Dataset for Optimizing Process-Based Models to Predict Current and Future Soil Organic Carbon Stocks at High-Resolution

Derek Pierson, Kathleen A. Lohse, Will Wieder, Jeremy Facer, Nicholas Patton, Mark S. Seyfried, Ryan Will, Gerald Flerschinger, Marie-Anne de Graaff

Research output: Non-textual formDigital or Visual Products

Abstract

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.

Original languageAmerican English
Media of outputOnline
DOIs
StatePublished - 14 Jan 2022

Keywords

  • carbon pools
  • clay
  • high-resolution
  • productivity
  • projections
  • soil carbon
  • soil temperature
  • spatial mapping

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