Skip to main content
Log in

Different Infiltration Methods for Swat Model Seasonal Calibration of Flow and Sediment Production

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

Hydrosedimentological models make it possible to better understand the dynamics of water and sediment production in watersheds when properly calibrated. The objective of this study was to analyze the effects of the curve number (CN) and Green and Ampt (GA) methods and of seasonal calibration of the Soil and Water Assessment Tool (SWAT) model for estimating flow and sediment production in an agricultural basin. In this research, we presented an original application with the hourly suspended sediment concentration (SSC) generated by artificial neural networks (ANNs) for use in SWAT model calibration. This method was applied in the Taboão basin (77.5 km2), with data from 2008 to 2018. The best Nash–Sutcliffe (NS) coefficient values were obtained using the combination of wet years for calibration and the GA method for both daily flow (NScalibration: 0.74; and NSvalidation: 0.68) and daily sediment production (NScalibration: 0.83; and NSvalidation: 0.77). The CN method did not result in satisfactory values during daily flow calibration (NScalibration 0.39). The results showed that it is possible to employ the SWAT model for hydrosedimentological prediction in the Taboão basin, with a favorable efficiency, using the GA method and calibration with data for wet periods.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Availability of Data and Materials

Not applicable.

References

  • Abbaspour KC, Johnson CA, Van Genuchten MT (2004) Estimating uncertain flow and transport parameters using a sequential uncertainty fitting procedure. Vadose Zone J 3(4):1340–1352

    Article  Google Scholar 

  • Al Khoury I, Boithias L, Labat D (2023) A review of the application of the soil and water assessment tool (SWAT) in karst watersheds. Water 15(5):954

    Article  Google Scholar 

  • Aloui S, Mazzoni A, Elomri A, Aouissi J, Boufekane A, Zghibi A (2023) A review of soil and water assessment tool (SWAT) studies of Mediterranean catchments: Applications, feasibility, and future directions. J Environ Manag 326:116799

    Article  Google Scholar 

  • Arnold JG, Moriasi DN, Gassman PW, Abbaspour KC, White MJ, Srinivasan R, ... Jha MK (2012) SWAT: Model use, calibration, and validation. Trans ASABE 55(4):1491–1508

    Article  Google Scholar 

  • Arnold JG, Srinivasan R, Muttiah RS, Williams JR (1998) Large area hydrologic modeling and assessment part I: model development 1. JAWRA J Am Water Resour Assoc 34(1):73–89

    Article  Google Scholar 

  • Bagnold RA (1977) Bed load transport by natural rivers. Water Resour Res 13(2):303–312

    Article  Google Scholar 

  • Bressiani DDA, Gassman PW, Fernandes JG, Garbossa LHP, Srinivasan R, Bonumá NB, Mendiondo EM (2015) Review of soil and water assessment tool (SWAT) applications in Brazil: Challenges and prospects. Int J Agric Biol Eng 8(3):9–35

    Google Scholar 

  • Brighenti TM, Bonumá NB, Chaffe PLB (2016) Calibração hierárquica do modelo SWAT em uma bacia hidrográfica Catarinense. RBRH 21:53–64

    Article  Google Scholar 

  • Brighenti TM, Bonumá NB, Srinivasan R, Chaffe PLB (2019) Simulating sub-daily hydrological process with SWAT: a review. Hydrol Sci J 64(12):1415–1423

    Article  Google Scholar 

  • Castro NMDR, Auzet AV, Chevallier P, Leprun JC (1999) Land use change effects on runoff and erosion from plot to catchment scale on the basaltic plateau of Southern Brazil. Hydrol Process 13(11):1621–1628

    Article  Google Scholar 

  • Daramola JM, Ekhwan T, Mokhtar J, Lam KC (2019) Streamflow sensitivity analysis, calibration and validation using soil and water assessment tools (SWAT) and sufi-2 algorithm. Afr Scholar Publ Res Int 15(2). ISSN: 2010–1086

  • Gupta HV, Sorooshian S, Yapo PO (1999) Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration. J Hydrol Eng 4(2):135–143

    Article  Google Scholar 

  • Guse B, Pfannerstill M, Kiesel J, Strauch M, Volk M, Fohrer N (2019) Analysing spatio-temporal process and parameter dynamics in models to characterise contrasting catchments. J Hydrol 570:863–874

    Article  Google Scholar 

  • Hosseini SH, Khaleghi MR (2020) Application of SWAT model and SWAT-CUP software in simulation and analysis of sediment uncertainty in arid and semi-arid watersheds (case study: The Zoshk-Abardeh watershed). Model Earth Syst Environ 6(4):2003–2013

