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

• Published in: Water resources management Vol. 35; no. 7; pp. 2197 - 2213
• Main Authors: Nguyen, Duc Hai, Kim, Seon-Ho, Kwon, Hyun-Han, Bae, Deg-Hyo
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.05.2021; Springer Nature B.V
• Subjects: Adaptive algorithms; administrative management; Algorithms; Areal precipitation; Atmospheric precipitations; Atmospheric Sciences; Bayesian analysis; Bayesian theory; Civil Engineering; Earth and Environmental Science; Earth Sciences; Environment; Flood control; Flood management; Forecasting; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology; Hydrologic models; Hydrology; Hydrology/Water Resources; Long short-term memory; Markov chain; Markov chains; Mathematical models; neural networks; Nowcasting; Parameter estimation; Parameter uncertainty; Precipitation; Probability theory; Radar; Rain; Rainfall; South Korea; Statistical methods; Two dimensional models; Uncertainty; Uncertainty analysis; water; Water levels; Watersheds; Weather forecasting; Weibull distribution; Weibull statistics; South Korea; Uncertainty quantification; Radar‐based forecasts; Delayed rejection and adaptive Metropolis algorithm; Bayesian approach; Urban flood management
• ISSN: 0920-4741; 1573-1650
• DOI: 10.1007/s11269-021-02835-1

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.