Advertisement

Water Resources Management

, Volume 32, Issue 2, pp 805–825 | Cite as

Comparative Assessment of SWAT Model Performance in two Distinct Catchments under Various DEM Scenarios of Varying Resolution, Sources and Resampling Methods

  • Manish Kumar Goyal
  • Venkatesh K. Panchariya
  • Ashutosh Sharma
  • Vishal Singh
Article

Abstract

The aim of this study is to evaluate the performance of hydrological model - Soil & Water Assessment Tool (SWAT) in two distinct catchments, under various Digital Elevation Model (DEM) scenarios of varying resolution (from 30 m to 300 m), sources (SRTM, ASTER and CartoDEM) and resampling methods (nearest neighbour, bilinear interpolation, majority and cubic convolution) available with ArcGIS software package. A comparison was made in between model response of highly elevated Himalayan Upper Teesta catchment and peninsular monsoon dominated Upper Narmada catchment in India. Model performance was assessed & subsequently compared based on statistical measures such as coefficient of determination (R2) and Nash-Sutcliffe Efficiency (NSE), for monthly runoff and sediment yield. The sensitivity of monthly model outputs of runoff, sediment yield, Total Nitrogen (TN) & Total Phosphorous (TP) towards DEM scenarios was studied based on Relative Difference (RD). The key findings of this study are: 1) topographic characteristics of Upper Teesta catchment were found to be more sensitive towards various DEM scenarios compared with Upper Narmada catchment; 2) model performance in simulating monthly runoff was found to be unaffected for both catchments due to changes in DEM resolution & resampling method; 3) in simulating monthly sediment yield, model performance was affected due to all DEM scenarios for Upper Narmada catchment, while scenarios of changing DEM resolution and resampling method have affected model performance for Upper Teesta catchment.

Keywords

DEM resolution DEM sources DEM resampling methods SWAT model Uncertainty Upper Narmada catchment Upper Teesta catchment 

Notes

Acknowledgements

This present research work has partly been carried out under DST research project no. YSS/2014/000878 and financial support is gratefully acknowledged.

