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Modeling ecosystem services (ESs) intrinsically involves the use of spatial and temporal data. Correct estimates of ecosystem services are inherently dependent upon the scale (resolution and extent) of the input spatial data. Sensitivity of modeling platforms typically used for quantifying ESs to simultaneous changes in the resolution and extent of spatial data is not particularly clear at present. This study used the Nutrient Delivery Ratio (NDR) model embedded in InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) for ascertaining the sensitivity of the outputs to three digital elevation models (DEM), two land cover datasets, and three precipitation grids for 57 watersheds located in Georgia, United States. Multivariate regression models were developed to verify the influence of the spatial resolution of input data on the NDR model output at two spatial extents (the state of Georgia and six physiographical regions within the state). Discrepancies in nutrient exports up to 77.4% and 168.1% were found among scenarios at the state level for nitrogen and phosphorus, respectively. Land cover datasets differing in resolution were responsible for the highest differences in nutrient exports. Significance (at 5% level) of spatial variables on the model outputs were different for the two spatial extents, demonstrating the influence of scale when modeling nutrient runoff and its importance for better policy prescriptions.

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Does Quantification of Ecosystem Services Depend Upon Scale (Resolution and Extent)? A Case Study Using the InVEST Nutrient Delivery Ratio Model in Georgia, United States