Simulation of soil carbon dynamics in Australia with {\sc Roth C}

Abstract

<p>We simulated soil organic carbon (C) dynamics across Australia with the Rothamsted carbon model ({\sc Roth C}) by connecting new spatially-explicit soil measurements and data with the model. This helped us to bridge the disconnection that exists between datasets used to inform the model and the processes that it depicts. We compiled publicly available continental-scale datasets and pre-processed, standardised and configured them to the required spatial and temporal resolutions. We then calibrated {\sc Roth C} and run simulations to estimate the baseline soil organic C stocks and composition in the 0--0.3~m layer at 4,043 sites in cropping, modified grazing, native grazing, and natural environments across Australia. We used data on the C fractions, the particulate, mineral associated, and resistant organic C (POC, MAOC and ROC, respectively) to represent the three main C pools in the {\sc Roth C} model's structure.<span class="Apple-converted-space">  </span>The model explained 97--98\% of the variation in measured total organic C in soils under cropping and grazing, and 65\% in soils under natural environments. We optimised the model at each site and experimented with different amounts of C inputs to simulate the potential for C accumulation under constant and chainging climate in a 100-year simulation. Soils under native grazing were the most potentially vulnerable to C decomposition and loss, while soils under natural environments were the least vulnerable. An empirical assessment of the controls on the C change showed that climate, pH, total N, the C:N ratio, and cropping were the most important controls on POC change. Clay content and climate were dominant controls on MAOC change. Consistent and explicit soil organic C simulations improve confidence in the model's estimations, contributing to the development of sustainable soil management under global change.<span class="Apple-converted-space"> </span></p>