Dangal, S. R., C. Schwalm, M. A. Cavigelli, H. T. Gollany, V. L. Jin, and J. Sanderman. 2022. Improving soil carbon estimates by linking conceptual pools against measurable carbon fractions in the DAYCENT Model Version 4.5. Journal of Advances in Modeling Earth Systems 14:e2021MS002622.

Citable PDF link: https://lter.kbs.msu.edu/pub/4039

Terrestrial soil organic carbon (SOC) dynamics play an important but uncertain role in the global carbon © cycle. Current modeling efforts to quantify SOC dynamics in response to global environmental changes do not accurately represent the size, distribution and flux of C from the soil. Here, we modified the daily Century (DAYCENT) biogeochemical model by tuning decomposition rates of conceptual SOC pools to match measurable C fraction data, followed by historical and future simulations of SOC dynamics. Results showed that simulations using fraction-constrained DAYCENT (DCfrac) led to better initialization of SOC stocks and distribution compared to default/SOC-only-constrained DAYCENT (DCdef) at long-term research sites. Regional simulation using DCfrac demonstrated higher SOC stocks for both croplands (34.86 vs. 26.17 MgC ha-1) and grasslands (54.05 vs. 40.82 MgC ha-1) compared to DCdef for the contemporary period (2001-2005 average), which better matched observationally constrained data-driven maps of current SOC distributions. Projection of SOC dynamics in response to land cover change under a high warming climate showed average absolute SOC loss of 8.44 and 10.43 MgC ha-1 for grasslands and croplands, respectively, using DCfrac whereas, SOC losses were 6.55 and 7.85 MgC ha-1 for grasslands and croplands, respectively, using DCdef. The projected SOC loss using DCfrac was 33% and 29% higher for croplands and grasslands compared to DCdef. Our modeling study demonstrates that initializing SOC pools with measurable C fraction data led to more accurate representation of SOC stocks and distribution of SOC into individual carbon pools resulting in the prediction of greater sensitivity to agricultural intensification and warming.

DOI: 10.1029/2021MS002622

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Modeling T1 T2 T3 T4 T5 T6 T7 T8

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