Quantifying Surface Water and Ground Water Interactions using a Coupled SWAT_FEM Model: Implications of Management Practices on Hydrological Processes in Irrigated River Basins


This study coupled the Soil and Water Assessment Tool (SWAT) with a ground water finite element model (FEM) with the enhancements of multiple interface conversions and management practices. The coupled model, SWAT_FEM was applied to assess the hydrology of the Chennai River basin in India, a coastal zone with significant irrigation. The SWAT_FEM enhanced the predictions of stream flows and ground water levels (R2:0.69,0.81; Nash Sutcliffe Efficiency (NSE):0.64,0.74) compared to the standalone model, SWAT (R2:0.64,0.66; NSE:0.60,0.63) respectively. The coupled model produced an all-inclusive representation of the impacts of management practices on the hydrological processes and generated insights into the spatiotemporal patterns of the surface water and ground water interactions in the study area. The results showed that the interactions of surface water and ground water were significant in the mainstream of Chennai River basin. The seasonal ground water levels obtained with the SWAT_FEM model reinforced the increases in exorbitant ground water abstraction rates (9%-44%) with the introduction of management practices including reservoirs, pond irrigation, and agricultural water use. The results emphasized that if the ground water demand continued to increase, accelerated and unregulated ground water extraction is bound to happen shortly to suffice the water use, which can bring about environmental problems to this basin. Overall, this study demonstrated the applicability of the SWAT_FEM model and its value to the water resources management in irrigated areas with management practices. The developed model can be utilized in water resources assessment tools for effective predictions of ground water contributions in river basins.

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  1. Ali M, Khan J, Aslam I, Khan Z (2011) Simulation of the impacts of land use change on surface runoff of Lai Nullah basin in Islamabad. Pakistan Landsc Urban Plan 102:271–279. https://doi.org/10.1016/j.landurbplan.2011.05.006

    Article  Google Scholar 

  2. Arnold G, Kiniry R, Srinivasan R, Williams R, Haney B, Neitsch L (2012) Soil & Water Assessment Tool: Input/Output Documentation. Texas Water Resources Institute (TR-439)

  3. Aubrecht C, Steinnocher K, Hollaus M, Wagner W (2008) Integrating earth observation and GIScience for high resolution spatial and functional modeling of urban land use. Comput Environ Urban Syst 33(1):15–25. https://doi.org/10.1016/j.compenvurbsys.2008.09.007

    Article  Google Scholar 

  4. Bailey RT, Wible TC, Arabi M, Records RM, Ditty J (2016) Assessing regional-scale spatio-temporal patterns of groundwater–surface water interactions using a coupled SWAT-MODFLOW model. Hydrol Process 30(23):4420–4433. https://doi.org/10.1002/hyp.10933

    Article  Google Scholar 

  5. Central Ground Water Board (2019) Report (CGWB, 2013). Ministry of Water Resources, River Development and Ganga Rejuvenation: Government of India. http://cgwb.gov.in/gwresource.html. Accessed 27 June 2019

  6. CGIARCSI (2019) SRTM 90m DEM. SRTM 90m Digital Elevation Database v4.1: Consortium for Spatial Information (CGIARCSI). http://www.cgiar-csi.org/data/srtm-90m-digital-elevation-database-v4-1. Accessed 29 June 2019

  7. Chen XH, Song J, Wang W (2010) Spatial variability of specific yield and vertical hydraulic conductivity in a highly permeable alluvial aquifer. J Hydrol 388:379–388. https://doi.org/10.1016/j.jhydrol.2010.05.017

    Article  Google Scholar 

  8. Cremeans MM, Devlin JF, Osorno TC, McKnight US, Bjerg PL (2020) A comparison of tools and methods for estimating groundwater-surface water exchange. Ground Water Monit Remediat 40(1):24–34. https://doi.org/10.1111/gwmr.12362

    Article  Google Scholar 

  9. Damtew GT, Kim Y (2015) Surface water potential assessment of ungauged catchments in lake Tana Basin, Ethiopia. 10th Alexander von Humboldt International Conference AvH10–11

  10. Dechmi F, Burguete J, Skhiri A (2012) SWAT application in intensive irrigation systems: model modification, calibration and validation. J Hydrol 470–471:227–238

    Article  Google Scholar 

  11. Deng Y, Jiang WJ, He B, Chen Z, Jia K (2018) Change in intensity and frequency of extreme precipitation and its possible teleconnection with large-scale climate index over the China from 1960 to 2015. J Geophys Res-Atmos 123(4):2068–2081. https://doi.org/10.1002/2017JD027078

  12. FAOSTAT (2019) Soil data 2007. The Food and Agriculture Organization of the United States (FAO) Database. http://www.fao.org/faostat/en/#data. Accessed 21 July 2019

