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Rainfall-Runoff Modeling: Comparison of Two Approaches with Different Data Requirements

Author

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  • A. Bhadra
  • A. Bandyopadhyay
  • R. Singh
  • N. Raghuwanshi

Abstract

Among several hydrological models developed over the years, the most widely used technique for estimating direct runoff depth from storm rainfall i.e., the United States Department of Agriculture (USDA) Soil Conservation Service’s (SCS) Curve Number (CN) method was adopted in the present study. In addition, the Muskingum method, which continues to be popular for routing of runoff in river network, was used in the developed model to route surface runoffs from different subbasin outlet points up to the outlet point of the catchment. SCS CN method in combination with Muskingum routing technique, however, required a detailed knowledge of several important properties of the watershed, namely, soil type, land use, antecedent soil water conditions, and channel information, which may not be readily available. Due to this complexity of semi-distributed conceptual approach (SCS CN method) and non-linearity involved in rainfall-runoff modeling, researchers also attempted another less data requiring approach for runoff prediction, i.e., the neural network approach, which is inherently suited to problems that are mathematically difficult to describe. The purpose of this study was to compare the rainfall-runoff modeling performance of semi-distributed conceptual SCS CN method (in combination with Muskingum routing technique) with that of empirical ANN technique. The models were coded in C language and to make them user friendly, a Graphical User Interface (GUI) was also developed in Visual Basic 6.0. The developed models were tested for Kangsabati catchment, situated in the western part of West Bengal, India. Monsoon data of 1996 to 1999 were used for calibration of the models whereas they were validated for another four years (1987, 1989, 1990, and 1993) monsoon data. Modeling efficiency (ME) and coefficient of residual mass (CRM) were used as performance indicators. Results indicated that for Kangsabati catchment, the empirical runoff prediction approach (ANN technique), in spite of requiring much less data, predicted daily runoff values more accurately than semi-distributed conceptual runoff prediction approach (SCS CN method). Copyright Springer Science+Business Media B.V. 2010

Suggested Citation

  • A. Bhadra & A. Bandyopadhyay & R. Singh & N. Raghuwanshi, 2010. "Rainfall-Runoff Modeling: Comparison of Two Approaches with Different Data Requirements," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(1), pages 37-62, January.
    • Handle: RePEc:spr:waterr:v:24:y:2010:i:1:p:37-62
    DOI: 10.1007/s11269-009-9436-z
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    Citations

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    Cited by:

    1. Rana Muhammad Adnan Ikram & Leonardo Goliatt & Ozgur Kisi & Slavisa Trajkovic & Shamsuddin Shahid, 2022. "Covariance Matrix Adaptation Evolution Strategy for Improving Machine Learning Approaches in Streamflow Prediction," Mathematics, MDPI, vol. 10(16), pages 1-30, August.
    2. Adam P. Piotrowski & Marzena Osuch & Jarosław J. Napiorkowski, 2019. "Joint Optimization of Conceptual Rainfall-Runoff Model Parameters and Weights Attributed to Meteorological Stations," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(13), pages 4509-4524, October.
    3. E.V. Taguas & J. Gómez & P. Denisi & L. Mateos, 2015. "Modelling the Rainfall-Runoff Relationships in a Large Olive Orchard Catchment in Southern Spain," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(7), pages 2361-2375, May.
    4. Marijana Hadzima-Nyarko & Anamarija Rabi & Marija Šperac, 2014. "Implementation of Artificial Neural Networks in Modeling the Water-Air Temperature Relationship of the River Drava," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(5), pages 1379-1394, March.
    5. M. Mustafa & R. Rezaur & S. Saiedi & M. Isa, 2012. "River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms—A Case Study in Malaysia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(7), pages 1879-1897, May.
    6. Shin-Jen Cheng, 2010. "Generation of Runoff Components from Exponential Expressions of Serial Reservoirs," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(13), pages 3561-3590, October.
    7. Mohammed Seyam & Faridah Othman, 2014. "The Influence of Accurate Lag Time Estimation on the Performance of Stream Flow Data-driven Based Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(9), pages 2583-2597, July.
    8. Ozgur Kisi & Alireza Nia & Mohsen Gosheh & Mohammad Tajabadi & Azadeh Ahmadi, 2012. "Intermittent Streamflow Forecasting by Using Several Data Driven Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(2), pages 457-474, January.
    9. D. Pantelakis & Th. Zissis & E. Anastasiadou-Partheniou & E. Baltas, 2012. "Numerical Models for the Simulation of Overland Flow in Fields Within Surface Irrigation Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(5), pages 1217-1229, March.

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