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| DOI | 10.1002/hyp.15040 |
| Uncertainty quantization of meteorological input and model parameters for hydrological modelling using a Bayesian-based integrated approach | |
| 发表日期 | 2024 |
| ISSN | 0885-6087 |
| EISSN | 1099-1085 |
| 起始页码 | 38 |
| 结束页码 | 1 |
| 卷号 | 38期号:1 |
| 英文摘要 | 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.image |
| 英文关键词 | Bayesian inference; hydrological modelling; individual and interactive effects; meteorological input error; uncertainty |
| 语种 | 英语 |
| WOS研究方向 | Water Resources |
| WOS类目 | Water Resources |
| WOS记录号 | WOS:001139106400001 |
| 来源期刊 | HYDROLOGICAL PROCESSES
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/303093 |
| 作者单位 | Northwest University Xi'an; Northwest University Xi'an; Chang'an University; Jilin University; Northwest University Xi'an |
| 推荐引用方式 GB/T 7714 | . Uncertainty quantization of meteorological input and model parameters for hydrological modelling using a Bayesian-based integrated approach[J],2024,38(1). |
| APA | (2024).Uncertainty quantization of meteorological input and model parameters for hydrological modelling using a Bayesian-based integrated approach.HYDROLOGICAL PROCESSES,38(1). |
| MLA | "Uncertainty quantization of meteorological input and model parameters for hydrological modelling using a Bayesian-based integrated approach".HYDROLOGICAL PROCESSES 38.1(2024). |
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