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http://dx.doi.org/10.15681/KSWE.2020.36.5.373

Parameter and Modeling Uncertainty Analysis of Semi-Distributed Hydrological Model using Markov-Chain Monte Carlo Technique  

Choi, Jeonghyeon (Division of Earth Environmental System Science (Major of Environmental Engineering), Pukyong National University)
Jang, Suhyung (Integrated Water Resources Management Center, K-water Research Institute)
Kim, Sangdan (Department of Environmental Engineering, Pukyong National University)
Publication Information
Journal of Korean Society on Water Environment / v.36, no.5, 2020 , pp. 373-384 More about this Journal
Abstract
Hydrological models are based on a combination of parameters that describe the hydrological characteristics and processes within a watershed. For this reason, the model performance and accuracy are highly dependent on the parameters. However, model uncertainties caused by parameters with stochastic characteristics need to be considered. As a follow-up to the study conducted by Choi et al (2020), who developed a relatively simple semi-distributed hydrological model, we propose a tool to estimate the posterior distribution of model parameters using the Metropolis-Hastings algorithm, a type of Markov-Chain Monte Carlo technique, and analyze the uncertainty of model parameters and simulated stream flow. In addition, the uncertainty caused by the parameters of each version is investigated using the lumped and semi-distributed versions of the applied model to the Hapcheon Dam watershed. The results suggest that the uncertainty of the semi-distributed model parameters was relatively higher than that of the lumped model parameters because the spatial variability of input data such as geomorphological and hydrometeorological parameters was inherent to the posterior distribution of the semi-distributed model parameters. Meanwhile, no significant difference existed between the two models in terms of uncertainty of the simulation outputs. The statistical goodness of fit of the simulated stream flows against the observed stream flows showed satisfactory reliability in both the semi-distributed and the lumped models, but the seasonality of the stream flow was reproduced relatively better by the distributed model.
Keywords
Semi-distributed hydrological model; Markov-Chain Monte Carlo technique; Metropolis-Hastings algorithm; Parameter calibration; Parameter uncertainty;
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