Estimating predictive hydrological uncertainty by dressing deterministic and ensemble forecasts; a comparison, with application to Meuse and Rhine

摘要

Highlights?Deterministic and ensemble forecasts are ‘dressed’ using Quantile Regression.?The dressing technique is combined with an ARMA error correction procedure.?Both bare and dressed forecasts are verified against observations.?Skill of dressed deterministic and dressed ensemble forecasts is comparable.AbstractTwo 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-