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A non-parametric method is applied to quantify residual uncertainty in\udhydrologic streamflow forecasting. This method acts as a post-processor on\uddeterministic model forecasts and generates a residual uncertainty\uddistribution. Based on instance-based learning, it uses a <i>k</i>\udnearest-neighbour search for similar historical hydrometeorological\udconditions to determine uncertainty intervals from a set of historical\uderrors, i.e. discrepancies between past forecast and observation. The\udperformance of this method is assessed using test cases of hydrologic\udforecasting in two UK rivers: the Severn and Brue. Forecasts in retrospect\udwere made and their uncertainties were estimated using kNN resampling and two\udalternative uncertainty estimators: quantile regression (QR) and uncertainty\udestimation based on local errors and clustering (UNEEC). Results show that\udkNN uncertainty estimation produces accurate and narrow uncertainty intervals\udwith good probability coverage. Analysis also shows that the performance of\udthis technique depends on the choice of search space. Nevertheless, the\udaccuracy and reliability of uncertainty intervals generated using kNN\udresampling are at least comparable to those produced by QR and UNEEC. It is\udconcluded that kNN uncertainty estimation is an interesting alternative to\udother post-processors, like QR and UNEEC, for estimating forecast\uduncertainty. Apart from its concept being simple and well understood, an\udadvantage of this method is that it is relatively easy to implement

Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting