Assessment of the different sources of uncertainty in a SWAT model of the River Senne (Belgium)

Tolessa Leta, O.; Nossent, J.; Velez, C.; Kumar Shrestha, N.; Van Griensven, A.; Bauwens, W.

doi:/10.1016/j.envsoft.2015.02.010

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Assessment of the different sources of uncertainty in a SWAT model of the River Senne (Belgium)

Tolessa Leta, O.; Nossent, J.; Velez, C.; Kumar Shrestha, N.; Van Griensven, A.; Bauwens, W. (2015). Assessment of the different sources of uncertainty in a SWAT model of the River Senne (Belgium). Environ. Model. Softw. 68: 129-146. https://dx.doi.org/10.1016/j.envsoft.2015.02.010

In: Environmental Modelling & Software. Elsevier: Oxford. ISSN 1364-8152; e-ISSN 1873-6726, meer

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Waterbouwkundig Laboratorium: Non-open access 283795 [ aanvragen ] VLIZ [ aanvragen ]

Trefwoorden

Motion > Water motion > Water currents > Stream flow
Zenne R. [Marine Regions]

Author keywords

Uncertainty assessment; Rainfall uncertainty; SWAT; DREAM

Auteurs		Top
Tolessa Leta, O. Nossent, J., meer Velez, C., meer	Kumar Shrestha, N. Van Griensven, A. Bauwens, W.

Abstract

Although rainfall input uncertainties are widely identified as being a key factor in hydrological models, the rainfall uncertainty is typically not included in the parameter identification and model output uncertainty analysis of complex distributed models such as SWAT and in maritime climate zones. This paper presents a methodology to assess the uncertainty of semi-distributed hydrological models by including, in addition to a list of model parameters, additional unknown factors in the calibration algorithm to account for the rainfall uncertainty (using multiplication factors for each separately identified rainfall event) and for the heteroscedastic nature of the errors of the stream flow. We used the Differential Evolution Adaptive Metropolis algorithm (DREAM(zs)) to infer the parameter posterior distributions and the output uncertainties of a SWAT model of the River Senne (Belgium). Explicitly considering heteroscedasticity and rainfall uncertainty leads to more realistic parameter values, better representation of water balance components and prediction uncertainty intervals.

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