Abstract. 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 framework. Our study involved the comparison of ten subcatchments in the Cape Fear watershed, NC, ranging in size and land use configuration. We analyzed the model sensitivity to the eco-hydrological parameters and the effect of extrapolating a lumped theory to a fully distributed model. Comparison of the model predictions with observations and with a lumped water balance model confirmed that the model is able to represent differences in land uses. Our results also emphasize the effect of climate input errors, especially annual precipitation, and errors in the eco-hydrological parameter Z, which are both comparable to the model structure uncertainties. In practice, 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 the results are inherently local, analysis of the model structure suggests that many insights from this study will hold globally. Further work toward characterization of uncertainties in such simple models will help identify the regions and decision contexts where the model predictions may be used with confidence.
Quantification of effects of climate variations and human activities on runoff by a monthly water balance model: A case study of the Chaobai River basin in northern China