References

  1. Abbaspour KC, Rouholahnejad E, Vaghefi S et al (2015) A continental-scale hydrology and water quality model for Europe: calibration and uncertainty of a high-resolution large-scale SWAT model. J Hydrol 524:733–752.  https://doi.org/10.1016/j.jhydrol.2015.03.027 CrossRefGoogle Scholar
  2. Arnold JG, Srinivasan R, Muttiah RS, Williams JR (1998) Large area hydrologic modeling and assessment part I: model development. J Am Water Resour Assoc 34:73–89CrossRefGoogle Scholar
  3. Arnold JG, Moriasi DN, Gassman PW et al (2012) SWAT: model use, calibration, and validation. Am Soc Agric. Biol Eng 55:1491–1508Google Scholar
  4. Booij MJ (2005) Impact of climate change on river flooding assessed with different spatial model resolutions. J Hydrol 303:176–198.  https://doi.org/10.1016/j.jhydrol.2004.07.013 CrossRefGoogle Scholar
  5. Bormann H (2008) Sensitivity of a soil-vegetation-atmosphere-transfer scheme to input data resolution and data classification. J Hydrol 351:154–169.  https://doi.org/10.1016/j.jhydrol.2007.12.011 CrossRefGoogle Scholar
  6. Casper AF, Dixon B, Earls J, Gore JA (2011) Linking a spatially explicit watershed model (SWAT) with an in-stream fish habitat model (PHABSIM): a case study of setting minimum flows and levels in a low gradient, sub-tropical river. River Res Appl 27:269–282.  https://doi.org/10.1002/rra.1355 CrossRefGoogle Scholar
  7. Chaplot V (2005) Impact of DEM mesh size and soil map scale on SWAT runoff, sediment, and NO3-N loads predictions. J Hydrol 312:207–222.  https://doi.org/10.1016/j.jhydrol.2005.02.017 CrossRefGoogle Scholar
  8. Chaubey I, Cotter AS, Costello TA, Soerens TS (2005) Effect of DEM data resolution on SWAT output uncertainty. Hydrol Process 19:621–628.  https://doi.org/10.1002/hyp.5607 CrossRefGoogle Scholar
  9. Chow VT, Maidment DR, Mays LW (2010) Applied hydrology, 2010th edn. McGraw Hill Education (India) Private Limited, BengaluruGoogle Scholar
  10. CWC & NRSC (2014) Narmada Basin. National Remote Sensing Center (NRSC). ISRO, HyderabadGoogle Scholar
  11. Darboux F, Gascuel-Odoux C, Davy P (2002) Effects of surface water storage by soil roughness on overland-flow generation. Earth Surf Process Landforms 27:223–233.  https://doi.org/10.1002/esp.313 CrossRefGoogle Scholar
  12. Deshpande RD, Gupta SK (2013) Groundwater helium: an indicator of active tectonic regions along Narmada River, central India. Chem Geol 344:42–49.  https://doi.org/10.1016/j.chemgeo.2013.02.020 CrossRefGoogle Scholar
  13. Di Luzio M, Arnold JG, Srinivasan R (2005) Effect of GIS data quality on small watershed stream flow and sediment simulations. Hydrol Process 19:629–650.  https://doi.org/10.1002/hyp.5612 CrossRefGoogle Scholar
  14. Dixon B, Earls J (2009) Resample or not?! Effects of resolution of DEMs in watershed modeling B. Hydrol Process 23:1714–1724.  https://doi.org/10.1002/hyp CrossRefGoogle Scholar
  15. Dubey A, Kant D, Singh O, Pandey RP (2013) A comparative study of environmental flow requirement approaches using hydrological index methods. J Indian Water Resour Soc 33:20–27Google Scholar
  16. Florinsky IV, Kuryakova GA (2000) Determination of grid size for digital terrain modelling in landscape investigations—exemplified ed by soil moisture distribution at a micro-scale. Int J Geogr Inf Sci 14:815–832.  https://doi.org/10.1080/13658816.2014.908472 CrossRefGoogle Scholar
  17. Gassman PW, Reyes MR, Green CH, Arnold JG (2007) The soil and water assessment tool: historical development, applications, and future research directions. Trans ASABE 50:1211–1250.  10.13031/2013.23637 CrossRefGoogle Scholar
  18. Goodchild M (1993) Data models and data quality: problems and prospects. In: Goodchild MF, Parks BO, Steyaert LT (eds) Visualization in geographical information systems. John Wiley, New York, pp 94–104Google Scholar
