Perspectives on transferring geospatial models of aquatic condition to management scenarios for resource valuation
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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.