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

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

Abstract licence: CC BY

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