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Paddy and Water Environment

, Volume 14, Issue 4, pp 499–508 | Cite as

Modification of SWAT auto-calibration for accurate flow estimation at all flow regimes

  • Hyunwoo Kang
  • Jongpil Moon
  • Yongchul Shin
  • Jichul Ryu
  • Dong Hyuk Kum
  • Chunhwa Jang
  • Joongdae Choi
  • Dong Soo Kong
  • Kyoung Jae LimEmail author
Article

Abstract

To secure accuracy in the Soil and Water Assessment Tool (SWAT) simulation for various hydrology and water quality studies, calibration and validation should be performed. When calibrating and validating the SWAT model with measured data, the Nash–Sutcliffe efficiency (NSE) is widely used, and is also used as a goal function of auto-calibration in the current SWAT model (SWAT ver. 2009). However, the NSE value has been known to be influenced by high values within a given dataset, at the cost of the accuracy in estimated lower flow values. Furthermore, the NSE is unable to consider direct runoff and baseflow separately. In this study, the existing SWAT auto-calibration was modified with direct runoff separation and flow clustering calibration, and current and modified SWAT auto-calibration were applied to the Soyanggang-dam watershed in South Korea. As a result, the NSE values for total streamflow, high flow, and low flow groups in direct runoff, and baseflow estimated through modified SWAT auto-calibration were 0.84, 0.34, 0.09, and 0.90, respectively. The NSE values of current SWAT auto-calibration were 0.83, 0.47, −0.14, and 0.90, respectively. As shown in this study, the modified SWAT auto-calibration shows better calibration results than current SWAT auto-calibration. With these capabilities, the SWAT-estimated flow matched the measured flow data well for the entire flow regime. The modified SWAT auto-calibration module developed in this study will provide a very efficient tool for the accurate simulation of hydrology, sediment transport, and water quality with no additional input datasets.

Keywords

Nash–Sutcliffe efficiency Auto-calibration K-means clustering Eckhardt digital filter 

Notes

Acknowledgments

This research was supported by the Geo-Advanced Innovative Action (GAIA) Project (No. 2014000540003, Surface Soil Resources Inventory & Integration: SSORII Research Group) in South Korea.

