Jiri Nossent

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

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. Adapted a method to incorporate rainfall uncertainty in distributed hydrologic models.Considered different sources of uncertainty in semi-distributed hydrologic model.Assessed impacts of different sources of uncertainty on model parameter estimations.Accounting for different sources of uncertainty leads to more realistic parameter values.Explicitly treating different uncertainty sources improves water balance representation.

Multi-variable sensitivity and identifiability analysis for a complex environmental model in view of integrated water quantity and water quality modeling

Environmental models are often over-parameterized. A sensitivity analysis can identify influential model parameters for, e.g. the parameter estimation process, model development, research prioritization and so on. This paper presents the results of an extensive study of the Latin-Hypercube-One-factor-At-a-Time (LH-OAT) procedure applied to the Soil and Water Assessment Tool (SWAT). The LH-OAT is a sensitivity analysis method that can be categorized as a screening method. The results of the sensitivity analyses for all output variables indicate that the SWAT model of the river Kleine Nete is mainly sensitive to flow related parameters. Rarely, water quality parameters get a high priority ranking. It is observed that the number of intervals used for the Latin-Hypercube sampling should be sufficiently high to achieve converged parameter rankings. Additionally, it is noted that the LH-OAT method can enhance the understanding of the model, e.g. on the use of water quality input data.