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Geomorphology Based Semi-Distributed Approach for Modelling Rainfall-Runoff Process

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  • Rajib Bhattacharjya
  • Sandeep Chaurasia

Abstract

This study presents a geomorphology based semi-distributed methodology for prediction of runoff of a catchment. In this proposed methodology, the catchment area is divided into a number of sub-catchments using the Thiessen polygon method. The rainfall records of particular rain-gauge station are considered as uniformly distributed over the entire sub-watershed. Four different weighting factors are proposed to obtain the sub-catchment’s contribution towards runoff. The weighting factors are calculated based on the geomorphological parameters of the catchment. The geomorphological parameters of the sub-watersheds are obtained from SRTM digital elevation data. The weighted contributions from all the sub-watersheds at current and previous time steps and the previous time step discharge are used to develop an Artificial Neural Network (ANN) for predicting the discharge at the basin outlet. A lump model considering average rainfall of the catchment is also developed using ANN for evaluating the performance of the proposed distributed model. For the lump model, average rainfall is calculated using Thiessen polygon method. The historic rainfall and runoff data recorded at the Dikrong basin, a sub-catchment of the river Brahmaputra is used to evaluate the efficiency of the developed methodology. The evaluation results show that the presented model is superior to the lump model and has the potential for field application. A comparative study is also carried out to obtain the most influential combination of geomorphological parameters in predicting the catchment’s runoff. Copyright Springer Science+Business Media Dordrecht 2013

Suggested Citation

  • Rajib Bhattacharjya & Sandeep Chaurasia, 2013. "Geomorphology Based Semi-Distributed Approach for Modelling Rainfall-Runoff Process," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(2), pages 567-579, January.
    • Handle: RePEc:spr:waterr:v:27:y:2013:i:2:p:567-579
    DOI: 10.1007/s11269-012-0202-2
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    References listed on IDEAS

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    1. Dragan Savic & Godfrey Walters & James Davidson, 1999. "A Genetic Programming Approach to Rainfall-Runoff Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 13(3), pages 219-231, June.
    2. Avinash Agarwal & R. Singh, 2004. "Runoff Modelling Through Back Propagation Artificial Neural Network With Variable Rainfall-Runoff Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 18(3), pages 285-300, June.
    3. Juran Ahmed & Arup Sarma, 2007. "Artificial neural network model for synthetic streamflow generation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(6), pages 1015-1029, June.
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    Cited by:

    1. Wei Zhang & Yan Zhu & Xuejun Wang, 2014. "A Modeling Method to Evaluate the Management Strategy of Urban Storm Runoff," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(2), pages 541-552, January.
    2. Yajie Wu & Yuan Chen & Yong Tian, 2022. "Incorporating Empirical Orthogonal Function Analysis into Machine Learning Models for Streamflow Prediction," Sustainability, MDPI, vol. 14(11), pages 1-19, May.

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