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Improved Runoff Estimation Using Event-Based Rainfall-Runoff Models

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Abstract

Event-based rainfall-runoff models are effective tools in operational hydrological forecasting and preparedness for extreme events. In the current study, the popular Natural Resources Soil Conservation curve number (NRCS-CN) model and the proposed simple nonlinear models were employed for runoff estimation. The runoff prediction capability of the NRCS model for the CN values obtained from tables was very poor in comparison to those calculated from the measured rainfall-runoff (storm-events) data. The proposed models were calibrated based on the rank-order, measured rainfall-runoff data (1,005 events) from 25 watersheds and validated in six watersheds for runoff estimation (170 events). The quantitative models’ performances were evaluated and compared based on the root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and percent bias (PBIAS). Using tabulated CNs, the NRCS model exhibited comparatively insignificant results in the maximum number of watersheds (high RMSE, low NSE, and statistically poor PBIAS values). Using storm-event based calibrated CNs, the NRCS model showed improvement for runoff estimation. Furthermore, the proposed models without the CN concept were superior (with comparatively low RMSE, high NSE, and statistically significant PBIAS values) for depicting improved performance in almost all of the watersheds.

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Acknowledgments

This research was supported by a grant from the Construction Technology Innovation Program (11CTIPC06-Development of Korean Advanced Technology for Hydrologic Analysis) funded by the Ministry of Land, Infrastructure, and Transport (MLIT) of Korea. Special thanks to the Hydrological Survey Center (HSC) of Korea for providing measured data of stream flow.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Hanyang University, Seoul, 133-791, Republic of Korea

    Muhammad Ajmal & Muhammad Waseem

  2. Department of Agricultural Engineering, University of Engineering and Technology, Peshawar, 25120, Pakistan

    Muhammad Ajmal

  3. Department of Civil Engineering, Seokyeong University, Seoul, 136-704, Republic of Korea

    Jae-Hyun Ahn

  4. Department of Civil and Environmental Engineering, Hanyang University, Ansan, 426-791, Republic of Korea

    Tae-Woong Kim

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  1. Muhammad Ajmal
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  2. Muhammad Waseem
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  3. Jae-Hyun Ahn
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  4. Tae-Woong Kim
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Correspondence to Tae-Woong Kim.

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Ajmal, M., Waseem, M., Ahn, JH. et al. Improved Runoff Estimation Using Event-Based Rainfall-Runoff Models. Water Resour Manage 29, 1995–2010 (2015). https://doi.org/10.1007/s11269-015-0924-z

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