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Abstract. This study focuses on the Iberian Peninsula and investigates the propagationof precipitation uncertainty, and its interaction with hydrologic modeling,in global water resource reanalysis. Analysis is based on ensemblehydrologic simulations for a period spanning 11 years (2000–2010). Tosimulate the hydrological variables of surface runoff, subsurface runoff,and evapotranspiration, we used four land surface models (LSMs) – JULES (Joint UKLand Environment Simulator), ORCHIDEE (Organising Carbon and Hydrology InDynamic Ecosystems), SURFEX (Surface Externalisée), and HTESSEL(Hydrology – Tiled European Centre forMedium-Range Weather Forecasts – ECMWF – Scheme for Surface Exchanges over Land) – and oneglobal hydrological model, WaterGAP3 (Water – a Global Assessment andPrognosis). Simulations were carried out for five precipitationproducts – CMORPH (the Climate Prediction CenterMorphing technique of the National Oceanic and Atmospheric Administration,or NOAA), PERSIANN (Precipitation Estimation from Remotely Sensed Information using ArtificialNeural Networks), 3B42V(7), ECMWF reanalysis, and a machine-learning-based blended product. As a reference, we used a ground-basedobservation-driven precipitation dataset, named SAFRAN, available at5 km, 1 h resolution. We present relative performances of hydrologicvariables for the different multi-model and multi-forcing scenarios. Overall,results reveal the complexity of the interaction between precipitationcharacteristics and different modeling schemes and show that uncertaintiesin the model simulations are attributed to both uncertainty in precipitationforcing and the model structure. Surface runoff is strongly sensitive toprecipitation uncertainty, and the degree of sensitivity dependssignificantly on the runoff generation scheme of each model examined.Evapotranspiration fluxes are comparatively less sensitive for this studyregion. Finally, our results suggest that there is no single model–forcingcombination that can outperform all others consistently for all variablesexamined and thus reinforce the fact that there are significant benefits toexploring different model structures as part of the overall modelingapproaches used for water resource applications.

Assessment of precipitation error propagation in multi-model global water resource reanalysis