration models in simulating cumulative infiltration and instantaneous infiltration rate in two paired watersheds (restored and control) in the Gonbad area of Hamedan Province, Iran.

Methods: Experimental data were collected using the double-ring infiltrometer method from the northern and southern slopes of the restored and control watersheds. Sampling was conducted more than 10 days after the last rainfall to eliminate the effect of antecedent soil moisture. Statistical criteria, including the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE), were employed to evaluate model performance. Simulations were carried out for a 120-minute timeframe, and the obtained data were compared with infiltration models for each slope.

Results: The results indicated that Horton and SCS models outperformed other models due to their flexibility and high accuracy in matching observed data. The highest cumulative infiltration was recorded in the northern slope of the restored watershed, with an observed value of 108.55 mm. The Horton (108.73 mm) and SCS (107.58 mm) models demonstrated excellent agreement with observed data. In the southern slope of the restored watershed, cumulative infiltration was observed to be 65.33 mm, with Horton and SCS models showing the closest predictions. For the northern slope of the control watershed, cumulative infiltration was observed at 50.66 mm, with Horton (50.97 mm) and SCS (52.35 mm) models providing the most accurate estimates. The lowest cumulative infiltration was recorded on the southern slope of the control watershed (33.11 mm).

Analysis of instantaneous infiltration rates yielded similar results. In the northern slope of the restored watershed, the infiltration rate decreased from 293.33 mm/h in the first minute to 35.33 mm/h in the 120th minute. Horton and SCS models showed the best performance in this slope. Southern slopes of both watersheds exhibited lower instantaneous infiltration rates compared to northern slopes, attributed to differences in microclimatic conditions and soil moisture retention capacity.

Conclusion: This study demonstrated that restoration efforts, such as improving vegetation cover and managing grazing, significantly enhance soil infiltration capacity. The restored watershed, particularly its northern slope, exhibited higher cumulative infiltration and instantaneous infiltration rates compared to the control watershed. Furthermore, Horton and SCS models were identified as the most suitable models for these conditions due to their accuracy and flexibility in simulating infiltration processes. Future studies are recommended to investigate the effects of seasonal variations, soil physical and chemical properties, and climatic influences on infiltration model performance.

References:

