Abstract
The study attempts to evaluate the impact of DEM source (AW3D30 DEM, CartoDEM v2 R1, SRTM v4.1 DEM and ASTER GDEM v2), DEM resolution (30 m to 1000 m), resampling approaches (nearest neighbor, bilinear interpolation, cubic convolution, majority) and area threshold (1500 Ha, 10,000 Ha, 25,000 Ha, 35,000 Ha, 50,000 Ha) on hydrological model (SWAT) simulated outputs. A methodological framework by two criteria: (1) DEM quality assessment and (2) river network delineation capability of DEM were developed for identifying best DEM among the considered DEMs for baseline scenario. It is found from the study that AW3D30 DEM best represented the terrain of the catchment among the evaluated topographic models with a least RMSE value of 7.44 m. Further AW3D30 DEM had the best river network extraction capability with a minimum RMSE value of 44.52 m in comparison with reference network. All the DEM scenarios were found to be insensitive for surface runoff. Ground water flow, evapotranspiration, potential evapotranspiration and water yield estimates did not show any sensitivity to DEM scenarios but soil water content showed its sensitivity to area threshold scenario. In water quality estimates, all DEM scenarios were found to be highly sensitive to sediment yields in comparison to total nitrogen and total phosphorus.
Similar content being viewed by others
References
Al-Yami M (2014) Analysis and visualisation of digital elevation data for catchment management. Ph.D. thesis, University of East Anglia, University of East Anglia
Arnold JG, Moriasi DN, Gassman PW, Abbaspour KC, White MJ, Srinivasan R, Santhi C, Harmel R, Van Griensven A, Van Liew MW et al (2012) SWA0T: Model use, calibration, and validation. Trans ASABE 55(4):1491–1508
Bloschl G, Bierkens MF, Chambel A, Cudennec C, Destouni G, Fiori A, Kirchner JW, McDonnell JJ, Savenije HH, Sivapalan M et al (2019) Twenty-three unsolved problems in hydrology (UPH) – a community perspective. Hydrol Sci J 64(10):1141–1158
Chaplot V (2005) Impact of DEM mesh size and soil map scale on SWAT runoff, sediment, and NO3-N load predictions. J Hydrol 312(1–4):207–222
Chaubey I, Cotter A, Costello T, Soerens T (2005) Effect of DEM data resolution on SWAT output uncertainty. Hydrol Process 19(3):621–628
Cho S-M, Lee M (2001) Sensitivity considerations when modeling hydrologic processes with digital elevation model. JAWRA J Am Water Resour Assoc 37(4):931–934
Cotter AS, Chaubey I, Costello TA, Soerens TS, Nelson MA (2003) Water quality model output uncertainty as affected by spatial resolution of input data. JAWRA J Am Water Resour Assoc 39(4):977–986
Gonga-Saholiariliva N, Gunnell Y, Petit C, Mering C (2011) Techniques for quantifying the accuracy of gridded elevation models and for mapping uncertainty in digital terrain analysis. Prog Phys Geogr 35(6):739–764
Goyal MK, Panchariya VK, Sharma A, Singh V (2018) Comparative assessment of SWAT model performance in two distinct catchments under various DEM scenarios of varying resolution, sources and resampling methods. Water Resour Manage 32(2):805–825
Her Y, Frankenberger J, Chaubey I, Srinivasan R (2015) Threshold effects in HRU definition of the soil and water assessment tool. Trans ASABE 58(2):367–378
Heuvelink GB (1998) Error propagation in environmental modelling with GIS. CRC Press, Boca Raton
Heuvelink GB (2002) Analysing uncertainty propagation in GIS: why is it not that simple? Uncertainty in remote sensing and GIS, pp 155–165
Kumar B, Lakshmi V, Patra KC (2017) Evaluating the uncertainties in the SWAT model outputs due to DEM grid size and resampling techniques in a large Himalayan river basin. J Hydrol Eng 22(9):04017039
Lin S, Jing C, Coles NA, Chaplot V, Moore NJ, Wu J (2013) Evaluating DEM source and resolution uncertainties in the soil and water assessment tool. Stochast Environ Res Risk Assess 27(1):209–221
