Skip to main content
SpringerLink
Log in

Estimating parameters for the Kostiakov-Lewis infiltration model from soil physical properties

  • Soils, Sec 2 • Global Change, Environ Risk Assess, Sustainable Land Use • Research Article
  • Published:
Journal of Soils and Sediments Aims and scope Submit manuscript

Abstract

Purpose

Infiltration modeling is an important tool to describe the process of water infiltration in the soil. However, direct measurements of the parameters of infiltration models are usually time-consuming and laborious. The present study proposed an effective method to estimate parameters of the Kostiakov-Lewis model (a classical infiltration model) from soil physical properties (SPPs).

Materials and methods

Parameters k, α, and f0 of the Kostiakov-Lewis infiltration models were measured in 240 double-ring field experiments in Shanxi Province, China. SPPs at the corresponding experimental points were measured at the topsoil layer (TL, 0–20 cm) and the top-subsoil layer (TSL, 0–20 and 20–40 cm). The Kennard-Stone (KS) sampling method and principal component analysis (PCA) were used for dividing training samples and extracting principal components (PCs) of SPPs, respectively. Partial least squares (PLS), back-propagation neural networks (BPNNs), and a support vector machine (SVM) were used to establish models for estimating k, α, and f0 with the SPPs of TL and TSL as the input variables (IV).

Results and discussion

The differences in soil density (BD), texture, and moisture content (θv) were found in topsoil and subsoil, but loading distributions of SPPs on PCs present different degrees of correlation. Moreover, SVM produced the most accurate estimation among these three methods for using the SPP of TL and TSL as inputs. The highest accuracy for k estimations was obtained by SVM using the SPP of TL as IV; R and RMSE in the model test process were 0.78 and 0.3 cm min−1, respectively. However, using SPP of TSL as IV obtained the highest accuracy for both α and f0 estimations with the SVM method (R values were 0.71 and 0.82, respectively, and RMSE values were 0.03 and 0.018 cm min−1) in the model testing.

Conclusions

The SVM method with SPPs as inputs is an effective and practical method for estimating the parameters of the Kostiakov-Lewis infiltration model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Ahuja LR, Kozak JA, Andales AA, Ma L (2007) Scaling parameters of the Lewis-Kostiakov water infiltration equation across soil textural classes and extension to rain infiltration. Trans ASABE 50:1525–1541

    Google Scholar 

  • Alagna V, Iovino M, Bagarello V, Mataix-Solera J, Lichner L (2018) Alternative analysis of transient infiltration experiment to estimate soil water repellency. Hydrol Process 33:661–674

    Google Scholar 

  • Ali S, Ghosh NC, Singh R, Sethy BK (2013) Generalized explicit models for estimation of wetting front length and potential recharge. Water Resour Manag 27:2429–2445

    Google Scholar 

  • Assouline S (2013) Infiltration into soils: conceptual approaches and solutions. Water Resour Res 49:1755–1772

    Google Scholar 

  • Babaei F, Zolfaghari AA, Yazdani MR, Sadeghipour A (2018) Spatial analysis of infiltration in agricultural lands in arid areas of Iran. Catena 170:25–35

    Google Scholar 

  • Bao SD (1999) Soil agrichemical analysis. In: China Agriculture Press. Beijing, China

    Google Scholar 

  • Basha HA (2011) Infiltration models for semi-infinite soil profiles. Water Resour Res 47:192–198

    Google Scholar 

  • Bayabil HK, Dile YT, Tebebu TY, Engda TA, Steenhuis TS (2019) Evaluating infiltration models and pedotransfer functions: implications for hydrologic modeling. Geoderma 338:159–169

    Google Scholar 

  • Biswas A, Zhang Y (2018) Sampling designs for validating digital soil maps: a review. Pedosphere 28:1–15

    Google Scholar 

  • Blake GR, Hartge KH (1986) Bulk density. In: Klute A (ed) Methods of soil analysis: part l – physical and mineralogical methods, second edn. ASA/SSSA, Monograph 9, Madison, pp 374–380

    Google Scholar 

  • Brus DJ (2019) Sampling for digital soil mapping: a tutorial supported by R scripts. Geoderma 338:464–480

    Google Scholar 

  • Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:27

    Google Scholar 

  • Chen X, Hu Q (2004) Groundwater influences on soil moisture and surface evaporation. J Hydrol 297:285–300

