Water Resources Management

, Volume 23, Issue 2, pp 303–324 | Cite as

Hourly Analyses of Hydrological and Water Quality Simulations Using the ESWAT Model



Detailed analyses of hydrological and water quality variables are very important to study the dynamic processes in a river basin. In this study, we have further modified the Enhanced Soil and Water Assessment Tool (ESWAT) model by incorporating hourly evapotranspiration and overland flow routing modules. Results from comparison of the performances by two ESWAT versions indicate that the modified version performed better than the original model. The modified ESWAT model has reasonably reproduced observed time series runoff and most commonly collected water quality data. In addition, input data availability at required spatial and temporal resolutions is the major bottleneck in implementing many detailed hydrological models. In this paper, we have also developed a robust methodology to successfully disaggregate daily rainfall data into hourly datasets. Furthermore, we have assessed the implications of such daily rainfall disaggregation schemes on subsequent simulation of hydrological and water quality variables at river basin level. The outcomes suggest that the multivariate rainfall disaggregation scheme better reproduced observed rainfall and runoff data.


ESWAT Multivariate Rainfall disaggregation RWQM SWAT 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Abulohom MS, Shah SMS, Ghumman AR (2001) Development of a rainfall–runoff model, its calibration and validation. Water Resour Manag 15(3):149–163CrossRefGoogle Scholar
  2. Allen RG, Periera LS, Raes D, Smith M (1998) Crop evapotranspiration; guidelines for computing crop water requirements. FAO Irrig Drain Pap 56. Rome, Italy, p 300Google Scholar
  3. Arnold JG, Williams JR, Srinivasan R, King KW (1996) SWAT manual. USDA agricultural service and Blackland Research Center, Temple, TexasGoogle Scholar
  4. Brown LC, Barnwell TO (1987) The enhanced stream water quality models QUAL2E and QUAL2E-UNCAS: user manual. Report EPA/600/3-87/007, EPA, Athens, GeorgiaGoogle Scholar
  5. Cao W, Bowden WB, Davie T, Fenemor A (2008) Modeling impacts of land cover change on critical water resources in the Motueka River Catchment, New Zealand. Water Resour Manag. doi:10.1007/s11269-008-9268-2
  6. Debele B (2005) Better insight into water resources management through integrated upland watershed and downstream waterbody hydrodynamic and water quality models (SWAT & CE-QUAL-W2). Ph.D. dissertation, Cornell University, Ithaca, NY, p 175Google Scholar
  7. Debele B, Srinivasan R, Parlange JY (2007) Accuracy evaluation of weather data generation and disaggregation methods at finer timescales. Adv Water Resour 30(5):1286–1300CrossRefGoogle Scholar
  8. Frederick RH, Myers VA, Auciello EP (1977) Five to sixty minute precipitation frequency for the Eastern and Central United States. U. S. Dep. Commerce, National Weather Service, National Oceanic and Atmospheric Administration Tech. Memo NWS HYDRO 35, Silver Spring, MD, p 36Google Scholar
  9. Fronteau C, van Griensven A, Bauwens W (1999) Construction and calibration of an integrated urban drainage model. In: Proceedings of the 5th international conference on water pollution, Lemnos, Greece May 24–26Google Scholar
  10. Henze M, Gujer W, Marais GVR, Matsuo T, Mino T, Wentzel C (1995) Activated sludge model No. 2 IAWPRC scientific and technical reports, 3, London, U.K.Google Scholar
  11. Hershernhorn J, Woolhiser DA (1987) Disaggregation of daily rainfall. J Hydrol 95:299–322CrossRefGoogle Scholar
  12. Hershfield DM (1961) Rainfall frequency atlas of the United States for durations from 30 minutes to 24 hours and return periods from 1 to 100 years. U.S. Dep. Commerce, Weather Bureau Technical Paper No. 40. Washington, DC, p 115Google Scholar
  13. Holvoet K, Gevaert V, Griensven A, Seuntjens P, Vanrolleghem PA (2007) Modeling the effectiveness of agricultural measures to reduce the amount of pesticides entering surface waters. Water Resour Manag 21(12):2027–2035CrossRefGoogle Scholar
  14. Ireson A, Makropoulos C, Maksimovic C (2006) Water resources modeling under data scarcity: coupling MIKE BASIN and ASM groundwater model. Water Resour Manag 20(4):567–590CrossRefGoogle Scholar
  15. Koutsoyiannis D (2001) Coupling stochastic models of different time scales. Water Resour Res 37(2):379–392CrossRefGoogle Scholar
  16. Koutsoyiannis D, Onof C (2001) Rainfall disaggregation using adjusting procedures on a Poisson cluster model. J Hydrol 246:109–122CrossRefGoogle Scholar
  17. Koutsoyiannis D, Onof C, Wheater HS (2001) Stochastic disaggregation of spatial–temporal rainfall with limited data. 26th General assembly of the European geophysical society, Geophysical research abstracts, vol. 3 NiceGoogle Scholar
  18. Koutsoyiannis D, Onof C, Wheater HS (2003) Multivariate rainfall disaggregation at a fine time scale. Water Resour Res 39(7):1–18CrossRefGoogle Scholar
