Skill assessment of a global hydrological model in reproducing flow extremes

Abstract

As an initial step in assessing the prospect of using macro-scale hydrological models (MHMs) for hydrological forecasting, this study investigates the skill of the MHM PCR-GLOBWB in reproducing past discharge extremes on a global scale. Global terrestrial hydrology from 1958 until 2001 is simulated by forcing PCR-GLOBWB with daily meteorological data obtained by downscaling the CRU dataset to daily fields using the ERA-40 reanalysis. Simulated discharge values are compared with observed monthly streamflow records for a selection of 20 large river basins that represent all continents and a wide range of climatic zones. We assess model skill in three ways. First, the general performance of the model in reproducing hydrographs is evaluated. Second, model skill in reproducing significantly higher and lower flows than the monthly normals is assessed in terms of skill scores used for forecasts of categorical events. Third, model skill in reproducing flood and drought events is assessed by constructing binary contingency tables for floods and droughts for each basin. The results show that the model has skill in all three types of hindcasting. After bias correction the model skill in simulating hydrographs is improved considerably. For most basins it is much higher than that of the climatology. The skill in hindcasting monthly anomalies is high compared to that of an imaginary unskilled system. The model also performs better than an unskilled system in hindcasting floods and droughts, with a markedly higher skill in floods. We conclude that the prospect for using PCR-GLOBWB for monthly and seasonal hydrological forecasting is positive. Our results which we argue are representative for other similar MHMs, show that MHMs have sufficient skill for use in forecasting flow extremes.