Environmental Science and Pollution Research

, Volume 26, Issue 9, pp 8971–8991 | Cite as

HSPF-based watershed-scale water quality modeling and uncertainty analysis

  • Maryam Roostaee
  • Zhiqiang DengEmail author
Research Article


This paper presents findings on uncertainties, introduced through digital elevation model (DEM) resolution and DEM resampling, in watershed-scale flow and water quality (NO3, P, and total suspended sediment) simulations. The simulations were performed using the Better Assessment Science Integrating Point and Nonpoint Sources/Hydrological Simulation Program Fortran watershed modeling system for two representative study watersheds delineated with both the original DEMs of four different resolutions (including 3.5, 10, 30, and 100 m) and the resampled DEMs of three different resolutions (including 10, 30, and 100 m), creating 14 simulation scenarios. Parameter uncertainties were quantified by means of the GLUE approach and compared to input data uncertainties. Results from the 14 simulation scenarios showed that there was a common increasing trend in errors of simulated flow and water quality parameters when the DEM resolution became coarser. The errors involved in the watershed with a mild slope were found to be substantially (up to 10 times) greater than those of the other watershed with a relatively steep slope. It was also found that sediment was the most sensitive and NO3 was the least sensitive parameters to the variation in DEM resolution, as evidenced by the maximum normalized root mean square error (NRMSE) of 250% in the simulated sediment concentration and 11% in the simulated NO3 concentration, respectively. Moreover, results achieved from the resampled (particularly coarser) DEMs were significantly different from corresponding ones from original DEMs. By comparing uncertainties from different sources, it was found that the parameter-induced uncertainties were higher than the resolution-induced uncertainties particularly in simulated NO3 and P concentrations for studied watersheds. The findings provide new insights into the sensitivity and uncertainty of water quality parameters and their simulation results, serving as the guidelines for developing and implementing water quality management and watershed restoration plans.


BASINS DEM resampling DEM resolution GLUE Sensitivity analysis Uncertainty analysis 



The material is based upon the work supported by the US Geological Survey and Louisiana Water Resources Research Institute.