  19. Jha M, Gassman PW, Secchi S et al (2004) Effect of watershed subdivision on SWAT flow, sediment, and nutrient predictions. J Am Water Resour Assoc 40:811–825.  https://doi.org/10.1111/j.1752-1688.2004.tb04460.x CrossRefGoogle Scholar
  20. Khare D, Patra D, Mondal A, Kundu S (2015) Impact of landuse/land cover change on run-off in a catchment of Narmada river in India. Appl Geomatics 7:23–35.  https://doi.org/10.1007/s12518-014-0148-6 CrossRefGoogle Scholar
  21. Kim J, Noh J, Son K, Kim I (2012) Impacts of GIS data quality on determination of runoff and suspended sediments in the Imha watershed in Korea. Geosci J 16:181–192.  https://doi.org/10.1007/s12303-012-0013-8 CrossRefGoogle Scholar
  22. Krause P, Boyle DP (2005) Advances in geosciences comparison of different efficiency criteria for hydrological model assessment. Adv Geosci 5:89–97.  https://doi.org/10.5194/adgeo-5-89-2005 CrossRefGoogle Scholar
  23. Lacroix MP, Martz LW, Kite GW, Garbrecht J (2002) Using digital terrain analysis modeling techniques for the parameterization of a hydrologic model. Environ Model Softw 17:127–136.  https://doi.org/10.1016/S1364-8152(01)00042-1 CrossRefGoogle Scholar
  24. Le Coz M, Delclaux F, Genthon P, Favreau G (2009) Assessment of digital elevation model (DEM) aggregation methods for hydrological modeling: Lake Chad basin, Africa. Comput Geosci 35:1661–1670.  https://doi.org/10.1016/j.cageo.2008.07.009 CrossRefGoogle Scholar
  25. Lin S, Jing C, Coles NA et al (2013) Evaluating DEM source and resolution uncertainties in the soil and water assessment tool. Stoch Environ Res Risk Assess 27:209–221.  https://doi.org/10.1007/s00477-012-0577-x CrossRefGoogle Scholar
  26. Mandal D, Sharda VN (2011) Assessment of permissible soil loss in India employing a quantitative bio-physical model. Curr Sci 100:383–390Google Scholar
  27. Meetei LI, Pattanayak SK, Bhaskar A et al (2007) Climatic imprints in quaternary valley fill deposits of the middle Teesta valley, Sikkim Himalaya. Quat Int 159:32–46.  https://doi.org/10.1016/j.quaint.2006.08.018 CrossRefGoogle Scholar
  28. Moore ID, Grayson RB, Ladson a R (1991) Digital terrain modeling : a review of hydrological geomorphological and biological applications. Hydrol Process 5:3–30.  https://doi.org/10.1002/hyp.3360050103 CrossRefGoogle Scholar
  29. Moriasi DN, Arnold JG, Van Liew MW et al (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50:885–900.  10.13031/2013.23153 CrossRefGoogle Scholar
  30. Muralikrishnan S, Pillai A, Narender B et al (2013) Validation of Indian national DEM from Cartosat-1 data. J Indian Soc. Remote Sens 41:1–13.  https://doi.org/10.1007/s12524-012-0212-9 Google Scholar
  31. Ndomba PM, Birhanu BZ (2008) Problems and prospects of SWAT model applications in NILOTIC catchments: a review. Nile Basin Water Eng Sci Mag 1:41–52Google Scholar
  32. Neitsch S, Arnold J, Kiniry J, Williams J (2011) Soil & water assessment tool theoretical documentation version 2009. Texas Water Resour Institute TR-406:1–647Google Scholar
  33. NRSC (2011) Evaluation of Indian National DEM from Cartosat-1 Data, Summary Report (Ver. 1). Indian Space Research Organisation, National Remote Sensing Center, Hyderabad, pp 1–19Google Scholar
  34. Patil RJ, Sharma SK, Tignath S (2014) Remote sensing and GIS based soil erosion assessment from an agricultural watershed. Arab J Geosci 8:6967–6984.  https://doi.org/10.1007/s12517-014-1718-y CrossRefGoogle Scholar
  35. Pullar D, Springer D (2000) Towards integrating GIS and catchment models. Environ Model Softw 15:451–459.  https://doi.org/10.1016/S1364-8152(00)00023-2 CrossRefGoogle Scholar
  36. Rajeevan M, Bhate J (2009) A high resolution daily gridded rainfall dataset (1971–2005) for mesoscale meteorological studies. Curr Sci 96:558–562Google Scholar
