Implications of bioenergy crop expansion on water availability and nitrogen loading over the conterminous United States

Tuesday, December 10, 2019 - 17:15
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Plantation areas of bioenergy crops are projected to expand throughout the 21st century driven by the need for clean energy and increases in cost of fossil energy production. Perennial grasses such as switchgrass and Miscanthus are better alternatives to traditional annual crops due to their higher productivity and water use efficiency and less demands for nutrients and water. However, the spatial distributions of bioenergy plantation are highly uncertain as they are largely influenced by local to state level incentives, environmental regulations, land availability in competition with other land use types. It is therefore crucial to develop a modeling framework capable of quantifying how bioenergy expansion would perturb the terrestrial water budget and influence nitrogen loading to streams over regions with significant projected expansions.

 

In this study, we take advantage of recent progress in three open-source community models: Version 5 of the Community Land Model with explicit representation of bioenergy crops (CLM5_bioenergy), Demeter - the land use and land cover Change Disaggregation Model, and Version 5.2 of the Global Change Assessment Model (GCAM5.2). An ensemble of land use scenarios from GCAM5.2 (i.e., SSP5-RCP8.5 and SSP2-RCP4.5) are selected to represent the business-as-usual and bioenergy expansion scenarios and downscaled by Demeter to provide 0.05o land use maps over the conterminous United States (CONUS) up to 2100. CLM5 is then configured to run over CONUS at 1/8th degree resolution, driven by historical and two dynamically downscaled future climate scenarios from the CMIP5 archive (i.e., RCP8.5 and RCP4.5) that are consistent with the selected GCAM land use scenarios. How choices in bioenergy feedstocks, expansion in marginal vs. existing crop areas would affect runoff, groundwater availability, and nitrogen loading to streams is evaluated based on the numerical experiments. We argue that such process-based modeling frameworks in which drivers for both climate and land use changes are self-consistent are crucial for understanding and quantifying the multi-sectorial impact of future shared socio-economic pathways and emission scenarios.

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