Application of the RothC Model in Simulating Effect of Climate Change on CO2 Emissions and Soil Organic Carbon Stocks in Semi-arid Climate of Khorasan-e-Razavi

 Soil is one of the main drivers of global warming through losing carbon in the form of CO2. On the other hand, its ability to sequester carbon is a suitable option for reducing CO2 emissions. Therefore, even few changes in carbon sequestration or decomposition of soil organic carbon affect the global atmospheric CO2 content. Although the soils of arid and semi-arid regions have low organic carbon content, they can sequester substantial amounts of carbon due to the large area of these regions. So, the Rothamsted carbon model was used to predict the impact of future climate changes on the amount of CO2 emissions and low soil organic carbon stocks in the semi-arid arable lands of Razavi Khorasan province. This model is one of the most widely used models for the study of soil organic carbon turnover and has been evaluated in a variety of ecosystems including grasslands, forests and croplands and in various climate regions. The RothC model is consists of five conceptual soil carbon pools, four active fractions and a small amount of inert organic matter (IOM) that is resistant to decay. The active pools splits into: Decomposable Plant Material (DPM), Resistant Plant Material (RPM), Microbial Biomass (BIO) and Humified Organic Matter (HUM). This model is able to reveal the effect of soil texture, temperature, rainfall, evaporation, vegetation and crop management on the soil organic carbon turnover process.

 The Rothamsted carbon model was calibrated and validated using data measured in 2020 and available data from the long-term field experiments in the semi-arid agricultural lands of Jolge Rokh. Then, by analyzing the climate change of the study area, the impact of climate change until the end of the current century on the amount of CO2 cumulative emissions, total organic carbon (TOC) and active carbon pools model were modeled and compared in the current climate and also climate change conditions.

 The comparison between the measured and simulated soil organic carbon values by the model shows the potential of the model to provide predictions with acceptable accuracy. The outcome of comparisons revealed that R2, Root Mean Square Error (RMSE), Mean Difference (MD), Mean Absolute Error (MAE) and Model efficiency were 0.97, 2.78, 2.11, 2.33 and 0.70 respectively. Assessment of climate changes in the region (during 1981-2020) showed a decrease in precipitation and a significant increase in temperature over the past 40 years. Climate change simulation was carried out by temperature increasing and decreasing the precipitation until the end of the current century, indicated the decrease of all active carbon pools. It was found that DPM, RPM, BIO, HUM and TOC decreased respectively to 2.41, 2.72, 2.51, 1.04 and 1.32% compared to the current climatic conditions, while the cumulative CO2 emission increased by 1.26%. Temperature rising leads to increase the rate modifying factor (a) by 2.20%, which enhances microbial respiration and decomposition rate of organic carbon and CO2 emissions (carbon output). However, it also increases the ecosystem's net primary productivity (carbon input). Decreases in rainfall and increase in potential evapotranspiration cause a reduction of the rate modifying factor (b) to 0.23%, which on one side reduces the activity of microorganisms and carbon biodegradation; but on the other side, it decreases the vegetation cover and following that reduces CO2 trapping during the photosynthesis process and transfers it to the soil. It seems that in arid and semi-arid climates where the lack of moisture is the most important limiting factor of the plants growth; the role of precipitation in carbon decomposition and sequestration is greater than temperature.

 The Rothamsted carbon model is suitable for regional simulations because it requires only easily obtainable inputs. Therefore RothC is an appropriate tool for estimating long-term effects of climate change and agricultural management (such as application of manures, returning plant residues to the soil, crop rotations, conservation tillage etc.). The RothC model validation in the cold semi-arid agricultural lands of the region, shows the ability of model to properly simulate the pattern of organic carbon changes. Also, simulation of soil organic carbon changes under the climate changes conditions indicates an increase in cumulative CO2 emissions and decrease in soil organic carbon pools of the study area. The methodology can be applied to other regional estimations, provided that the relevant data are available. The predictions allowed to identify the land management potential to carbon sequestration. Such information demonstrate a beneficial tool for evaluation of past land management effects on soil organic carbon trends and also estimation of future climate change effects on soil organic carbon stocks and CO2 emissions.