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.

这项研究提出了一种方法，通过使用自适应贝叶斯马尔可夫链蒙特卡洛（MCMC）方法，基于平均面降水量（MAP）预测的不同来源，对单个水文学水位预测过程的每个阶段的不确定性进行量化。MAP预测源自拉格朗日外推法（MAPLE）系统和长短期记忆（LSTM）网络的麦克吉尔降水临近预报法。由MAPLE MAP预测和经LSTM校正的五个强降雨的MAP预测强制采用耦合的1D / 2D城市水文模型（1D / 2D-UHM）处理韩国首尔江南流域两个站点的预测水位事件。在Weibull分布参数的不确定性估计中，将使用延迟拒绝和自适应Metropolis（DRAM）算法的贝叶斯方法与Metropolis-Hastings（MH）算法进行了比较。分析了相关过程中各阶段和来源的不确定性贡献，包括定量降水估计（QPE）输入，MAP输入和1D / 2D-UHM。结果表明，MAPLE MAP预测的不确定性贡献在3小时的预测时间内最高。用于MAPLE MAP预测的QPE输入的不确定性贡献最小，包括LSTM校正的MAP源，MAP和耦合模型在内的两个源的不确定性贡献比QPE输入的不确定性更大。