    Google Scholar 

  • Corradini C, Morbidelli R, Flammini A, Govindaraju RS (2011) A parameterized model for local infiltration in two-layered soils with a more permeable upper layer. J Hydrol 396:221–232

    Google Scholar 

  • Damodhara Rao M, Raghuwanshi NS, Singh R (2006) Development of a physically based 1D-infiltration model for irrigated soils. Agr Water Manage 85:165–174

    Google Scholar 

  • Das SK, Samui P, Sabat AK (2012) Prediction of field hydraulic conductivity of clay liners using an artificial neural network and support vector machine. Int J Geomechanic 12:606–611

    Google Scholar 

  • Deng P, Zhu J (2016) Analysis of effective Green–Ampt hydraulic parameters for vertically layered soils. J Hydrol 538:705–712

    Google Scholar 

  • Deng J, Chen X, Du Z, Zhang Y (2011) Soil water simulation and predication using stochastic models based on LS-SVM for red soil region of China. Water Resour Manag 25:2823–2836

    Google Scholar 

  • Di Prima S, Concialdi P, Lassabatere L, Angulo-Jaramillo R, Pirastru M, Cerdà A, Keesstra S (2018) Laboratory testing of Beerkan infiltration experiments for assessing the role of soil sealing on water infiltration. Catena 167:373–384

    Google Scholar 

  • Elmaloglou S, Malamos N (2006) A methodology for determining the surface and vertical components of the wetting front under a surface point source, with root water uptake and evaporation. Irrig Drain 55:99–111

    Google Scholar 

  • Gee GW, Bauder, J.W. (1986): Particles size analysis. In: Klute, A. (Ed.), Methods of Soil Analysis: Part 1 – Physical and Mineralogical Methods, second ed. ASA/SSSA, Monograph 9, pp. 383–411

  • Ghorbani-Dashtaki S, Homaee M, Loiskandl W (2016) Towards using pedotransfer functions for estimating infiltration parameters. Hydrol Sci J 61:1477–1488

    Google Scholar 

  • Green WH, G (1911) Studies on soil physics, part I. the flow of air and water through soils. J Agric Sci 4:1–24

  • Guisheng F, Yonghong H, Min S,(2011) Experimental study on the reasonable inbuilt-ring depth of soil one-dimensional infiltration experiment in field. CCTA

  • Hocking RR (2013) Methods and applications of linear models: regression and the analysis of variance, 3rd edn. Wiley, New York

    Google Scholar 

  • Horn R, Way T, Rostek J (2003) Effect of repeated tractor wheeling on stress/strain properties and consequences on physical properties in structured arable soils. Soil Tillage Res 73:101–106

    Google Scholar 

  • Horton RE (1938) The interpretation and application of runoff plot experiments with reference to soil erosion problems. Soil Sci Soc Am Proc 5:399–C417

    Google Scholar 

  • Huang JTC (2000) Soil erosion and dryland farming. CRC Press

  • Huang D, Chen J, Zhan L, Wang T, Su Z (2016) Evaporation from sand and loess soils: an experimental approach. Transp Porous Media 113:639–651

    CAS  Google Scholar 

  • Iovino M, Angulo-Jaramillo R, Bagarello V, Gerke HH, Jabro J, Lassabatere L (2017) Thematic issue on soil water infiltration. J Hydrol Hydromechanic 65:205–208

    Google Scholar 

  • Jha MK, Mahapatra S, Mohan C, Pohshna C (2019) Infiltration characteristics of lateritic vadose zones: field experiments and modeling. Soil Tillage Res 187:219–234

    Google Scholar 

  • Keller T, Håkansson I (2010) Estimation of reference bulk density from soil particle size distribution and soil organic matter content. Geoderma 154:398–406

    CAS  Google Scholar 

  • Kennard RW, Stone LA (1969) Computer aided design of experiments. Technometrics 26:217–224

    Google Scholar 

  • Khatri KL, Smith RJ (2005) Evaluation of methods for determining infiltration parameters from irrigation advance data. Irrig Drain 54:467–482

    Google Scholar 

  • Kostiakov AN (1932) On the dynamics of the coefficient of water percolation in soils and on the necessity of studying it from a dynamic point of view for purposes of amelioration. In: Sixth commission, International Society of Soil Science, part a, pp 15–21

