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Uncertainty Quantification of Water Level Predictions from Radar‐based Areal Rainfall Using an Adaptive MCMC Algorithm

Author

Listed:
  • Duc Hai Nguyen

    (Sejong University
    Thuyloi University)

  • Seon-Ho Kim

    (Sejong University)

  • Hyun-Han Kwon

    (Sejong University)

  • Deg-Hyo Bae

    (Sejong University)

Abstract

This study proposes an approach for the uncertainty quantification at each stage of a single hydrological process of water level predictions based on different sources of mean areal precipitation (MAP) forecasts by using an adaptive Bayesian Markov chain Monte Carlo (MCMC) approach. The MAP forecasts are derived from the McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE) system and a long short-term memory (LSTM) network. The predicted water levels at two stations in the Gangnam catchment, Seoul, South Korea, are processed with a coupled 1D/2D urban hydrological model (1D/2D-UHM) forced by MAPLE MAP forecasts and LSTM-corrected MAP forecasts of five heavy rainfall events. The proposed Bayesian approach using the delayed rejection and adaptive Metropolis (DRAM) algorithm was compared with the Metropolis-Hastings (MH) algorithm in the uncertainty estimation of Weibull distribution parameters. The uncertainty contributions of the stages and sources in the related process were analyzed, including quantitative precipitation estimation (QPE) inputs, MAP inputs and 1D/2D-UHM. The results indicate that the uncertainty contribution of the MAPLE MAP forecasting is the highest in the 3-hour forecasting time. The uncertainty contribution of the QPE input for MAPLE MAP forecasting is the smallest and that of two sources, including the LSTM-corrected MAP source, and MAP and the coupled model is more significant than that of the QPE input. This research showed that the adaptive Bayesian MCMC method using the DRAM algorithm might be a robust option in quantitative uncertainty analyses of a single hydrological process, especially for urban flood management.

Suggested Citation

  • Duc Hai Nguyen & Seon-Ho Kim & Hyun-Han Kwon & Deg-Hyo Bae, 2021. "Uncertainty Quantification of Water Level Predictions from Radar‐based Areal Rainfall Using an Adaptive MCMC Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(7), pages 2197-2213, May.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:7:d:10.1007_s11269-021-02835-1
    DOI: 10.1007/s11269-021-02835-1
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    References listed on IDEAS

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    1. Chi Zhang & Martin F. Lambert & Jinzhe Gong & Aaron C. Zecchin & Angus R. Simpson & Mark L. Stephens, 2020. "Bayesian Inverse Transient Analysis for Pipeline Condition Assessment: Parameter Estimation and Uncertainty Quantification," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 2807-2820, July.
    2. Wei Zhang & Tian Li, 2015. "The Influence of Objective Function and Acceptability Threshold on Uncertainty Assessment of an Urban Drainage Hydraulic Model with Generalized Likelihood Uncertainty Estimation Methodology," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(6), pages 2059-2072, April.
    3. Wentao Xu & Cong Jiang & Lei Yan & Lingqi Li & Shuonan Liu, 2018. "An Adaptive Metropolis-Hastings Optimization Algorithm of Bayesian Estimation in Non-Stationary Flood Frequency Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(4), pages 1343-1366, March.
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