Articles | Volume 19, issue 7
Hydrol. Earth Syst. Sci., 19, 3181–3201, 2015
https://doi.org/10.5194/hess-19-3181-2015
Hydrol. Earth Syst. Sci., 19, 3181–3201, 2015
https://doi.org/10.5194/hess-19-3181-2015

Research article 23 Jul 2015

Research article | 23 Jul 2015

Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments

N. Dogulu1,a, P. López López1,2,b, D. P. Solomatine1,3, A. H. Weerts2,4, and D. L. Shrestha5 N. Dogulu et al.
  • 1UNESCO-IHE Institute for Water Education, Delft, the Netherlands
  • 2Deltares, Delft, the Netherlands
  • 3Delft University of Technology, Delft, the Netherlands
  • 4Hydrology and Quantitative Water Management Group, Department of Environmental Sciences, Wageningen University, Wageningen, the Netherlands
  • 5CSIRO Land and Water, Highett, Victoria, Australia
  • acurrently at: Department of Civil Engineering, Middle East Technical University, Ankara, Turkey
  • bnow at: Utrecht University (Utrecht) and Deltares (Delft), the Netherlands

Abstract. In operational hydrology, estimation of the predictive uncertainty of hydrological models used for flood modelling is essential for risk-based decision making for flood warning and emergency management. In the literature, there exists a variety of methods analysing and predicting uncertainty. However, studies devoted to comparing the performance of the methods in predicting uncertainty are limited. This paper focuses on the methods predicting model residual uncertainty that differ in methodological complexity: quantile regression (QR) and UNcertainty Estimation based on local Errors and Clustering (UNEEC). The comparison of the methods is aimed at investigating how well a simpler method using fewer input data performs over a more complex method with more predictors. We test these two methods on several catchments from the UK that vary in hydrological characteristics and the models used. Special attention is given to the methods' performance under different hydrological conditions. Furthermore, normality of model residuals in data clusters (identified by UNEEC) is analysed. It is found that basin lag time and forecast lead time have a large impact on the quantification of uncertainty and the presence of normality in model residuals' distribution. In general, it can be said that both methods give similar results. At the same time, it is also shown that the UNEEC method provides better performance than QR for small catchments with the changing hydrological dynamics, i.e. rapid response catchments. It is recommended that more case studies of catchments of distinct hydrologic behaviour, with diverse climatic conditions, and having various hydrological features, be considered.

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