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


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.


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



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.


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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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