    Article  Google Scholar 

  • Jeong J, Kannan N, Arnold JG, Glick R, Gosselink L, Srinivasan R, Harmel RD (2011) Development of sub-daily erosion and sediment transport algorithms for SWAT. Trans ASABE 54(5):1685–1691

    Article  Google Scholar 

  • King KW, Arnold JG, Bingner RL (1999) Comparison of Green-Ampt and curve number methods on Goodwin Creek watershed using SWAT. Trans ASAE 42(4):919–926

    Article  Google Scholar 

  • Koltsida E, Mamassis N, Kallioras A (2021) Hydrological modeling using the SWAT Model in urban and peri-urban environments: the case of Kifissos experimental sub-basin (Athens, Greece). Hydrol Earth Syst Sci Discuss 2021:1–24

    Google Scholar 

  • Kottek M, Grieser J, Beck C, Rudolf B, Rubel F (2006) World map of the Köppen-Geiger climate classification updated. Meteorol Z (Berl) 15:259–263

  • Kumar A, Matta G, Bhatnagar S (2021) A coherent approach of Water Quality Indices and Multivariate Statistical Models to estimate the water quality and pollution source apportionment of River Ganga System in Himalayan region, Uttarakhand, India. Environ Sci Pollut Res 28:42837–42852

    Article  Google Scholar 

  • Li M, Di Z, Duan Q (2021) Effect of sensitivity analysis on parameter optimization: Case study based on streamflow simulations using the SWAT model in China. J Hydrol 603:126896

    Article  Google Scholar 

  • Matta G, Kumar A, Nayak A, Kumar P, Kumar A, Naik PK, Singh SK (2022) Assessing heavy metal index referencing health risk in Ganga River System. Int J River Basin Manag 1–11

  • Meaurio M, Zabaleta A, Srinivasan R, Sauvage S, Sánchez-Pérez JM, Lechuga-Crespo JL, Antiguedad I (2021) Long-term and event-scale sub-daily streamflow and sediment simulation in a small forested catchment. Hydrol Sci J 66(5):862–873

    Article  Google Scholar 

  • Mein RG, Larson CL (1973) Modeling infiltration during a steady rain. Water Resour Res 9(2):384–394

    Article  Google Scholar 

  • Monteith JL (1981) Evaporation and surface temperature. Q J R Meteorol Soc 107(451):1–27

    Article  Google Scholar 

  • Moriasi DN, Gitau MW, Pai N, Daggupati P (2015) Hydrologic and water quality models: Performance measures and evaluation criteria. Trans ASABE 58(6):1763–1785

    Article  Google Scholar 

  • Musie M, Sen S, Srivastava P (2020) Application of CORDEX-AFRICA and NEX-GDDP datasets for hydrologic projections under climate change in Lake Ziway sub-basin. Ethiopia. J Hydrol: Reg Stud 31:100721

    Google Scholar 

  • Nayak A, Matta G, Uniyal DP (2022) Hydrochemical characterization of groundwater quality using chemometric analysis and water quality indices in the foothills of Himalayas. Environ Dev Sustain 1–32

  • Nilawar AP, Waikar ML (2019) Impacts of climate change on streamflow and sediment concentration under RCP 4.5 and 8.5: A case study in Purna river basin, India. Sci Total Environ 650:2685–2696

    Article  Google Scholar 

  • Neitsch SL, Arnold JG, Kiniry JR, Williams JR (2011) Soil and water assessment tool theoretical documentation version 2009. Texas Water Resour Inst

  • Oliveira MHC, Sari V, dos Reis Castro NM, Pedrollo OC (2017) Estimation of soil water content in watershed using artificial neural networks. Hydrol Sci J 62(13):2120–2138

    Article  Google Scholar 

  • Ranjan R, Mishra A (2022) Examining model performances and parameter uncertainty for streamflow and suspended sediment regime simulation: Comparison of three calibration methods. J Hydrol 612:128304

    Article  Google Scholar 

  • Sari V (2017) Monitoramento e modelagem da produção de sedimentos em uma bacia hidrográfica no noroeste do Rio Grande do Sul. 313 p. Tese (Doutorado). Instituto de Pesquisas Hidráulicas da UFRGS

  • Sari V, dos Reis Castro NM, Pedrollo OC (2017) Estimate of suspended sediment concentration from monitored data of turbidity and water level using artificial neural networks. Water Resour Manag 31:4909–4923

    Article  Google Scholar 

  • SCS (1972) National engineering handbook, section 4, hydrology. US Department of Agriculture, SCS, Washington, DC