  • Latorre B, Moret-Fernández D, Lassabatere L, Rahmati M, López MV, Angulo-Jaramillo R, Sorando R, Comín F, Jiménez JJ (2018) Influence of the β parameter of the Haverkamp model on the transient soil water infiltration curve. J Hydrol 564:222–229

    Google Scholar 

  • Lei G, Zeng W, Zhu J, Zha Y, Fang Y, Song Y, Chen M, Qian Y, Wu J, Huang J, (2019) Quantification of Leaf Growth, Height Increase, and Compensatory Root Water Uptake of Sunflower in Heterogeneous Saline Soils. Agronomy Journal 111(3):1010

    CAS  Google Scholar 

  • Li W (2001) Agro-ecological farming systems in China. CRC Press, p 454

  • Li F-M, Xu J (2002) Rainwater-collecting eco-agriculture in semi-arid region of Loess Plateau. Chin J Eco-Agric 10:101–103

    Google Scholar 

  • Ma Y, Feng S, Zhan H, Liu X, Su D, Kang S, Song X (2011) Water infiltration in layered soils with air entrapment: modified Green-Ampt model and experimental validation. J Hydrol Eng 16:628–638

    Google Scholar 

  • Mezencev VJ (1948) Theory of formation of the surface runoff. Meteorol I Gidrologia 3:33–46

    Google Scholar 

  • Mirzaee S, Zolfaghari AA, Gorji M, Dyck M, Ghorbani Dashtaki S (2013) Evaluation of infiltration models with different numbers of fitting parameters in different soil texture classes. Arch Agron Soil Sci 60:681–693

    Google Scholar 

  • Mishra SK, Tyagi JV, Singh VP (2003) Comparison of infiltration models. Hydrol Process 17:2629–2652

    Google Scholar 

  • Moraes R, Valiati JF, Gavião Neto WP (2013) Document-level sentiment classification: an empirical comparison between SVM and ANN. Expert Syst Appl 40:621–633

    Google Scholar 

  • Moravejalahkami B, Mostafazadeh-Fard B, Heidarpour M, Abbasi F (2009) Furrow infiltration and roughness prediction for different furrow inflow hydrographs using a zero-inertia model with a multilevel calibration approach. Biosyst Eng 103:374–381

    Google Scholar 

  • Moreira WH, Tormena CA, Karlen DL, ÁPd S, Keller T, Betioli E (2016) Seasonal changes in soil physical properties under long-term no-tillage. Soil Tillage Res 160:53–64

    Google Scholar 

  • Mouazen AM, Kuang B, De Baerdemaeker J, Ramon H (2010) Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy. Geoderma 158:23–31

    CAS  Google Scholar 

  • Nie W, Huang H, Ma X, Fei L (2017a) Evaluation of closed-end border irrigation accounting for soil infiltration variability. J Irrig Drain Eng 143:04017008

    Google Scholar 

  • Nie W, Ma X, Fei L (2017b) Evaluation of infiltration models and variability of soil infiltration properties at multiple scales. Irrig Drain 66:589–599

    Google Scholar 

  • Parchami-Araghi F, Mirlatifi SM, Ghorbani Dashtaki S, Mahdian MH (2013) Point estimation of soil water infiltration process using artificial neural networks for some calcareous soils. J Hydrol 481:35–47

    CAS  Google Scholar 

  • Parhi PK (2016) An improvement to the waterfront advance-time relation for irrigation borders. Irrig Drain 65:568–573

    Google Scholar 

  • Parhi PK, Mishra SK, Singh R (2007) A modification to Kostiakov and modified Kostiakov infiltration models. Water Resour Manag 21:1973–1989

    Google Scholar 

  • Patil NG, Singh SK (2016) Pedotransfer functions for estimating soil hydraulic properties: a review. Pedosphere 26:417–430

    Google Scholar 

  • Philip JR (1957) The theory of infiltration: 1. The infiltration equation and its solution. Soil Sci 83:345–C357

    CAS  Google Scholar 

  • Raghuwanshi NS, Saha R, Mailapalli DR, Upadhyaya SK (2011) Infiltration evaluation strategy for border irrigation management. J Irrig Drain Eng-ASCE 137:602–609

    Google Scholar 

  • Rahmati M (2017) Reliable and accurate point-based prediction of cumulative infiltration using soil readily available characteristics: a comparison between GMDH, ANN, and MLR. J Hydrol 551:81–91