  19. Masliev I, Somlyody L, Koncsos L (1995) On reconciliation of traditional water quality models and activated sludge models. Working paper WP-95-18, IIASA, Laxenburg, AustriaGoogle Scholar
  20. Mishra A, Kar S, Singh VP (2007) Prioritizing structural management by quantifying the effect of land use and land cover on watershed runoff and sediment yield. Water Resour Manag 21(11):1899–1913CrossRefGoogle Scholar
  21. Nash JE (1958) The form of the instantaneous unit hydrograph, vol. III. IUGG Gen. Assem. Toronto, IAHS Publ. no. 45, pp 114–121Google Scholar
  22. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models. I. A discussion of principles. J Hydrol 10(3):282–290CrossRefGoogle Scholar
  23. Neitsch SL, Arnold JG, Kiniry JR, Williams JR (2001) Soil and water assessment tool (SWAT) theoretical documentation. Blackland Research Center, Texas Agricultural Experiment Station, Temple, Texas, p 781Google Scholar
  24. Rauch W, Bertrand-Krajewski JL, Krebs P, Mark O, Schilling W, Schütze M, Vanrolleghem PA (2002) Deterministic modeling of integrated urban drainage systems. Water Sci Technol 45(3):81–94Google Scholar
  25. Reichert P, Borchardt D, Henze M, Koncsos L, Rauch W, Shanahan P, Slomyody L, Vanrolleghem P (2001) River water quality model (RWQM) No. 1: II. Biochemical process equations. Water Sci Technol 43(5):11–30Google Scholar
  26. Robertson DM, Roerish ED (1999) Influence of various water quality sampling strategies on load estimates for small streams. Water Resour Res 35(12):3747–3759CrossRefGoogle Scholar
  27. Robinson RB, Wood MS, Smoot JL, Moore SE (2004) Parametric modeling of water quality and sampling strategy in a high-altitude Appalachian stream. J Hydrol 287:62–73CrossRefGoogle Scholar
  28. Runkel RL, Crawford CG, Cohn TA (2004) Load estimator (LOADEST): a FORTRAN program for estimating constituent loads in streams and rivers. Techniques and methods Book 4, Chapter A5, USGS, Reston, VA, p 75Google Scholar
  29. Shanahan P, Henze M, Koncsos L, Rauch W, Reichert P, Slomyody L, Vanrolleghem P (1998) River water quality modeling: II. Problems of the art. Water Sci Technol 43(5):11–30Google Scholar
  30. Shanahan P, Borchardt D, Henze M, Rauch W, Reichert P, Slomyody L, Vanrolleghem P (2001) River water quality model no. 1 (RWQM1): I. Modeling approach. Water Sci Technol 43(5):1–9Google Scholar
  31. Sharpley AN, Williams JR (1990) EPIC—erosion productivity impact calculator. Model documentation, U.S. Department of Agriculture, Agricultural Research Service, Tech. Bull, p 1768Google Scholar
  32. Sincock AM, Wheater HS, Whitehead PG (2003) Calibration and sensitivity analysis of a river water quality model under unsteady flow conditions. J Hydrol 277:214–229CrossRefGoogle Scholar
  33. Socolofsky S, Adams EE, Entekhabi D (2001) Disaggregation of daily rainfall for continuous watershed modeling. J Hydrol Eng 6(4):300–309, ASCECrossRefGoogle Scholar
  34. Szilagyi J (2003) State–space discretization of the Kalinin–Milyukov_Nash–Cascade in a sample-data system framework for stream flow forecasting. J Hydrol Eng 8(6):339–347, ASCECrossRefGoogle Scholar
  35. United States Environmental Protection Agency (2000) National water quality inventory report. US EPA, Washington, DCGoogle Scholar
  36. United States Soil Conservation Service (1986) Technical release 55: urban hydrology for small watersheds. USDA, NRCS, Conservation Engineering DivisionGoogle Scholar
  37. Vandenberghe V, Van Griensven A, Bauwens W (2001) Sensitivity analysis and calibration of the parameters of ESWAT: application to the River Dender. Water Sci Technol 43(7):295–300Google Scholar
  38. Van Griensven A (2002) Developments towards integrated water quality modeling for river basins. Ph.D. dissertation, Vrije Universiteit Brussel, Belgium, p 280Google Scholar
  39. Van Griensven A, Bauwens W (2001) Integral water quality modeling of catchments. Water Sci Technol 43(7):321–328Google Scholar
  40. Van Griensven A, Bauwens W (2003) Multiobjective autocalibration for semidistributed water quality models. Water Resour Res 39(12):1348–1356CrossRefGoogle Scholar
  41. Van Griensven A, Francos A, Bauwens W (2001) Sensitivity analysis and calibration of an integral dynamic model for river water quality. Water Sci Technol 45(5):321–328Google Scholar
  42. Waichler SR, Wigmosta MS (2003) Development of hourly meteorological values from daily data and significance to hydrological modeling at H. J. Andrews Experimental Forest. J Hydrometeorol 4(2):251–263CrossRefGoogle Scholar
  43. White KL, Chaubey I (2005) Sensitivity analysis, calibration and validations for a multisite and multivariable SWAT model. J Am Water Resour Assoc 41(5):1077–1089CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • Bekele Debele
    • 1
    • 2
  • R. Srinivasan
    • 3
  • J-Yves Parlange
    • 1
  1. 1.Department of Biological and Environmental EngineeringCornell UniversityIthacaUSA
  2. 2.Silver SpringUSA
  3. 3.Spatial Sciences LaboratoryTexas A&M UniversityCollege StationUSA

Personalised recommendations