  37. Refsgaard JC, Storm B (1996) Construction, Calibration And Validation of Hydrological Models. In: Abbott MB, Refsgaard JC (eds) Distributed Hydrological Modelling. Water Sci Technol Lib 22:41–54. Springer, Dordrecht.  https://doi.org/10.1007/978-94-009-0257-2_3
  38. Rifman S (1973) Digital rectification of ERTS multispectral imagery. In: Symp. Significant results obtained from ERTS-1. NASA, United States, pp 1131–1142Google Scholar
  39. Robinson N, Regetz J, Guralnick RP (2014) EarthEnv-DEM90: a nearly-global, void-free, multi-scale smoothed, 90m digital elevation model from fused ASTER and SRTM data. ISPRS J Photogramm Remote Sens 87:57–67.  https://doi.org/10.1016/j.isprsjprs.2013.11.002 CrossRefGoogle Scholar
  40. Santhi C, Arnold JG, Williams JR et al (2001) Validation of the SWAT model on a large river basin with point and nonpoint sources. J Am Water Resour Assoc 37:1169–1188.  https://doi.org/10.1111/j.1752-1688.2001.tb03630.x CrossRefGoogle Scholar
  41. Sharma A, Tiwari KN, Bhadoria PBS (2009) Measuring the accuracy of contour interpolated digital elevation models. J Indian Soc Remote Sens 37:139–146.  https://doi.org/10.1007/s12524-009-0005-y CrossRefGoogle Scholar
  42. Sharma A, Tiwari KN, Bhadoria PBS (2011) Determining the optimum cell size of digital elevation model for hydrologic application. J Earth Syst Sci 120:573–582.  https://doi.org/10.1007/s12040-011-0092-3 CrossRefGoogle Scholar
  43. Singh V, Goyal MK (2016a) Changes in climate extremes by the use of CMIP5 coupled climate models over eastern Himalayas. Environ Earth Sci 75:1–27.  https://doi.org/10.1007/s12665-016-5651-0 CrossRefGoogle Scholar
  44. Singh V, Goyal MK (2016b) Analysis and trends of precipitation lapse rate and extreme indices over north Sikkim eastern Himalayas under CMIP5ESM-2M RCPs experiments. Atmos Res 167:34–60.  https://doi.org/10.1016/j.atmosres.2015.07.005 CrossRefGoogle Scholar
  45. Subash N, Sikka AK (2014) Trend analysis of rainfall and temperature and its relationship over India. Theor Appl Climatol 117:449–462.  https://doi.org/10.1007/s00704-013-1015-9 CrossRefGoogle Scholar
  46. Sui DZ, Maggio RC (1999) Integrating GIS with hydrological modeling: practices, problems, and prospects. Comput Environ Urban Syst 23:33–51.  https://doi.org/10.1016/S0198-9715(98)00052-0 CrossRefGoogle Scholar
  47. Tan ML, Ficklin DL, Dixon B et al (2015) Impacts of DEM resolution, source, and resampling technique on SWAT-simulated streamflow. Appl Geogr 63:357–368.  https://doi.org/10.1016/j.apgeog.2015.07.014 CrossRefGoogle Scholar
  48. US-EPA (2003) National Management Measures for the control of non-point pollution from agriculture. U.S. Environmental Protection Agency, Washington DCGoogle Scholar
  49. Wechsler SP (2007) Uncertainties associated with digital elevation models for hydrologic applications : a review. Hydrol Earth Syst Sci:1481–1500Google Scholar
  50. Wu S, Li J, Huang GH (2008) A study on DEM-derived primary topographic attributes for hydrologic applications: sensitivity to elevation data resolution. Appl Geogr 28:210–223.  https://doi.org/10.1016/j.apgeog.2008.02.006 CrossRefGoogle Scholar
  51. Xu F, Dong G, Wang Q et al (2016) Impacts of DEM uncertainties on critical source areas identification for non-point source pollution control based on SWAT model. J Hydrol 540:355–367.  https://doi.org/10.1016/j.jhydrol.2016.06.019 CrossRefGoogle Scholar
  52. Zhang P, Liu R, Bao Y et al (2014) Uncertainty of SWAT model at different DEM resolutions in a large mountainous watershed. Water Res 53:132–144.  https://doi.org/10.1016/j.watres.2014.01.018 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  • Manish Kumar Goyal
    • 1
  • Venkatesh K. Panchariya
    • 1
  • Ashutosh Sharma
    • 1
  • Vishal Singh
    • 1
  1. 1.Department of Civil EngineeringIndian Institute of TechnologyGuwahatiIndia

Personalised recommendations