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|Title:||Disentangling Uncertainties in Distributed Hydrological Modeling Using Multiplicative Error Models and Sequential Data Assimilation|
|Authors:||SALAMON Peter; FEYEN Luc|
|Citation:||WATER RESOURCES RESEARCH vol. 46 p. 20|
|Publisher:||AMER GEOPHYSICAL UNION|
|Type:||Articles in periodicals and books|
|Abstract:||The quantification of uncertainty in hydrologic modeling is a difficult task, as it arises from a combination of physical measurement errors, errors due to different temporal and spatial scales, and errors in the mathematical description of hydrologic processes. This paper presents an approach to infer during a calibration process the statistical properties of the principal error sources in hydrologic models, namely parameter, precipitation, potential evapotranspiration, and structural model uncertainty, by means of sequential data assimilation. We perform sequential data assimilation using a particle filter that combines stochastic universal resampling and kernel smoothing with local shrinkage to improve its performance in comparison to traditional basic importance sampling filters. Precipitation, potential evapotranspiration, and structural model uncertainty are incorporated into the calibration process using multiplicative error models. To illustrate the applicability of this approach and its usefulness to provide insights into the properties of the different error sources the particle filter is applied to a large-scale distributed hydrological model of the Rhine river basin. A synthetic case study, diagnostic checks on the model performance and the verification of the underlying statistical assumptions of the adopted error models demonstrate that the posterior distributions can be considered as reliable. Posterior distributions of the precipitation, potential evapotranspiration, and structural model multipliers are used to identify whether a systematic bias for the two input variables as well as for structural model error exists. Furthermore, the distributions illustrate that uncertainty from those sources can be reduced significantly in comparison to the prior assumptions adopted. An evaluation of the predictive uncertainty of the hydrologic model illustrates that considering parameter, precipitation, potential evapotranspiration, and structural model uncertainty appears to be sufficient to characterize the principal sources of error. Results indicate, however, that the assumptions taken in the output error model and the simplifications of the multiplicative error models do not always hold in practice. Therefore more sophisticated error models and a better quantification of the discharge measurement error are required to further improve the characterization of the different error sources.|
|JRC Institute:||Institute for Environment and Sustainability|
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