Supplementary Data for UVAFME model and forest management scenarios in Tanana Valley State Forest
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For assistance, contact Shelby Sundquist, ss3988@nau.edu, https://orcid.org/0000-0001-5379-0008 Folder structureraw - select raw data products used, if available and a reasonable size. See supplementary data for where to find large data. UVAFME_src - source code for version of UVAFME model used in this studymgmt_calc - data associated with management scenario inputs scripts - creating input files for UVAFME and analyzing output scripts/inputs - creating input files for TVSF and KLC study areas scripts/analyses - creating figures from model output in TVSF and KLC scripts/.../intermediate_data - intermediate data products which required significant computational time or were created by handModel input/ output/ logs/ jobfiles/ file_lists input_data - input data for TVSF under different climate and management scenarios file_lists - file lists to specify inputs for UVAFME runs jobfiles - jobs to call UVAFME with each file list logs - output from jobs to screen for errors output_data - output data for TVSF under different climate and management scenarios Subdirectories for above folders mgmt_testing - contains all 15,819 sites simulated cf, bau, prj - counterfactual, business-as-usual, adaptive ("projected") management scenarios hist, gcm45, gcm85 - climate scenario. Historical, RCP4.5 (SSP2), RCP8.5 (SSP5) unit_XX - each forest subunit. These are separated to improve runtime. In dynamic management scenarios, all sites in a subunit must be run together. site_mgmt/bsp - 500 black spruce sites selected to test fuel treatments cf, harv, harvplant, prune, shear, shearplant, thin - different management options UVAFME can test. "-plant" means white spruce seedlings were planted after treatment. Not all of these scenarios were included in published figures. 0degCC:4degCC - linear climate forcings tested to see how much this matters for regrowth. Ultimately, only the historic climate scenario (0degCC) was used in published figures. Previously published data not included in this zip: Climate and climate change (temperature, precipitation, relative humidity): Wang et al. 2016 10.1371/journal.pone.0156720; https://climatena.ca/; version 7.31 Topography: Porter et al. 2023 https://www.pgc.umn.edu/data/arcticdem/ Soils: Sand content: Hengl 2018c https://zenodo.org/records/2525662 Bulk density: Hengl 2018 https://zenodo.org/records/2525665 Topographic wetness index: Hengl 2018a https://zenodo.org/record/1447210 Soil water content Hengl and Gupta 2019 https://zenodo.org/records/2784001 Wind speed: Fick and Hijmans 2017 https://worldclim.org/Cloud cover: Harris et al. 2014 https://www.cru.uea.ac.uk/ CitationsFick, S.E. and Hijmans, R.J. (2017) ‘WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas’, International Journal of Climatology, 37(12), pp. 4302–4315. Available at: https://doi.org/10.1002/joc.5086. Harris, I. et al. (2014) ‘Updated high-resolution grids of monthly climatic observations – the CRU TS3.10 Dataset’, International Journal of Climatology, 34(3), pp. 623–642. Available at: https://doi.org/10.1002/joc.3711. Hengl, T. (2018a) ‘Global DEM derivatives at 250 m, 1 km and 2 km based on the MERIT DEM’. Zenodo. Available at: https://doi.org/10.5281/zenodo.1447210. Hengl, T. (2018b) ‘Sand content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution’. Available at: https://doi.org/10.5281/zenodo.2525662. Hengl, T. (2018c) ‘Soil bulk density (fine earth) 10 x kg / m-cubic at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution’. Zenodo. Available at: https://doi.org/10.5281/zenodo.2525665. Hengl, T. and Gupta, S. (2019) ‘Soil water content (volumetric %) for 33kPa and 1500kPa suctions predicted at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution’. Zenodo. Available at: https://doi.org/10.5281/zenodo.2784001. Porter, Claire, Ian Howat, Myoung-Jon Noh, Erik Husby, Samuel Khuvis, Evan Danish, Karen Tomko, et al. 2023. “ArcticDEM - Mosaics, Version 4.1.” Harvard Dataverse. https://doi.org/10.7910/DVN/3VDC4W. Wang, T. et al. (2016) ‘Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America’, PLOS ONE, 11(6), p. e0156720. Available at: https://doi.org/10.1371/journal.pone.0156720.
