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....

Full description

Saved in:
Bibliographic Details
Published in:Environmental modelling & software : with environment data news 2015-06, Vol.68, p.129-146
Main Authors: Leta, Olkeba Tolessa, Nossent, Jiri, Velez, Carlos, Shrestha, Narayan Kumar, van Griensven, Ann, Bauwens, Willy
Format: Article
Language:English
Subjects:
Algorithms
Assessments
Computer programs
DREAM
Freshwater
Hydrology models
Mathematical models
Rainfall
Rainfall uncertainty
River Senne
Rivers
Stream flow
SWAT
Uncertainty
Uncertainty assessment
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1364-8152
DOI:10.1016/j.envsoft.2015.02.010