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

Abstract

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.

Suitable Land-Use and Land-Cover Allocation Scenarios to Minimize Sediment and Nutrient Loads into Kwan Phayao, Upper Ing Watershed, Thailand

“…Moreover, as a runoff proxy, the annual rainfall is not a sensitive factor for estimating nutrients, as suggested by [61] and the previous study of [62]. The best fit of the sixth-order polynomial equation between annual rainfall and nutrient (N and P) export under three different scenarios in this study reconfirm the relationship as mentioned earlier.…”

“…In contrast, the annual rainfall, as a runoff proxy, was not observed as being sensitive to the estimated data, due to its calculation to modify the load, in order to account for runoff potential by relating the precipitation per cell to the average over the raster, as suggested by [61]. Therefore, non-linear regression analysis was considered in order to reconfirm the suggestion of [61] and the previous study of [62].…”

Mapping the planet’s critical natural assets

“…Validation of NCP is particularly difficult given that there are no direct measurements for many NCP with assessment reliant on remotely sensed proxies. We utilize the best available global modelling approaches and data, most of which have been validated in at least some locations 19,25,[34][35][36][37][38][39][40][41] . Where uncertainty existed about what distance was most appropriate to model the delivery of NCP (for example, how far to model people downstream or how far people might travel to natural assets), we performed further sensitivity analysis and confirmed that the estimated land area of critical natural assets is robust to the distance chosen (impacting results by <5%; Supplementary Table 6).…”

Measuring changes in financial and ecosystems service outcomes with simulated grassland restoration in a Corn Belt watershed

“…One difference between their study and ours is that they restored lands to pre-settlement vegetation, which included a mix of vegetation types including grassland, wetland, savanna, woodland, and forest, while we restored lands to perennial grassland. Regardless, InVEST modules are designed only to understand the effects of land management (see Hamel, 2014 for model assessment) and results are often sensitive to geographic location, spatial scale, input data (i.e., precipitation, export coefficients), and the resolution of those data sets (Salata et al, 2017;Benez-Secanho and Dwivedi, 2019). For example, Redhead et al (2018) found that InVEST's nutrient reduction model performed well in terms of the relative magnitude of nitrogen and phosphorus export, but could over or underestimate actual nutrient export by as much as 65%.…”