biomass production

Model-data comparisons are always challenging, especially when working at a large spatial scale and evaluating multiple response variables. We implemented the Soil and Water Assessment Tool (SWAT) to simulate water quantity and quality for the Tennessee River Basin. We developed three innovations to overcome hurdles associated with limited data for model evaluation: 1) we implemented an auto-calibration approach to allow simultaneous calibration against multiple responses, including synthetic response variables, such as the HUC8 runoff as an area-weighted average of runoff in multiple subbasins; 2) we identified empirical spatiotemporal datasets to use in our comparison; and 3) we compared functional patterns in landuse-nutrient relationships between SWAT and empirical data. In addition, twenty-two (22) reservoirs in the TRB were included in the SWAT setup, which is conducive to a more realistic modeling of runoff and water quality. We used the 2009 Cropland Data Layer (CDL-2009) (USDA-NASS, 2014) to represent the baseline (i.e., Scenario Base) land use/land cover. The SWAT model was calibrated and validated under Scenario Base. We further applied SWAT to project water quantity and quality under Scenario BC1 and HH3 by replacing the baseline landuse with corresponding future landuse map. This protocol created a total of 4026, 3702, and 4115 distinct HRUs in 55 subbasins under Scenario Base, BC1, and HH3, respectively.

Tennessee River Basin
1. GIS data:
1.1 River basin boundaries, subbasin boundaries, stream network data, landuse/cover, and soil: https://github.com/wanggangsheng/TRB_GIS_data.git
1.2 Additional data (e.g., DEM) can be obtained by contacting the author at wangg@ornl.gov

2. SWAT input files for 3 landuse scenarios
2.1 baseline scenario: https://github.com/wanggangsheng/SWATIO_TRB_Base_updateAll.git
2.2 BC1 scenario: https://github.com/wanggangsheng/SWATIO_TRB_BC1_updateAll.git
2.3 HH3 scenario: https://github.com/wanggangsheng/SWATIO_TRB_HH3_updateAll.git

Model code:
https://bioenergykdf.net/content/swatopt-auto-calibration-tools-swat-20…

References:
Wang G, Jager HI, Baskaran LM, Brandt CC. Hydrologic and water quality responses to biomass production in the Tennessee river basin. GCB Bioenergy. 2018;00:1–17. https://doi.org/10.1111/gcbb.12537.

Landscape implications of bioenergy feedstock choices are significant and depend on land-use practices and their environmental impacts. Although land-use changes and carbon emissions associated with bioenergy feedstock production are dynamic and complicated, lignocellulosic feedstocks may offer opportunities that enhance sustainability when compared to other transportation fuel alternatives. For bioenergy sustainability, major drivers and concerns revolve around energy security, food production, land productivity, soil carbon and erosion, greenhouse gas emissions, biodiversity, air quality, and water quantity and quality. The many implications of bioenergy feedstock choices require several indicators at multiple scales to provide a more complete accounting of effects. Ultimately, the long-term sustainability of bioenergy feedstock resources (as well as food supplies) throughout the world depends on land-use practices and landscape dynamics. Land-management decisions often invoke trade-offs among potential environmental effects and social and economic factors as well as future opportunities for resource use. The hypothesis being addressed in this paper is that sustainability of bioenergy feedstock production can be achieved via appropriately designed crop residue and perennial lignocellulosic systems. We find that decision makers need scientific advancements and adequate data that both provide quantitative and qualitative measures of the effects of bioenergy feedstock choices at different spatial and temporal scales and allow fair comparisons among available options for renewable liquid fuels.