Alizadeh, A. (2014). Water, Soil, and Plant Relationship. Ferdowsi University Press, 722 pages. [In Persia].
ASTM (2003) D3385–03 standard test method for infiltration rate of soils in field using double-ring infiltrometer. Annual Book of ASTM Standards 04.08. ASTM, West Conshohocken, Penn
Barani, GH. A. Khanjani, M.J. Skafi, M. (2000). Recalibration of infiltration equation US Soil Protection Organization by least squares method. Agriculture, 2(1): 43-50. [In Persia].
Barkhordari, J. Nikkami, D. Samadi, M. Tabatabaeizadeh, M. (2015). Suitability of different rainfall erosivity indices in cold, dry climate of central Iran, a case study in Nir rangeland research station–Yazd Province Watershed Engineering and Management, 7(2), 136-144. [In Persia]
Bouwer, H. (1986). Intake rate: cylinder infiltrometer. Methods of Soil Analysis: Part 1 Physical and Mineralogical Methods, 5, 825-844.
Cahoon, Joel. "Kostiakov infiltration parameters from kinematic wave model." Journal of irrigation and drainage engineering 124.2 (1998): 127-130.
Fakhernikche, A., Vafakhah, M., & Sadeghi, S.H. (2014). Evaluation of the performance of various cumulative infiltration models in different land use types and soil textures using a rain simulator. Soil and Water Science Journal, 24(1), 183-193. [In Persia].
Fazelpour Aghdaei, M., Malekinejad, H., Ekhtesasi, M., & Barkhordari, J. (2016). Efficiency of infiltration equations and analysis of surface permeability in flood spreading areas of Yazd province. Watershed Engineering and Management Journal, 8(3), 264-274. [In Persia].
Furman, A., Warrick, A. W., Zerihun, D., & Sanchez, C. A. (2006). Modified Kostiakov infiltration function: Accounting for initial and boundary conditions. Journal of irrigation and drainage engineering, 132(6), 587-596.
Goodarzi, L. Akhundali, A.M. and Zarei, H. (2012). Evaluation and determination of coefficients of water infiltration models in the Ashtarinan Plain. Soil and Water Resources Conservation, 1(3), 39-45. [In Persia]
Hoyos, E. M., & Cavalcante, A. L. B. (2015). Sensitivity analysis of one-dimensional infiltration models. EJGA, 20, 4313-4324.
Javadi, A. Zamani, N. (2016). Sensitivity analysis of coefficients of different infiltration equations to changes in soil moisture and water load. National Congress of Irrigation and Drainage of Iran. SID. https://sid.ir/paper/872336/fa. [In Persia]
Kavousi, M. Vatankhah, M. Vafakhah, M. (2013). Evaluation of Some Infiltration Models for Different Land Uses in Kojour Watershed. Journal of Irrigation and Water Engineering. 4(1), 1-13. [In Persia]
Mohammadi, M. H., & Refahi, R. (2004). Estimation of infiltration equation parameters by soil physical characteristic. Iranian Journal of Agricultural Sciences, 36(6), 1391-1398 [In Persia]
Marqasi, M., Hashemi, S. R., Siuki, A. K., Shahidi, A., & Neyshabouri, S. Z. (2023). Simulation model of water infiltration in soil using combination technique. Ain Shams Engineering Journal, 14(11), 102550.
Mousavi Dehmurdi, M., Ghorbani Dashtaki, Sh., & Mashayekhi, P. (2019). Comparison of the performance of some water infiltration models into soil based on data obtained from double-ring infiltrometers and HYDRUS-1D software. Applied Soil Research Journal, 7(2), 182-195. [In Persia]
Poozen, M. T., Chari, M. M. Afrasiab, P. (2020). Evaluation Different Methods of Scaling the Philip Equation . Journal of Water and Soil, 34(1), 27-42. [In Persia]
Rasool, T., Dar, A. Q., & Wani, M. A. (2021). Development of a predictive equation for modelling the infiltration process using gene expression programming. Water Resources Management, 35, 1871-1888.
Sadegh Zadeh, K. S., Shirmohammadi, A., Montas, H. J., & Felton, G. (2007). Evaluation of infiltration models in contaminated landscape. Journal of Environmental Science and Health, Part A, 42(7), 983-988.
Shin, S. S., Park, S. D., Pierson, F. B., & Williams, C. J. (2019). Evaluation of physical erosivity factor for interrill erosion on steep vegetated hillslopes. Journal of Hydrology, 571, 559-572.
Song, W., Jiao, J., Du, P., & Liu, H. (2021). Optimizing the soil conservation service curve number model by accounting for rainfall characteristics: a case study of surface water sources in Beijing. Environmental Monitoring and Assessment, 193, 1-17.
Talebi, A., Pourmohammadi, S., & Rahimian, M.H. (2014). Determining the most suitable permeability equation for watersheds in dry and semi-dry areas (Case study: Maneshad watershed, Yazd province). Soil and Water Science Journal, 24(4), 101-114. [In Persia]
Vaezi, A., Behtari, M., & Foroumdi, M. (2019). Evaluation of water infiltration models in soils with different textures under various initial moisture content levels. Ecohydrology, 6(3), 707-717. [In Persia].
Valiantzas, J. D., Pollalis, E. D., Soulis, K. X., & Londra, P. A. (2009). Modified form of the extended Kostiakov equation including various initial and boundary conditions. Journal of irrigation and drainage engineering, 135(4), 450-458.
Xin, Y., Xie, Y., & Liu, Y. (2019). Effects of residue cover on infiltration process of the black soil under rainfall simulations. Water, 11(12), 2593.
Yang, M., Zhang, Y., & Pan, X. (2020). Improving the Horton infiltration equation by considering soil moisture variation. Journal of Hydrology, 586, 124864.