Natural Hazards

, Volume 91, Issue 1, pp 179–199 | Cite as

Hydrological modeling of storm runoff and snowmelt in Taunton River Basin by applications of HEC-HMS and PRMS models

Original Paper


Reliable predictions of storm runoff from rainfall and snowmelt are important for flood hazard mitigation and resilience. In this study, the HEC-HMS and PRMS hydrological models have been applied to simulate storm runoff in Taunton River Basin for the storm event in 2010 when maximum rainfall intensity reached approximate 5 in/day in March, and the snowfall reached about 11 inches in December. Model parameters were calibrated, and model performance was evaluated by comparing model-simulated daily stream flow with observations. For the rainstorm period during March–April, results indicate that both HEC-HMS and PRMS provide very good predictions of rainfall runoff with the correlation values above 0.95, and PRMS produces lower root-mean-square errors than those from HEC-HMS. Over the 12-month period including the snowfall in December, the time series of flow by PRMS match better with observations than those from the HEC-HMS. The 12-month overall correlation values for HEC-HMS and PRMS are 0.91 and 0.97 at Bridgewater Station, and 0.89 and 0.97 at Threemile Station, respectively. For an extreme storm scenario of the maximum historical 36.7-inch snowfall in early February in combination with the rainstorm in March–April of 2010, model simulations indicate that the flow would substantially increase by about 50% or more. Comparisons between HEC-HMS and RPMS models have been analyzed to provide references for storm runoff modeling.


Storm runoff modeling Snowmelt HEC-HMS PRMS Taunton River 



This study is supported in part by the Coastal Resilience Center, Department of Homeland Security, through the University of North Carolina. The authors also thank the advices for snowmelt modeling in HEC-HMS by Tom Brauer and Mike Bartles at Hydrologic Engineering Center of U.S. Army Corps of Engineers.


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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Hydraulic Engineering, College of Civil EngineeringTongji UniversityShanghaiChina
  2. 2.Department of Civil and Environmental EngineeringFlorida A&M University - Florida State UniversityTallahasseeUSA
  3. 3.School of OcenographyUniversity of Rhode IsalndNarraganssetUSA

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