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Bayesian Framework for Uncertainty Quantification and Bias Correction of Projected Streamflow in Climate Change Impact Assessment

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  • Jose George

    (Indian Institute of Technology Palakkad)

  • P. Athira

    (Indian Institute of Technology Palakkad
    Indian Institute of Technology Palakkad)

Abstract

The study focuses on the uncertainty quantification and bias correction of hydrological projections using Bayesian applications. The climate change impact assessment on streamflow has been done using Soil and Water Assessment Tool (SWAT) model in Bharathapuzha river basin, India. The uncertainty quantification has been done by using Generalised Likelihood Uncertainty Estimation (GLUE) algorithm and the ensemble spread in the streamflow projections is quantified as the total uncertainty. A Hierarchical Bayesian Algorithm is adopted in the current study to remove the systematic bias in the projections of extreme streamflow. The approach established a probabilistic correction to the projected streamflow based on the biases in daily scale hindcast streamflow simulations with the corresponding observed historical streamflow data. The procedure is applied to the ensemble streamflow predictions for the Bharathapuzha catchment and over 10 times reduction in RMSE is observed in the bias corrected streamflow. The skill of the procedure in correcting the streamflow across different terciles is studied using the concept of reliability and significant improvement is observed in the reliability of high and medium flow ranges. The average width of the ensemble streamflow simulation band for the period 2021–2030 is seen to reduce from 5560 cumec to 2188 cumec after the correction procedure is applied.

Suggested Citation

  • Jose George & P. Athira, 2024. "Bayesian Framework for Uncertainty Quantification and Bias Correction of Projected Streamflow in Climate Change Impact Assessment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(12), pages 4499-4516, September.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:12:d:10.1007_s11269-024-03876-y
    DOI: 10.1007/s11269-024-03876-y
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    References listed on IDEAS

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    1. S. Rehana & P. Mujumdar, 2014. "Basin Scale Water Resources Systems Modeling Under Cascading Uncertainties," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(10), pages 3127-3142, August.
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    Cited by:

    1. Hao Ke & Wenzhuo Wang & Zengchuan Dong & Benyou Jia & Ziqin Zheng & Shujun Wu, 2024. "Xinanjiang-Based Interval Forecasting Model for Daily Streamflow Considering Climate Change Impacts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(14), pages 5507-5522, November.

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