References

  1. 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(1):73–89CrossRefGoogle Scholar
  2. Bandyopadhyay S, Maulik U (2002) An evolutionary technique based on K-means algorithm for optimal clustering in RN. Inf Sci 146(1–4):221–237CrossRefGoogle Scholar
  3. Chow VT, Maidment DR, Mays LW (1988) Applied hydrology. McGraw-Hill Book Co., New YorkGoogle Scholar
  4. Donigian AS (2000) HSPF training workshop handbook and CD. Lecture #19, Calibration and Verification Issues, Slide #L19-22. EPA Headquarters, Washington Information Center. Presented and prepared for U.S. EPA, Office of Water, Office of Science and Technology, Washington, DCGoogle Scholar
  5. Eckhardt K (2005) How to construct recursive digital filters for baseflow separation. Hydrol Process 19(2):507–515CrossRefGoogle Scholar
  6. Eckhardt K, Arnold JG (2001) Automatic calibration of a distributed catchment model. J Hydrol 251:103–109CrossRefGoogle Scholar
  7. George AB, Raghuwanshi NS, Singh R (2004) Development and testing of a GIS integrated irrigation scheduling model. Agric Water Manag 66(3):221–237CrossRefGoogle Scholar
  8. Lai ZCJ, Huang TJ (2010) Fast global K-means clustering using cluster membership and inequality. Pattern Recognit 43(5):1945–1963CrossRefGoogle Scholar
  9. Lautenbach S, Berlekamp J, Graf N, Seppelt R, Matthies M (2009) Scenario analysis and management options for sustainable river basin management: application of the Elbe DSS. Environ Model Softw 24(1):26–43CrossRefGoogle Scholar
  10. Legates DR, McCabe GJ Jr (1999) Evaluating the use of goodness-of-fit measures in hydrologic and hydroclimatic model validation. Water Resour Res 35(1):233–241CrossRefGoogle Scholar
  11. Lenhart T, Kckhardt K, Fohrer N, Frede HG (2002) Comparison of two different approaches of sensitivity analysis. Phys Chem Earth 27(9–10):645–654CrossRefGoogle Scholar
  12. Lim KJ, Engel BA, Tang Z, Choi J, Kim K, Muthukrishnan S, Tripathy D (2005) Automated web GIS-based hydrograph analysis tool, WHAT. J Am Water Resour Assoc 41(6):1407–1416CrossRefGoogle Scholar
  13. Lim KJ, Park YS, Kim J, Shin YC, Kim NW, Kim SJ, Jeon JH, Engel BA (2010) Development of genetic algorithm-based optimization module in WHAT system for hydrograph analysis and model application. Comput Geosci 36(7):936–944CrossRefGoogle Scholar
  14. MacQueen JB (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley symposium on mathematical statistics and probability, vol 1. University of California Press, pp 281–297Google Scholar
  15. McCuen RH, Knight Z, Cutter AG (2006) Evaluation of the Nash-Sutcliffe efficiency index. J Hydrol Eng 11(6):597–602CrossRefGoogle Scholar
  16. Mitchell GV, Mein RG, McMahon TA (2001) Modeling the urban water cycle. Environ Model Softw 16(7):615–629CrossRefGoogle Scholar
  17. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models: part I. A discussion of principles. J Hydrol 10:282–290CrossRefGoogle Scholar
  18. Ndomba P, Mtalo F, Killingtveit A (2008) SWAT model application in a data scarce tropical complex catchment in Tanzania. Phys Chem Earth 33(8–13):626–632CrossRefGoogle Scholar
  19. Pandit YP, Badhe YP, Sharma BK, Tambe SS, Kulkarni BD (2011) Classification of Indian power coals using K-means clustering and self organizing map neural network. Fuel 90(1):339–347CrossRefGoogle Scholar
  20. Park Y, Kim J, Park J, Jeon JH, Choi DH, Kim T, Choi J, Ahn J, Kim KS, Lim KJ (2007) Evaluation of SWAT applicability to simulate of sediment behaviors at the Imha-Dam watershed. J Korean Soc Water Qual 23(4):467–473Google Scholar
  21. Pisinaras V, Petalas C, Gikas DG, Gemitzi A, Tsihrintzis AV (2010) Hydrological and water quality modeling in a medium-sized basin using the Soil and Water Assessment Tool (SWAT). Desalination 250(1):274–286CrossRefGoogle Scholar
  22. Rutledge AT (1993) Computer programs for describing the recession of groundwater discharge and for estimating mean groundwater recharge and discharge from streamflow records. Water Resources Investigations Report 93-4121Google Scholar
  23. Sloto RA, Crouse MY (1996) HYSEP: a computer program for streamflow hydrograph separation and analysis. US Geological Survey Water-Resources Investigations Report 96-4040Google Scholar
  24. Van Griensven A, Bauwens W (2003) Multi-objective autocalibration for semi-distributed water quality models. Water Resour Res 39(10):1348Google Scholar
  25. Van Griensven A, Meixner AT (2006) Methods to quantify and identify the sources of uncertainty for river basin water quality models. Water Sci Technol 53(1):51–59CrossRefPubMedGoogle Scholar
  26. Van Griensven A, Francos A, Bauwens W (2002) Sensitivity analysis and auto-calibration of an integral dynamic model for river water quality. Water Sci Technol 45(9):325–332PubMedGoogle Scholar
  27. Verbunt M, Zwaaftink GM, Gurtz J (2005) The hydrologic impact of land cover changes and hydropower station in the Alpine Rhine basin. Ecol Model 187(1):71–84CrossRefGoogle Scholar
  28. Winchell M, Srinivasan M, Di Luzio M, Arnold J (2010) ArcSWAT interface for SWAT 2009 user’s guide. Blackland Research Center, Temple, TXGoogle Scholar
  29. Wu K, Johnston AC (2007) Hydrologic response to climatic variability in a great lakes watershed: a case study with the SWAT model. J Hydrol 337(1–2):187–199CrossRefGoogle Scholar
  30. Yoon C, Han J, Jung K, Jang J (2007) Application of BASINS/WinHSPF for pollutant loading estimation in Soyanggang-dam watershed. Korean Soc Limnol 40(2):201–213Google Scholar
  31. Zhou H, Liu Y (2008) Accurate integration of multi-view range image using K-means clustering. J Pattern Recognit Soc 41(1):152–175CrossRefGoogle Scholar

Copyright information

© The International Society of Paddy and Water Environment Engineering and Springer Japan 2015

Authors and Affiliations

  • Hyunwoo Kang
    • 1
  • Jongpil Moon
    • 2
  • Yongchul Shin
    • 3
  • Jichul Ryu
    • 4
  • Dong Hyuk Kum
    • 5
  • Chunhwa Jang
    • 6
  • Joongdae Choi
    • 5
  • Dong Soo Kong
    • 7
  • Kyoung Jae Lim
    • 5
    Email author
  1. 1.Department of Biological Systems EngineeringVirginia TechBlacksburgUSA
  2. 2.Department of Agricultural EngineeringNational Academy of Agricultural ScienceJeonjuSouth Korea
  3. 3.Department of Agricultural Civil EngineeringKyungpook National UniversityDaeguSouth Korea
  4. 4.Department of Water Environment ResearchNational Institute of Environmental ResearchIncheonSouth Korea
  5. 5.Department of Regional Infrastructure EngineeringKangwon National UniversityChuncheonSouth Korea
  6. 6.Department of Agricultural and Biological EngineeringUniversity of illinois at Urbana-ChampaignUrbanaUSA
  7. 7.Department of Life ScienceKyonggi UniversityYeongtong-gu, SuwonSouth Korea

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