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There is an increasing demand for assessment of water provisioning ecosystemservices. While simple models with low data and expertise requirements areattractive, their use as decision-aid tools should be supported byuncertainty characterization. We assessed the performance of the InVESTannual water yield model, a popular tool for ecosystem service assessmentbased on the Budyko hydrological framework. Our study involved thecomparison of 10 subcatchments ranging in size and land-use configuration,in the Cape Fear basin, North Carolina. We analyzed the model sensitivity toclimate variables and input parameters, and the structural error associatedwith the use of the Budyko framework, a lumped (catchment-scale) modeltheory, in a spatially explicit way. Comparison of model predictions withobservations and with the lumped model predictions confirmed that the InVESTmodel is able to represent differences in land uses  and therefore in thespatial distribution of water provisioning services. Our results emphasizethe effect of climate input errors, especially annual precipitation, anderrors in the ecohydrological parameter <i>Z</i>, which are both comparable to themodel structure uncertainties. Our case study supports the use of the modelfor predicting land-use change effect on water provisioning, although itsuse for identifying areas of high water yield will be influenced byprecipitation errors. While some results are context-specific, our studyprovides general insights and methods to help identify the regions anddecision contexts where the model predictions may be used with confidence

Uncertainty analysis of a spatially explicit annual water-balance model: case study of the Cape Fear basin, North Carolina