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Perspectives on transferring geospatial models of aquatic condition to management scenarios for resource valuation
Citation:
Hill, Ryan A, E. Fergus, P. Ringold, B. Rashleigh, S. Leibowitz, Phil Kaufmann, J. Renee Brooks, AND J. Doyle. Perspectives on transferring geospatial models of aquatic condition to management scenarios for resource valuation. Joint Aquatic Sciences Meeting, Grand Rapids, Michigan, May 14 - 20, 2022.
Impact/Purpose:
Advances in the availability and geospatial data have allowed us to model and map several measures of instream ecological condition across the US. However, turning these maps and models into management scenarios is a major challenge. In this talk, we will describe the difficulties associated with making management scenarios and decisions with large-scale geospatial modeling. We will also explore several possible solutions to this issue by describing recent work within the literature and research that is currently being conducted by EPA staff and affiliates. With this presentation, we hope to open a discussion with the broader scientific community on improving modeling to provide management scenarios that can be used when valuing aquatic resources. This study supports the development of spatial interpolations of aquatic condition to improve economic research being conducted by the NCEE. This work presents progress on an FY22 deliverable under SSWR 1.2.2 (“Empirical models to interpolate benthic macroinvertebrate observed/expected ratios, or other biological indicator(s) of aquatic ecosystem health, from NARS stream and lake condition to HUC12 or HUC8 units over the conterminous US (CONUS) FY22”).
Description:
The influence of watershed land use on instream physical, chemical, and biological conditions is well documented. Improved availability of watershed data has allowed for modeling and spatial interpolation of several such features across the conterminous US. While these models provide insights into the distribution of aquatic resources, their condition, and land uses influencing them, translating these insights into management scenarios is challenging. This difficulty arises from the coarseness of land use metrics, the irreversibility of some land use types, or both. For example, watershed agriculture is often an important covariate in models, but agricultural intensity and impacts vary widely among regions and over time but is not quantified in standard land cover datasets. Further, changes in extent, and large-scale reversal of agriculture is unlikely. Thus, it is unknown whether models that neglect land use intensity are appropriate for predicting future outcomes. Models that confer mechanistic understanding, such as SEM, can be labor intensive and often offer little or no returns in model performance, but their insights may overcome challenges in management scenario planning and resource valuation. In this presentation, we review recent work and provide our perspective on additional research needed to improve the transferability of spatial models to provide management scenarios for resource valuation. We show that addressing these challenges will require careful consideration of current limitations in both data and modeling and better accounting of land use intensities, the instream stressors they produce, as well as mitigating factors within watersheds, such as wetlands.