Underestimation of boreal soil carbon stocks by mathematical soil carbon models linked to soil nutrient status

Abstract

<p><strong>Abstract.</strong> Inaccurate estimate of the largest terrestrial carbon pool, soil organic carbon (SOC) stock, is the major source of uncertainty in simulating feedback of climate warming on ecosystematmosphere carbon exchange by process based ecosystem and soil carbon models. Although the models need to simplify complex environmental processes of soil carbon sequestration, in a large mosaic of environments a missing key driver could lead into a modelling bias in predictions of SOC stock change. <br><br> We aimed to evaluate SOC stock estimates of process based models (Yasso07, Q, and CENTURY) against the Swedish forest soil inventory data (3230 samples) organized by recursive partitioning method into distinct soil groups with underlying SOC stock development linked to physicochemical conditions. <br><br> The Yasso07 and Q models that used only climate and litterfall input data and ignored soil properties generally agreed with two third of measurements. However, in fertile sites with high nitrogen deposition, high cation exchange capacity, or moderately increased soil water content, Yasso07 and Q underestimated SOC stocks. Accounting for soil texture (clay, silt, and sand content) and structure (bulk density) in CENTURY model showed no improvement on carbon stock estimates, as CENTURY deviated in similar manner. <br><br> Our analysis suggested that the soils with poorly predicted SOC stocks, as characterized by the high nutrient status and well sorted parent material, indeed have had other predominat drivers of SOC stabilization lacking in the models presumably the mycorrhizal organic uptake and organo-mineral stabilization processes. Our results imply that the role of soil nutrient status as regulator of organic matter mineralization has to be re-evaluated, since correct steady state SOC stocks are decisive for predicting future SOC change.</p>

Evaluating CENTURY and Yasso soil carbon models for CO2 emissions and organic carbon stocks of boreal forest soil with Bayesian multi‐model inference

“…Although CENTURY accounted for site‐specific differences in both litterfall and soil characteristics, CENTURY SOC showed little variation between the sites, which was comparable to the Yasso models that do not use specific soil information. These spatially unchanging amounts of SOC were consistent with testing of the CENTURY model with data from a Swedish forest soil inventory, where its SOC differed only for soils with large clay content (Ťupek et al, ).…”

“…The similarity between modelled and measured SOC on spruce sites, and the more considerable difference in pine sites, might also result from differences in plant litter production, which is a predominant factor for the models. The essential role of plant nutrient status in SOC accumulation (Fernández‐Martínez et al, ), but its underrepresentation in soil C models (Ťupek et al, ), could partly explain the difference in measured SOC. The pine forest sites differed in the C/N ratio of the mineral soil, and the soil in the Tammela Pine forest was more moist and fertile than that in the Punkaharju pine forest.…”

“…The model accounted for topsoil N and plant litter C:N ratio, despite N being held constant during the simulations. The sensitivity of SOC stock to topsoil N and plant C:N ratio was weak compared to the sensitivity to litter input (Ťupek et al, ). We ran CENTURY simulations using two alternative temperature and moisture response functions for the rate of decomposition: Kelly et al () and Adair et al () (Table S2), later referred to as CENTURY.K and CENTURY.A, respectively.…”

“…pools, processes and interactions) representing organic matter decomposition and its dependence on environmental conditions (temperature and moisture and other variables) (Figure ). However, prediction of the magnitude and spatial variation of SOC is far from perfect (Hashimoto et al, ; Lehtonen et al, ; Todd‐Brown et al, ; Ťupek et al, ). Model uncertainties hinder conclusions on both changes of SOC and CO 2 emissions (Lehtonen & Heikkinen, ).…”

Underestimation of boreal forest soil carbon stocks related to soil classification and drainage

“…The spatial distribution of forest soil C stocks in the landscape interacts with drivers such as climate, management, and vegetation dynamics and thus is an important aspect of model predictions of soil C. Global, as well as regional, models have been found to lack the ability to predict the spatial distribution of soil C, indicating that key processes are missing (Shaw et al 2014;Todd-Brown et al 2013). On a national scale, models have been evaluated to improve their prediction of spatially distributed soil C stocks and to identify model deficiencies related to processes occurring at a wide range of environmental and topographical conditions (Black et al 2014;Ť upek et al 2016). The model Yasso07, with its focus on litter input, litter chemistry, and climate-controlled mineralization rates (Tuomi et al , 2011a, has been applied at the global scale showing a good correspondence to measured soil C stocks (Thum et al 2011).…”