  13. Furusho C, Andrieu H, Chancibault K (2014) Analysis of the hydrological behaviour of an urbanizing basin. Hydrol Process 28:1809–1819. https://doi.org/10.1002/hyp.9706

    Article  Google Scholar 

  14. Gerezgiher AK, Haile M, Gebresamuel G, Yusuf MM (2018) Land suitability analysis for sorghum crop production in northern semi-arid Ethiopia: Application of GIS-based fuzzy AHP approach. Cogent Food Agric 4(1):1507184. https://doi.org/10.1080/23311932.2018.1507184

    Article  Google Scholar 

  15. GFMS (2019) Global Flood Monitoring System (GFMS) Portal: Real-time Quasi-Global Hydrological Calculations at 1/8th Degree and 1 km Resolution. University of Maryland. http://flood.umd.edu/. Accessed 21 July 2019

  16. Guzman JA, Moriasi DN, Gowda PH, Steiner JL, Starks PJ, Arnold JG, Srinivasan R (2015) A model integration framework for linking SWAT and MODFLOW. Environ Model Software 73:103–116. https://doi.org/10.1016/j.envsoft.2015.08.011

    Article  Google Scholar 

  17. Hancock PJ (2002) Human impacts on the stream–groundwater exchange zone. Environ Manage 29:763–781. https://doi.org/10.1007/s00267-001-0064-5

    Article  Google Scholar 

  18. Huang H, Chen Z, Wang T, Zhang L, Liu T, Zhou G (2021) Pattern and degree of groundwater recharge from river leakage in a karst canyon area under intensive mine dewatering. Sci Total Environ 774:144921. https://doi.org/10.1016/j.scitotenv.2020.144921

    Article  Google Scholar 

  19. Huo A, Dang J, Song J, Chen XH, Mao H (2016) Simulation modeling for water governance in basins based on surface water and ground water. Agric Water Manag 174:22–29. https://doi.org/10.1016/j.agwat.2016.02.027

    Article  Google Scholar 

  20. Hussin NH, Yusoff I, Raksmey M (2020) Comparison of applications to evaluate groundwater recharge at lower kelantan river basin. Malaysia Geosci J 10(8):289. https://doi.org/10.3390/geosciences10080289

    Article  Google Scholar 

  21. Indian Meteorological Department (2018) Meteorological Centre: Season's Rainfall. IMD: Season’s Rainfall 1986–2010 Meteorological Centre, Tamil Nadu. https://imdtvm.gov.in/index.php?option=com_content&task=view&id=29&Itemid=43. Accessed 26 July 2018

  22. Integrated Water Flow Model (2011) User’s Manual: Integrated Hydrological Models Development Unit Modeling Support Branch Bay-Delta Office

  23. IWMI (2018) Land products. International Water Management Institute (IWMI): Global Irrigated Area Mapping (GIAM) Database. http://www.iwmi.cgiar.org/. Accessed 21 May 2018

  24. Jagadeshan G, Anandasabari K, Poornavel S (2015) Ground water quality of Kosasthalaiyar River Basin, Thiruvallur District, Tamil Nadu, India. International Journal of Innovative Research in Science, Engineering and Technology 4:1164–1170

    Google Scholar 

  25. Jonathan RS (2001) Delaunay Refinement algorithms for Triangulator mesh generation. National Science Foundation under Awards ACI-9875170, CMS-9980063, CMS-9318163, and EIA-9802069

  26. Joseph N, Preetha PP, Narasimhan B (2021) Assessment of environmental flow requirements using a coupled surface water-groundwater model and a flow health tool: A case study of Son river in the Ganga basin. Ecol Indic 121:107110. https://doi.org/10.1016/j.ecolind.2020.107110

    Article  Google Scholar 

  27. Leta OT, El-Kadi AI, Dulai H, Ghazal KA (2016) Assessment of climate change impacts on water balance components of Heeia watershed in Hawaii. J Hydrol Reg Stud 8:182–197. https://doi.org/10.1016/j.ejrh.2016.09.006

    Article  Google Scholar 

  28. Li Q, Unger A, Sudicky E, Kassenaar D, Wexler E, Shikaze S (2008) Simulating the multi-seasonal response of a large-scale watershed with a 3D physically-based hydrologic model. J Hydrol 357(3):317–336. https://doi.org/10.1016/j.jhydrol.2008.05.024

    Article  Google Scholar 

  29. Li Y, Huo A, Liu R, Chen S, Wang X, Li J (2013) Water resources responses to climate changes in Xi’an Heihe River Basin based on SWAT model. Water Resour Res 2:301–330. https://doi.org/10.12677/JWRR.2013.25043

