Observing and Simulating Spatial Variations of Forest Carbon Stocks in Complex Terrain

Abstract

The terrestrial carbon (C) cycle remains the least constrained component in the global C cycle, partly due to the difficulty of quantifying C sources and sinks in complex terrain. In this paper, we used observations at the Shale Hills Critical Zone Observatory and a biogeochemistry model, Biome-BGC, to study the spatial distribution of C stocks and fluxes in a first-order watershed. The model simulated the average C pools and fluxes in the watershed after constraining three model parameters with observations. The model was able to generate the observed spatial patterns of C pools in the watershed, with higher biomass and soil C in the valley and lower values on the ridgetop, though the model underestimated the ridgetop to valley differences. We examined the simulated effect of four environmental factors, soil moisture, soil temperature, nitrogen (N) availability and solar radiation, on the spatial distribution of C pools. Among these factors, soil water and N availability coupled together dominate the spatial distribution of aboveground biomass. Soil water was the most important factor controlling soil C. These results are highly sensitive to van Genuchten parameters, which describe the soil water retention curve. This study highlights the importance of the hydrologic system in describing within-watershed structure in terrestrial C stocks.

Temporal patterns of fine‐root dynamics have little influence on seasonal soil CO2 efflux in a mixed, mesic forest

“…Some modeling efforts at Shale Hills have shown overestimation of soil respiration in the early spring and late fall (Smeglin et al, 2020). This could be due to the relationship we showed here between fine‐root mortality and soil CO 2 efflux rates, presumably from decomposition, over a year (Figure 3).…”

“…Oaks (Quercus), hickory (Carya), maple (Acer), and some evergreen species (Tsuga canadensis, Pinus strobus, and Pinus virginiana) make up the composition of the tree species within the SSHCZO (Naithani et al, 2013). Precipitation and mean annual temperature for the SSHCZO averaged 1115 mm from 2015 to 2018 (data not shown) and 9.9 C in 2008-2010 (Smeglin et al, 2020).…”

“…Through the lmer() function, we used the "correlation of fixed-effects table" to assess potential correlation issues between variables initially. Previous work has suggested that topography has significant effects on soil CO 2 concentrations (Hasenmueller et al, 2015;Shi et al, 2018;Smeglin et al, 2020). As our focus in this work was on assessing the potential relationship of root dynamics with soil carbon flux rates, we treated topography as a random effect and incorporated a nested random effect for site within topography in our model.…”

Missing Climate Feedbacks in Fire Models: Limitations and Uncertainties in Fuel Loadings and the Role of Decomposition in Fine Fuel Accumulation

“…Land surface fire regime models use pools of C developed primarily for decomposition algorithms to represent fine surface fuel loads (litter and woody debris C pools). In the original domain of such models, these pools and decomposition processes play an important role in C accounting and nutrient cycling and they have been evaluated and refined for such purposes (e.g., Burke et al., 2003; Morales et al., 2005; Smeglin et al., 2020; Zierl et al., 2007). However, they have not been evaluated in the expanded model context of fine fuel accumulation and fire behavior.…”

Application of a Panel Data Quantile-Regression Model to the Dynamics of Carbon Sequestration in Pinus kesiya var. langbianensis Natural Forests

“…These data are often not well-correlated or follow spatially non-normal distributions [15]. A large number of spatial models have been applied [9,16,17], such as the GWR (Geographically weighted regression model), GWRK (Geographically weighted regression kriging model) [18], LMM (Linear mixed model), SEM (Spatial error model), and SLM (Spatial lag model), etc.…”