Uncertainty quantization of meteorological input and model parameters for hydrological modelling using a Bayesian‐based integrated approach

• Published in: Hydrological processes Vol. 38; no. 1
• Main Authors: Yan, Xueman, Song, Jinxi, An, Yongkai, Lu, Wenxi
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.01.2024; Wiley Subscription Services, Inc
• Subjects: Annual runoff; Bayesian analysis; Bayesian inference; Bayesian theory; Catchment area; Errors; Evolutionary computation; Factorial analysis; Hydrologic models; Hydrologic processes; hydrological modelling; Hydrology; individual and interactive effects; Integrated approach; Markov chains; meteorological input error; Modelling; Parameter uncertainty; Parameters; Probability theory; Runoff; Snowmelt; Soil water; Statistical inference; Technology assessment; Uncertainty
• ISSN: 0885-6087; 1099-1085
• DOI: 10.1002/hyp.15040

The traditional treatment of uncertainty in hydrological modelling primarily attributes it to model parameters, but rarely systematically considers meteorological input errors, especially in quantifying the impact of meteorological input errors on parameter uncertainty. This study developed a Bayesian‐based integrated approach to quantitatively investigate uncertainties in meteorological inputs (precipitation and temperature) and model parameters as well as the variation in parameter uncertainty due to meteorological input errors. Additionally, we analysed the propagation from these uncertainties to runoff response in snowmelt and non‐snowmelt periods. The applicability and advantages of this approach were presented by applying of the Soil and Water Assessment Tool to the Shitoukoumen Reservoir Catchment. Differential Evolution Adaptive Metropolis‐Markov Chain Monte Carlo was applied for the straightforward Bayesian inference the uncertainties of meteorological inputs and model parameters. On this basis, multilevel factorial analysis technology was used to quantitatively investigate the specific impact of the model parameters' individual and interactive effects due to meteorological input errors. Finally, the impact of meteorological input errors and model parameter uncertainty on the model performance were analysed and quantified systematically. The results showed that the meteorological input errors could affect the random characteristics of multiple model parameters. Moreover, meteorological input errors could further affect the model parameters' effects on annual average runoff. Overall, the above results have significant implications in enhancing hydrological model to simulate/predict runoff and understanding hydrological processes during different periods. The key findings of this study were summarized: The meteorological input errors influenced the related hydrological processes such as overland runoff, snow melting, and evaporation. The meteorological input errors tend to affect the interaction between hydrological processes. Comprehensive consideration of the meteorological input error and model parameter uncertainty is helpful to improve the simulation/prediction ability of hydrological models.