    Article  Google Scholar 

  30. Ma X, Xu J, Luo Y, Aggarwal S, Li J (2009) Response of hydrological processes to land cover and climate changes in Kejie watershed, South-West China. Hydrol Process 23:1179–1191. https://doi.org/10.1002/hyp.7233

    Article  Google Scholar 

  31. Mays L (2001) Water Resources Engineering. First Edition, John Wiley and Sons

  32. Miller JD, Stewart E, Hess T, Brewer T (2020) Evaluating landscape metrics for characterising hydrological response to storm events in urbanised catchments. Urban Water J 17(3):247–258. https://doi.org/10.1080/1573062X.2020.1760320

    Article  Google Scholar 

  33. Mosaed S, Alrashidi BRT (2019) Estimating groundwater recharge for a freshwater lens in an arid region: Formative and stability assessment. Hydrol Process 34(4):1063–1080. https://doi.org/10.1002/hyp.13649

    Article  Google Scholar 

  34. Narasimhan B, Ranjan SR (2000) Electrokinetic barrier to prevent subsurface contaminant migration: theoretical model development and validation. J Contam Hydrol 42:1–17. https://doi.org/10.1016/S0169-7722(99)00089-3

    Article  Google Scholar 

  35. NRSC (2018) Geophysical Products / Land Products. Indian Space Research Organization: Natural Remote Sensing Centre (NRSC) Database. https://nrsc.gov.in/Geophysical_Products. Accessed 21 May 2018

  36. Paul N, Elango L (2018) Predicting future water supply-demand gap with a new reservoir, desalination plant and waste water reuse by water evaluation and planning model for Chennai megacity. India Groundw Sustain Dev 7:8–19. https://doi.org/10.1016/j.gsd.2018.02.005

    Article  Google Scholar 

  37. Pikul MF, Street RL, Remson I (1974) A numerical model based on coupled one-dimensional Richards and Boussinesq equations. Water Resour Res 10(2):295–302. https://doi.org/10.1029/WR010i002p00295

    Article  Google Scholar 

  38. Preetha PP, Al‑Hamdan AZ (2020a) Developing nitrate-nitrogen transport models using remotely-sensed geospatial data of soil moisture profiles and wet depositions. J Environ Sci Health A (online)

  39. Preetha PP, Al‑Hamdan AZ (2020b) Integrating finite-element-model and remote-sensing data into SWAT to estimate transit times of nitrate in groundwater. Hydrogeol J

  40. Sivaraman KR, Thillaigovidarajan S (2012) Chennai River Basin Micro Level Report. http://www.rainwaterharvesting.org. Accessed 22 Nov 2012

  41. TNAU (2018) TNAU Agritech Portal: Downloads. Tamil Nadu Agricultural University, Coimbatore. http://agritech.tnau.ac.in/downloads.html. Accessed 22 Nov 2018

  42. Varli D, Yilmaz K (2018) A multi-scale approach for improved characterization of surface water—groundwater interactions: Integrating thermal remote sensing and in-stream measurements. Water 10(7):854. https://doi.org/10.3390/w10070854

    Article  Google Scholar 

  43. Yang J, Zhang G (2011) Water infiltration in urban soils and its effects on the quantity and quality of runoff. J Soils Sediments 11:751–761. https://doi.org/10.1007/s11368-011-0356-1

    Article  Google Scholar 

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We are grateful to the reviewers and editors for their valuable comments and suggestions, which enhanced this article.

Author information




Conceptualization, Data analysis, and Manuscript development: P.P. Preetha. Data analysis and Manuscript development: N. Joseph. Conceptualization, editing, and review: B. Narasimhan.

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Correspondence to Pooja P. Preetha.

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• This study developed a coupled model to quantify surface and ground water interactions in irrigated river basins

• The SWAT_FEM model improved the hydrological predictions compared to the standalone SWAT model

• A novel approach on how management practices would impact surface water and ground water potential is proposed

• Extreme seasonal ground water fluctuations were predicted with management practices in the basin

• The implications of SWAT_FEM model into ground water assessments and water resources management is evaluated

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Table 2 Sources and details of input data used in the study
Table 3 The inputs needed for modeling the managed conditions using reservoirs
Fig. 8

The agricultural management of (a) standard crop calendar and (b) alternate rice calendar simulated in SWAT model

Fig. 9

Annual water balance components at Poondi reservoir in SWAT model

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Preetha, P.P., Joseph, N. & Narasimhan, B. Quantifying Surface Water and Ground Water Interactions using a Coupled SWAT_FEM Model: Implications of Management Practices on Hydrological Processes in Irrigated River Basins. Water Resour Manage 35, 2781–2797 (2021). https://doi.org/10.1007/s11269-021-02867-7

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  • Stream flow
  • Ground water level
  • Surface water and ground water interaction
  • Chennai River basin
  • FEM
  • SWAT