  1. Al-Abed NA, Whiteley HR (2002) Calibration of the Hydrological Simulation Program Fortran (HSPF) model using automatic calibration and geographical information systems. Hydrol Process 16:3169–3188. CrossRefGoogle Scholar
  2. Beven K, Binley A (1992) The future of distributed models: model calibration and uncertainty prediction. Hydrol Process 6:279–298. CrossRefGoogle Scholar
  3. Chaplot V (2014) Impact of spatial input data resolution on hydrological and erosion modeling: recommendations from a global assessment. Phys Chem Earth 67–69:23–35. CrossRefGoogle Scholar
  4. Chaubey I, Cotter AS, Costello TA, Soerens TS (2005) Effect of DEM data resolution on SWAT output uncertainty. Hydrol Process 19:621–628CrossRefGoogle Scholar
  5. Cotter AS, Chaubey I, Costello TA, Soerens TS, Nelson MA (2003) Water quality model output uncertainty as affected by spatial resolution of input dateGoogle Scholar
  6. Dixon B, Earls J (2009) Resample or not?! Effects of resolution of DEMs in watershed modeling. Hydrol Process 23:1714–1724CrossRefGoogle Scholar
  7. Donigian AS (2002) Watershed model calibration and validation: the HSPF experience. Proc Water Environ Fed 2002:44–73CrossRefGoogle Scholar
  8. Duda P, Hummel PR, Donigian AS, Imhoff JC (2012) BASINS/HSPF: model use, calibration, and validation. Trans ASABE 55:1523–1547. CrossRefGoogle Scholar
  9. Earls J, Dixon B (2005) A comparative study of the effects of input resolution on the SWAT model. WIT Trans Ecol Environ 83.
  10. Fonseca A, Ames DP, Yang P, Botelho C, Boaventura R, Vilar V (2014) Watershed model parameter estimation and uncertainty in data-limited environments. Environ Model Softw 51:84–93. CrossRefGoogle Scholar
  11. Freer J, Beven K, Ambroise B (1996) Bayesian estimation of uncertainty in runoff prediction and the value of data: an application of the GLUE approach. Water Resour Res 32(7):2161–2173. CrossRefGoogle Scholar
  12. Freni G, Mannina G, Viviani G (2009) Uncertainty assessment of an integrated urban drainage model. J Hydrol 373:392–404. CrossRefGoogle Scholar
  13. Gong Y, Shen Z, Hong Q, Liu R, Liao Q (2011) Parameter uncertainty analysis in watershed total phosphorus modeling using the GLUE methodology. Agric Ecosyst Environ 142:246–255. CrossRefGoogle Scholar
  14. Iskra I, Droste R (2008) Parameter uncertainty of a watershed model. Can Water Resour J 33:5–22. CrossRefGoogle Scholar
  15. Lenhart T, Eckhardt K, Fohrer N, Frede H-G (2002) Comparison of two different approaches of sensitivity analysis. Phys Chem Earth Parts A/B/C 27:645–654. CrossRefGoogle Scholar
  16. Li Z, Liu H, Luo C, Li Y, Li H, Pan J, Jiang X, Zhou Q, Xiong Z (2015) Simulation of runoff and nutrient export from a typical small watershed in China using the Hydrological Simulation Program–Fortran. Environ Sci Pollut Res 22:7954–7966. CrossRefGoogle Scholar
  17. 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. Stoch Env Res Risk A 27:209–221CrossRefGoogle Scholar
  18. Liu Z, Tong STY (2011) Using HSPF to model the hydrologic and water quality impacts of riparian land-use change in a small watershed. J Environ Inform 17:1–14. CrossRefGoogle Scholar
  19. Luo C, Li Z, Wu M, Jiang K, Chen X, Li H (2017) Comprehensive study on parameter sensitivity for flow and nutrient modeling in the Hydrological Simulation Program Fortran model. Environ Sci Pollut Res 24:20982–20994. CrossRefGoogle Scholar
  20. Mishra A, Kar S, Singh V (2007) Determination of runoff and sediment yield from a small watershed in sub-humid subtropics using the HSPF model. Hydrol. ProcessGoogle Scholar
  21. Moriasi D, Arnold J, Van Liew M, Bingner R (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50(3):885–900. CrossRefGoogle Scholar
  22. Moriasi D, Gitau M, Pai N, Daggupati P (2015) Hydrologic and water quality models: performance measures and evaluation criteria. Trans ASABE 58(6):1763–1785CrossRefGoogle Scholar
  23. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol 10:282–290. CrossRefGoogle Scholar
  24. Patil A, Deng Z-Q (2012) Input data measurement-induced uncertainty in watershed modelling. Hydrol Sci J 57:118–133. CrossRefGoogle Scholar
  25. Roostaee M, Deng Z (2018) Uncertainty analysis of watershed-based flow and water quality modelling with different DEM data sources. HIC 2018. 13thGoogle Scholar
  26. 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 63:357–368CrossRefGoogle Scholar
  27. Wang H, Wu Z, Hu C (2015) A comprehensive study of the effect of input data on hydrology and non-point source pollution modeling. Water Resour ManagGoogle Scholar
  28. 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 28:210–223CrossRefGoogle Scholar
  29. Zhang J, Ross M (2012) Hydrologic simulation of clay-settling areas in the phosphate mining district, Florida. Hydrol Process 26:3770–3778. CrossRefGoogle Scholar
  30. Zhang P, Liu R, Bao Y, Wang J, Yu W, Shen Z (2014) Uncertainty of SWAT model at different DEM resolutions in a large mountainous watershed. Water Res 53:132–144CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Civil and Environmental EngineeringLouisiana State UniversityBaton RougeUSA
  2. 2.Department of Civil and Environmental EngineeringLouisiana State UniversityBaton RougeUSA

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