    Google Scholar 

  • Reichert JM, Suzuki LEAS, Reinert DJ, Horn R, Håkansson I (2009) Reference bulk density and critical degree-of-compactness for no-till crop production in subtropical highly weathered soils. Soil Tillage Res 102:242–254

    Google Scholar 

  • Richards LA (1931) Capillary conduction of liquids in porous mediums. Physics 1:318–333

    Google Scholar 

  • Rossel RAV, Behrens T (2010) Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 158:46–54

    CAS  Google Scholar 

  • Sajjadi SAH, Mirzaei M, Nasab AF, Ghezelje A, Tadayonfar G, Sarkardeh H (2016) Effect of soil physical properties on infiltration rate. Geomechan Eng 10:727–736

    Google Scholar 

  • Sayah B, Gil-Rodríguez M, Juana L (2016) Development of one-dimensional solutions for water infiltration. Analysis and parameters estimation. J Hydrol 535:226–234

    Google Scholar 

  • Strelkoff T (1990) SRFR: a computer program for simulating flow in surface irrigation: furrows-basins-borders. Water Conservation Laboratory, Agricultural Research Service, U.S. Department of Agriculture

  • Strudley M, Green T, Ascoughii J (2008) Tillage effects on soil hydraulic properties in space and time: state of the science. Soil Tillage Res 99:4–48

    Google Scholar 

  • Van den Putte A, Govers G, Leys A, Langhans C, Clymans W, Diels J (2013) Estimating the parameters of the Green–Ampt infiltration equation from rainfall simulation data: why simpler is better. J Hydrol 476:332–344

    Google Scholar 

  • Votrubová J, Císlerová M, Gao Amin MH, Hall LD (2003) Recurrent ponded infiltration into structured soil: a magnetic resonance imaging study. Water Resour Res 39. https://doi.org/10.1029/2003WR002222

  • Walter R (1976) Calibration of selected infiltration equations for the Georgia Coastal Plain. U.S. Agricultural Research Service. Southern Region. ARS-S. no. 113

  • Waszczyszyn Z, Ziemiański L (2001) Neural networks in mechanics of structures and materials – new results and prospects of applications. Comput Struct 79:2261–2276

    Google Scholar 

  • Wei Y, Wu X, Cai C (2015) Splash erosion of clay–sand mixtures and its relationship with soil physical properties: the effects of particle size distribution on soil structure. Catena 135:254–262

    Google Scholar 

  • Wold S, Sjöström M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst 58:109–130

    CAS  Google Scholar 

  • Wong KI, Wong PK, Cheung CS, Vong CM (2013) Modelling of diesel engine performance using advanced machine learning methods under scarce and exponential data set. Appl Soft Comput 13:4428–4441

    Google Scholar 

  • Wösten JHM (1997) Chapter 10 Pedotransfer functions to evaluate soil quality. Dev Soil Sci 25:221–245

    Google Scholar 

Download references

Funding

This work was supported by the National Natural Science Foundation of China (grant nos. 51879196, 51790533, and 51609175) and the Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin (China Institute of Water Resources and Hydropower Research) (grant no. IWHR-SKL-KF201814).

Author information

Authors and Affiliations

  1. College of Water Resource Science and Engineering, Taiyuan University of Technology, Taiyuan, 030024, Shanxi Province, People’s Republic of China

    Guoqing Lei & Guisheng Fan

  2. State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, Hubei Province, People’s Republic of China

    Guoqing Lei, Wenzhi Zeng & Jiesheng Huang

  3. Soil, Water, and Environmental Science Department, School of Earth and Environmental Sciences, University of Arizona, Tucson, AZ, 85721, USA

    Guoqing Lei

  4. State Key Laboratory of Simulation and Regulation of the Water Cycle in River Basins, China Institute of Water Resources and Hydropower Research, Beijing, 100038, People’s Republic of China

    Wenzhi Zeng

Authors
  1. Guoqing Lei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guisheng Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenzhi Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiesheng Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Guisheng Fan or Wenzhi Zeng.

Additional information

Responsible editor: Fanghua Hao

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lei, G., Fan, G., Zeng, W. et al. Estimating parameters for the Kostiakov-Lewis infiltration model from soil physical properties. J Soils Sediments 20, 166–180 (2020). https://doi.org/10.1007/s11368-019-02332-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11368-019-02332-4

Keywords

Search

Navigation