  • Singh A, Jha SK (2021) Identification of sensitive parameters in daily and monthly hydrological simulations in small to large catchments in Central India. J Hydrol 601:126632

    Article  Google Scholar 

  • Teixeira LC, Mariani PP, Pedrollo OC, dos Reis Castro NM, Sari V (2020) Artificial neural network and fuzzy inference system models for forecasting suspended sediment and turbidity in basins at different scales. Water Resour Manag 34(11):3709–3723

    Article  Google Scholar 

  • Van Griensven AV, Meixner T, Grunwald S, Bishop T, Diluzio M, Srinivasan R (2006) A global sensitivity analysis tool for the parameters of multi-variable catchment models. J Hydrol 324(1–4):10–23

    Article  Google Scholar 

  • Verma N, Dash P (2016) Sensitivity analysis through SWAT model for Sirsa River Basin in Western Himalaya. Natl Geogr J India 62(3):245–258

    Google Scholar 

  • Viji R, Prasanna PR, Ilangovan R (2015) Modified SCS-CN and Green-Ampt methods in surface runoff modelling for the Kundahpallam watershed, Nilgiris, Western Ghats, India. Aquat Procedia 4:677–684

    Article  Google Scholar 

  • Weibel CL, Szupiany R, Latosinski F, Amsler M, Repasch M (2022) Sources and temporal dynamics of suspended sediment transport along the middle Paraná River. J S Am Earth Sci 119:103968

    Article  Google Scholar 

  • Williams JR (1969) Flood routing with variable travel time or variable storage coefficients. Trans ASAE 12(1):100–0103

    Article  Google Scholar 

  • Williams JR (1980) Spnm, a model for predicting sediment, phosphorus, and nitrogen yields from agricultural basins 1. JAWRA J Am Water Resour Assoc 16(5):843–848

    Article  Google Scholar 

  • Williams JR, Berndt HD (1977) Sediment yield prediction based on watershed hydrology. Trans ASAE 20(6):1100–1104

    Article  Google Scholar 

  • Wu L, Liu X, Chen J, Li J, Yu Y, Ma X (2022a) Efficiency assessment of best management practices in sediment reduction by investigating cost-effective tradeoffs. Agric Water Manag 265:107546

    Article  Google Scholar 

  • Wu H, Zhang J, Bao Z, Wang G, Wang W, Yang Y, Wang J (2022b) Runoff modeling in ungauged catchments using machine learning algorithm-based model parameters regionalization methodology. Engineering

  • Xiang X, Ao T, Xiao Q, Li X, Zhou L, Chen Y, Bi Y, Guo J (2022) Parameter sensitivity analysis of SWAT modeling in the Upper Heihe River Basin using four typical approaches. Appl Sci 12(19):9862

    Article  Google Scholar 

  • Yamamoto EMS, Sayama T, Yamamoto K (2020) Comparison of runoff generation methods for land use impact assessment using the SWAT model in humid tropics. Hydrol Res Lett 14(2):81–88

    Article  Google Scholar 

  • Yang X, Liu Q, He Y, Luo X, Zhang X (2016) Comparison of daily and sub-daily SWAT models for daily streamflow simulation in the Upper Huai River Basin of China. Stoch Env Res Risk Assess 30:959–972

    Article  Google Scholar 

  • Zhang D, Chen X, Yao H, Lin B (2015) Improved calibration scheme of SWAT by separating wet and dry seasons. Ecol Model 301:54–61

    Article  Google Scholar 

  • Zhu Q, Zhang X, Ma C, Gao C, Xu YP (2016) Investigating the uncertainty and transferability of parameters in SWAT model under climate change. Hydrol Sci J 61(5):914–930

    Google Scholar 

Download references

Funding

Financiadora de Estudos e Projetos.

Author information

Authors and Affiliations

Authors

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Priscila Pacheco Mariani, Nilza Maria dos Reis Castro, Vanessa Sari, Taís Carine Schmitt and Olavo Correa Pedrollo. The first draft of the manuscript was written by Priscila Pacheco Mariani, and all authors commented on previous versions of the manuscript. All the authors have read and approved the final manuscript.

Corresponding author

Correspondence to Priscila Pacheco Mariani.

Ethics declarations

Ethical Approval

Not applicable.

Competing Interests

The authors have no relevant financial or nonfinancial interests to disclose.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mariani, P.P., dos Reis Castro, N.M., Sari, V. et al. Different Infiltration Methods for Swat Model Seasonal Calibration of Flow and Sediment Production. Water Resour Manage 38, 303–322 (2024). https://doi.org/10.1007/s11269-023-03671-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-023-03671-1

Keywords

Navigation