DOI: 10.5194/hess-23-2147-2019
论文题名: A likelihood framework for deterministic hydrological models and the importance of non-stationary autocorrelation
作者: Ammann L. ; Fenicia F. ; Reichert P.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2019
卷: 23, 期: 4 起始页码: 2147
结束页码: 2172
语种: 英语
Scopus关键词: Autocorrelation
; Catchments
; Forecasting
; Gaussian noise (electronic)
; Probability distributions
; Arbitrary probability distribution
; Correlated errors
; Hydrological modeling
; Hydrological models
; Likelihood functions
; Residual error models
; Stationary correlation
; Temporal resolution
; Errors
; autocorrelation
; catchment
; hydrological modeling
; maximum likelihood analysis
; prediction
; streamflow
; uncertainty analysis
英文摘要: The widespread application of deterministic hydrological models in research and practice calls for suitable methods to describe their uncertainty. The errors of those models are often heteroscedastic, non-Gaussian and correlated due to the memory effect of errors in state variables. Still, residual error models are usually highly simplified, often neglecting some of the mentioned characteristics. This is partly because general approaches to account for all of those characteristics are lacking, and partly because the benefits of more complex error models in terms of achieving better predictions are unclear. For example, the joint inference of autocorrelation of errors and hydrological model parameters has been shown to lead to poor predictions. This study presents a framework for likelihood functions for deterministic hydrological models that considers correlated errors and allows for an arbitrary probability distribution of observed streamflow. The choice of this distribution reflects prior knowledge about non-normality of the errors. The framework was used to evaluate increasingly complex error models with data of varying temporal resolution (daily to hourly) in two catchments. We found that (1) the joint inference of hydrological and error model parameters leads to poor predictions when conventional error models with stationary correlation are used, which confirms previous studies; (2) the quality of these predictions worsens with higher temporal resolution of the data; (3) accounting for a non-stationary autocorrelation of the errors, i.e. allowing it to vary between wet and dry periods, largely alleviates the observed problems; and (4) accounting for autocorrelation leads to more realistic model output, as shown by signatures such as the flashiness index. Overall, this study contributes to a better description of residual errors of deterministic hydrological models. © Author(s) 2019. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/162981
Appears in Collections: 气候变化与战略
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作者单位: Ammann, L., Swiss Federal Institute of Aquatic Science and Technology (Eawag), Dubendorf, Switzerland, Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland; Fenicia, F., Swiss Federal Institute of Aquatic Science and Technology (Eawag), Dubendorf, Switzerland; Reichert, P., Swiss Federal Institute of Aquatic Science and Technology (Eawag), Dubendorf, Switzerland, Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
Recommended Citation:
Ammann L.,Fenicia F.,Reichert P.. A likelihood framework for deterministic hydrological models and the importance of non-stationary autocorrelation[J]. Hydrology and Earth System Sciences,2019-01-01,23(4)