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SCS-CN-Based Improved Models for Direct Surface Runoff Estimation from Large Rainfall Events

Author

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  • Ravindra Kumar Verma

    (Indian Institute of Technology)

  • Sangeeta Verma

    (National Institute of Hydrology)

  • Surendra Kumar Mishra

    (Indian Institute of Technology)

  • Ashish Pandey

    (Indian Institute of Technology)

Abstract

This study presents a procedure to estimate more accurate direct surface runoff from large rainfall (>25.4 mm)-runoff events. Improved models (M5-M7) are derived by coupling two concepts: (i) initial abstraction as 2% of the total rainfall and (ii) runoff coefficient = degree of saturation. Performance of ten different models including the original SCS-CN method (M1), Mishra and Singh 2002 (M2), Mishra et al. 2006 (M3), Ajmal et al. 2016 (M4), improved models (M5-M7) and their simplified forms (M8-M10) is evaluated using large (7687) number of rainfall events derived from 98 watersheds of USDA-ARS to assess the accuracy of runoff estimation. Quantitatively, it is assessed using seven performance indices, viz., R2, NSE, PBIAS, RMSE, NRMSE, RSR, and MAE; categories; and Ranking and Grading System (RGS). The resulting high values of R2, RSR, RGS, and lowest values of NSE, PBIAS, RMSE, NRMSE, and MAE for the improved models (M5-M7) reveal that improved models performed better than the existing models (M1-M4). Similarly, based on different performance categories, all improved models exhibited superior performance in most of the watersheds than did the existing models. Sensitivity analysis indicated CN to be the most sensitive parameter of the improved model. The proposed model is seen to have overcome the limitations of the original and its previous versions intended for large events and can thus be used for estimating runoff more accurately.

Suggested Citation

  • Ravindra Kumar Verma & Sangeeta Verma & Surendra Kumar Mishra & Ashish Pandey, 2021. "SCS-CN-Based Improved Models for Direct Surface Runoff Estimation from Large Rainfall Events," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(7), pages 2149-2175, May.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:7:d:10.1007_s11269-021-02831-5
    DOI: 10.1007/s11269-021-02831-5
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    References listed on IDEAS

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    1. Shanshan Hu & Yunyun Fan & Tao Zhang, 2020. "Assessing the Effect of Land Use Change on Surface Runoff in a Rapidly Urbanized City: A Case Study of the Central Area of Beijing," Land, MDPI, vol. 9(1), pages 1-15, January.
    2. Mihai Voda & Constantin Adrian Sarpe & Anna Izabella Voda, 2019. "Romanian River Basins Lag Time Analysis. The SCS-CN Versus RNS Comparative Approach Developed for Small Watersheds," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(1), pages 245-259, January.
    3. Pingjin Jiao & Di Xu & Shaoli Wang & Yingduo Yu & Songjun Han, 2015. "Improved SCS-CN Method Based on Storage and Depletion of Antecedent Daily Precipitation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(13), pages 4753-4765, October.
    4. S. K. Mishra & M. K. Jain & V. P. Singh, 2004. "Evaluation of the SCS-CN-Based Model Incorporating Antecedent Moisture," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 18(6), pages 567-589, December.
    5. Selome Tessema & Steve Lyon & Shimelis Setegn & Ulla Mörtberg, 2014. "Effects of Different Retention Parameter Estimation Methods on the Prediction of Surface Runoff Using the SCS Curve Number Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(10), pages 3241-3254, August.
    6. Julio Pérez-Sánchez & Javier Senent-Aparicio & Francisco Segura-Méndez & David Pulido-Velazquez & Raghavan Srinivasan, 2019. "Evaluating Hydrological Models for Deriving Water Resources in Peninsular Spain," Sustainability, MDPI, vol. 11(10), pages 1-36, May.
    7. P. Singh & S. Mishra & R. Berndtsson & M. Jain & R. Pandey, 2015. "Development of a Modified SMA Based MSCS-CN Model for Runoff Estimation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(11), pages 4111-4127, September.
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    2. Hui Xu & Junlong Gao & Xinchun Yu & Qianqian Qin & Shiqiang Du & Jiahong Wen, 2024. "Assessment of Rainstorm Waterlogging Disaster Risk in Rapidly Urbanizing Areas Based on Land Use Scenario Simulation: A Case Study of Jiangqiao Town in Shanghai, China," Land, MDPI, vol. 13(7), pages 1-18, July.
    3. Ting Zhang & Qian Gao & Huaming Xie & Qianjiao Wu & Yuwen Yu & Chukun Zhou & Zixian Chen & Hanqing Hu, 2022. "Response of Water Yield to Future Climate Change Based on InVEST and CMIP6—A Case Study of the Chaohu Lake Basin," Sustainability, MDPI, vol. 14(21), pages 1-19, October.
    4. Esmatullah Sangin & S. K. Mishra & Pravin R. Patil, 2024. "Analogy Between SCS-CN and Muskingum Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(1), pages 153-171, January.

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