The Value of Simple Models: Performance of a Spatially-explicit Seasonal Model for Valuing Water Provisioning (InVEST)

Valuing hydrologic ecosystem services (ES) requires a truly integrated approach, linking knowledge of hydrologic processes to that of the socio-economic context of a region. Although both the hydrological and socio-economic dimensions are complex, practitioners need simple and credible models to address pressing questions brought by global change. We developed such a model for the supply, service, and value of water provisioning; available to the ES community through the open-source InVEST (Integrated Valuation of Ecosytem Services and Trade-offs) software. The model is characterized by i) low data requirements, with the aim of being applied in data-scarce environments; ii) spatially-explicit outputs, to easily address spatial planning questions; iii) a seasonal time-step, representing a compromise between data knowledge and ability to address season-dependent questions (water supply for irrigation, hydropower production); iv) explicit representation of beneficiaries, to facilitate valuation of the provisioning service for different groups; v) flexible valuation framework, to address a variety of ES questions. The model theory is based on the recent advances in hydrology, using the "limits" concept for water balance modeling and spatial indices for subsurface and surface runoff. We tested the model performance in the Mad River catchment, Vermont, USA, comparing its results with the data-intensive RHESSys model for two typical ES questions: the identification of hotspots of service and valuation of the provisioning service for hydropower production. Uncertainty analyses, including sensitivity analyses and Monte Carlo analyses, were performed to quantify uncertainty in both hydrological outputs and service provisioning, and improve guidance for users. We present these results through a range of spatial and non-spatial outputs, emphasizing the importance of results interpretation and visualization for ES assessments.