Print Email Facebook Twitter Estimating predictive hydrological uncertainty by dressing deterministic and ensemble forecasts; a comparison, with application to Meuse and Rhine Title Estimating predictive hydrological uncertainty by dressing deterministic and ensemble forecasts; a comparison, with application to Meuse and Rhine Author Verkade, J.S. (TU Delft Safety and Security Science; Deltares; Ministerie van Infrastructuur en Milieu) Brown, J. D. (Hydrologic Solutions Limited) Davids, F. (Deltares; Ministerie van Infrastructuur en Milieu) Reggiani, P. (University of Siegen) Weerts, A. H. (Deltares; Wageningen University & Research) Date 2017 Abstract Two statistical post-processing approaches for estimation of predictive hydrological uncertainty are compared: (i) ‘dressing’ of a deterministic forecast by adding a single, combined estimate of both hydrological and meteorological uncertainty and (ii) ‘dressing’ of an ensemble streamflow forecast by adding an estimate of hydrological uncertainty to each individual streamflow ensemble member. Both approaches aim to produce an estimate of the ‘total uncertainty’ that captures both the meteorological and hydrological uncertainties. They differ in the degree to which they make use of statistical post-processing techniques. In the ‘lumped’ approach, both sources of uncertainty are lumped by post-processing deterministic forecasts using their verifying observations. In the 'source-specific’ approach, the meteorological uncertainties are estimated by an ensemble of weather forecasts. These ensemble members are routed through a hydrological model and a realization of the probability distribution of hydrological uncertainties (only) is then added to each ensemble member to arrive at an estimate of the total uncertainty. The techniques are applied to one location in the Meuse basin and three locations in the Rhine basin. Resulting forecasts are assessed for their reliability and sharpness, as well as compared in terms of multiple verification scores including the relative mean error, Brier Skill Score, Mean Continuous Ranked Probability Skill Score, Relative Operating Characteristic Score and Relative Economic Value. The dressed deterministic forecasts are generally more reliable than the dressed ensemble forecasts, but the latter are sharper. On balance, however, they show similar quality across a range of verification metrics, with the dressed ensembles coming out slightly better. Some additional analyses are suggested. Notably, these include statistical post-processing of the meteorological forecasts in order to increase their reliability, thus increasing the reliability of the streamflow forecasts produced with ensemble meteorological forcings. Subject Ensemble dressingHydrological forecastingPredictive uncertaintyQuantile RegressionStatistical post-processing To reference this document use: http://resolver.tudelft.nl/uuid:c87031f8-13f4-4576-8ef7-1888b090db1e DOI https://doi.org/10.1016/j.jhydrol.2017.10.024 ISSN 0022-1694 Source Journal of Hydrology, 555, 257-277 Part of collection Institutional Repository Document type journal article Rights © 2017 J.S. Verkade, J. D. Brown, F. Davids, P. Reggiani, A. H. Weerts Files PDF 1_s2.0_S0022169417306790.pdf 5.19 MB Close viewer /islandora/object/uuid:c87031f8-13f4-4576-8ef7-1888b090db1e/datastream/OBJ/view