Intercomparison of lumped versus distributed hydrologic model ensemble simulations on operational forecast scales

Distributed hydrologic models, with the capability to incorporate a variety of spatially-varying land characteristics and precipitation forcing data, are thought to have great potential for improving hydrologic forecasting. However, uncertainty in the high resolution estimates of precipitation and model parameters may diminish potential gains in prediction accuracy achieved by accounting for the inherent spatial variability. This paper develops a probabilistic methodology for comparing ensemble streamflow simulations from hydrologic models with high- and low-spatial resolution under uncertainty in both precipitation input and model parameters. The methodology produces ensemble streamflow simulations using well calibrated hydrologic models, and evaluates the distinctiveness of the ensembles from the high- and low-resolution models for the same simulation point. The study watersheds are of the scale for which operational streamflow forecasts are issued (order of a few 1000 km2), and the models employed are adaptations of operational models used by the US National Weather Service.