Summary & Purpose
Soil carbon (C) management and mitigation policies are reliant on estimates of soil C stocks, especially at fine spatial scales. However, given enduring data limitations, statistical models used for such estimates are limited in their ability to predict the underlying composition and vulnerability of soil C to global change. Here we show that an optimized, process-based model is uniquely suited to fill this gap. We parameterize the MIcrobial-MIneral Carbon Stabilization (MIMICS) model with spatially-explicit data across the Reynolds Creek Experimental Watershed in SW Idaho, USA, and illustrate that data-constrained model parameterization can reduce uncertainty in total soil C stocks (r=0.82 for an independent dataset). We produce the first high-resolution (10 m2) estimates of soil C stocks (including litter, microbial, particulate, and protected C pools) to a depth of 30 cm across the entire watershed (239 km2), and predict their respective vulnerabilities to a suite of environmental disturbances. Generating high-resolution estimates of soil C stocks and measurable underlying pools, are a critical step towards understanding soil C storage and vulnerability, and informing land management under a changing climate.
Date of Publication or Submission
1-14-2022
DOI
https://doi.org/10.18122/reynoldscreek.26.boisestate
Funding Citation
This study was conducted in collaboration and cooperation with the USDA Agricultural Research Service, Northwest Watershed Research Center, Boise, Idaho, and landowners within the Reynolds Creek Experimental Watershed and Critical Zone Observatory (RCEW-CZO). Support for this research was provided by the U.S. National Science Foundation (NSF) via RCEW-CZO Cooperative agreement NSF EAR-1331872 (Lohse, Seyfried). DP and WRW were supported by USDA-NIFA-AFRI 2020-67019-31395. WRW was supported by NSF ARCSS-2031238 and NSF DEB-1926413.
Data Source Credits
https://doi.org/10.18122/reynoldscreek/20/boisestate
Single Dataset or Series?
Series
Data Format
*.tiff; *.xlsx; *.csv; *.docx; *.txt
Data Attributes
Brief overview of data products. See readme.txt files for additional specifics. 1) Continuous spatial projections of 0-30 cm soil carbon stocks (10 m resolution raster) for the Reynolds Creek Experimental Watershed (RCEW) produced using the Microbial Mineral Carbon Stabilization model (https://github.com/wwieder/MIMICS). Also includes associated estimates of litter C stocks, soil microbial C stocks and protected SOC stocks, as well as projected SOC stocks following 10% increase in gross ecosystem production or 1 degree Celsius increase in mean annual soil temperature. 2) Continuous spatial layers (raster) for the RCEW used for MIMICS model forcing (i.e. model inputs). Specifically, these layers pertain to estimated mean annual 0-30 cm soil temperature, gross ecosystem productivity, 0-30 cm soil clay content (sources from SSURGO, https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/?cid=nrcs142p2_053627), and estimated vegetation lignin:N content. 3) Tabular data for RCEW point-based correlations used to generate spatially continuous estimates of mean annual soil temperature (MAST, estimate for soil depth 0-30 cm) and gross ecosystem productivity (GEP). MAST was estimated based on field observation relationships with elevation and estimated incoming solar radiation. GEP was estimated from hyperspectral imagery reclassified according to the modified soil adjusted vegetation index formula (MSAVI2). Estimates of GEP based on MSAVI2 were originally performed by Jeremy Facer and the associated report is included. 4) Calculated 0-30 cm soil organic carbon stocks. Used for MIMICS model calibration and validation. Original field measures by depth increment were sourced in part from previously published data: https://doi.org/10.18122/reynoldscreek/11/boisestate 5) A suite of spatial layers specific to the RCEW that were used to derive other layers or were otherwise useful during the associated study. Including digital elevation model and corresponding hillshade, terrain aspect, RCEW boundary, and polygon for RCEW valley agricultural land.
Time Period
2010-2022
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Use Restrictions
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BOISE STATE UNIVERSITY MAKES NO REPRESENTATIONS ABOUT THE SUITABILITY OF THE INFORMATION CONTAINED IN OR PROVIDED AS PART OF THE SYSTEM FOR ANY PURPOSE. ALL SUCH INFORMATION IS PROVIDED "AS IS" WITHOUT WARRANTY OF ANY KIND. BOISE STATE UNIVERSITY HEREBY DISCLAIMS ALL WARRANTIES AND CONDITIONS WITH REGARD TO THIS INFORMATION, INCLUDING ALL WARRANTIES AND CONDITIONS OF MERCHANTABILITY, WHETHER EXPRESS, IMPLIED OR STATUTORY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT.
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Recommended Citation
Pierson, Derek; Lohse, Kathleen A.; Wieder, Will; Facer, Jeremy; Patton, Nicholas; Seyfried, Mark S.; Will, Ryan; Flerschinger, Gerald; and de Graaff, Marie-Anne. (2022). Dataset for Optimizing Process-Based Models to Predict Current and Future Soil Organic Carbon Stocks at High-Resolution [Data set]. Retrieved from https://doi.org/10.18122/reynoldscreek.26.boisestate
Comments
Erratum 1/21/2022: Author list has been updated with final three additional authors.