Maddalena RL, McKone TE, Hsieh DP, Geng S (2001) Influential input classification in probabilistic multimedia models. Stoch Env Res Risk Assess 15(1):1–17
Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50(3):885–900
Mukherjee S, Joshi P, Mukherjee S, Ghosh A, Garg R, Mukhopadhyay A (2013) Evaluation of vertical accuracy of open source digital elevation model (DEM). Int J Appl Earth Obs Geoinf 21:205–217
Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I - a discussion of principles. J Hydrol 10(3):282–290
Nearing GS, Tian Y, Gupta HV, Clark MP, Harrison KW, Weijs SV (2016) A philosophical basis for hydrological uncertainty. Hydrol Sci J 61(9):1666–1678
O’Callaghan JF, Mark DM (1984) The extraction of drainage networks from digital elevation data. Comput vis Graph Image Process 28(3):323–344
Pai D, Sridhar L, Rajeevan M, Sreejith O, Satbhai N, Mukhopadhyay B (2014) Development of a new high spatial resolution (0.25 × 0.25) long period (1901–2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region. Mausam 65(1):1–18
Prasannakumar V, Shiny R, Geetha N, Vijith H (2011) Applicability of SRTM data for landform characterisation and geomorphometry: a comparison with contour-derived parameters. Int J Digit Earth 4(5):387–401
Renard B, Kavetski D, Kuczera G, Thyer M, Franks SW (2010) Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors. Water Resour Res 46(5)
Srivastava A, Rajeevan M, Kshirsagar S (2009) Development of a high resolution daily gridded temperature data set (1969–2005) for the Indian region. Atmos Sci Lett 10(4):249–254
Tan ML, Ficklin DL, Dixon B, Yusop Z, Chaplot V (2015) Impacts of DEM resolution, source, and resampling technique on SWAT-simulated streamflow. Appl Geogr 63:357–368
Tan ML, Ramli HP, Tam TH (2018) Effect of DEM resolution, source, resampling technique and area threshold on SWAT outputs. Water Resour Manag 32(14):4591–4606
USGS (1998) Standards for digital elevation models. part 3, quality control, national mapping program technical instructions. United states geological survey, p 10. Retrieved from http://nationalmap.gov/standards/demstds.html
Wagener T, Gupta HV (2005) Model identification for hydrological forecasting under uncertainty. Stoch Env Res Risk Assess 19(6):378–387
Wechsler SP (2003) Perceptions of digital elevation model uncertainty by DEM users. URISA-Washington DC 15(2):57–64
Wechsler SP (2007) Uncertainties associated with digital elevation models for hydrologic applications: a review. Hydrol Earth Syst Sci 11(4):1481–1500
Weng Q (2002) Quantifying uncertainty of digital elevation models derived from topographic maps. Advances in spatial data handling. Springer, Berlin, pp 403–418
Wise S (2000) Assessing the quality for hydrological applications of digital elevation models derived from contours. Hydrol Process 14(11–12):1909–1929
Xu F, Dong G, Wang Q, Liu L, Yu W, Men C, Liu R (2016) Impacts of DEM uncertainties on critical source areas identification for nonpoint source pollution control based on SWAT model. J Hydrol 540:355–367
Acknowledgements
The authors would like to acknowledge Science and Engineering Research Board (SERB), India for providing the financial support for this research program vide Project No. ECR/2016/000057.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
None.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
ESM 1
(DOCX 15.5 kb)
Rights and permissions
About this article
Cite this article
Sukumaran, H., Sahoo, S.N. A Methodological Framework for Identification of Baseline Scenario and Assessing the Impact of DEM Scenarios on SWAT Model Outputs. Water Resour Manage 34, 4795–4814 (2020). https://doi.org/10.1007/s11269-020-02691-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11269-020-02691-5