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Evaluating the Performance of CHIRPS Satellite Rainfall Data for Streamflow Forecasting

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  • Bhuvanamitra Sulugodu

    (National Institute of Technology Karnataka)

  • Paresh Chandra Deka

    (National Institute of Technology Karnataka)

Abstract

Streamflow forecasting can offer valuable information for optimal management of water resources, flood mitigation, and drought warning. This research aims in evaluating the effectiveness of CHIRPS satellite rainfall data in comparison with IMD gridded Rainfall Data and development of various flow forecasting models. Daily rainfall data for three decades (1983–2012) over the Nethravathi Basin, Karnataka, India is used for analysis. The analysis is carried out for the monsoon season (June–September), out of which 70% data considered for training the model and remaining for testing. Different input combinations are developed, and soft-computing methods like ANFIS, GRNN, PSO-ANN, and ELM are applied for flow forecasting on a temporal scale. The model performance is evaluated using various statistical indices like NNSE, RRMSE, and MAE. The results indicate that CHIRPS rainfall showed better performance in comparison with IMD data. ELM expressed an enhanced effect when compared to all other methods. The usefulness and effectiveness of CHIRPS data compared to IMD data has been explored.

Suggested Citation

  • Bhuvanamitra Sulugodu & Paresh Chandra Deka, 2019. "Evaluating the Performance of CHIRPS Satellite Rainfall Data for Streamflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3913-3927, September.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:11:d:10.1007_s11269-019-02340-6
    DOI: 10.1007/s11269-019-02340-6
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    References listed on IDEAS

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    1. Hadi Sanikhani & Ozgur Kisi, 2012. "River Flow Estimation and Forecasting by Using Two Different Adaptive Neuro-Fuzzy Approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(6), pages 1715-1729, April.
    2. Sinan Jasim Hadi & Mustafa Tombul, 2018. "Streamflow Forecasting Using Four Wavelet Transformation Combinations Approaches with Data-Driven Models: A Comparative Study," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(14), pages 4661-4679, November.
    3. Feng Gao & Yuhu Zhang & Xiulin Ren & Yunjun Yao & Zengchao Hao & Wanyuan Cai, 2018. "Evaluation of CHIRPS and its application for drought monitoring over the Haihe River Basin, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 92(1), pages 155-172, May.
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    Cited by:

    1. Rasoul Sarvestan & Reza Barati & Aliakbar Shamsipour & Sahar Khazaei & Manfred Kleidorfer, 2024. "Evaluation of the performance of satellite products and microphysical schemes with the aim of forecasting early flood warnings in arid and semi-arid regions (a case study of northeastern Iran)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(13), pages 12401-12426, October.

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