A benchmark for soil organic matter degradation under variably saturated flow conditions

Soil contains the largest terrestrial pool of organic matter, and the cycling of organic carbon in soils plays a crucial role in controlling atmospheric carbon dioxide (CO2) and global climate change. Although considerable progress has been made in previous modeling studies on the fate of soil organic matter (SOM), only a few models used a process-based approach for investigating these strongly coupled and complex soil systems, which involve SOM oxidation, transient water flow, and mass transport processes in aqueous and gaseous phases. Typically, physically based models for water flow, as well as solute and gas transport, are not coupled with state-of-the-art SOM degradation models. Reactive transport models (RTMs) provide a flexible framework for implementing different SOM degradation concepts and integrating biogeochemical processes with water flow and mass transport. Given the complex nature of carbon cycling in soils coupled with flow and mass transport, code intercomparison using well-defined benchmarks is in many cases the only practical method of model verification. The benchmark presented in this manuscript focuses on SOM oxidation under variably saturated flow conditions. The benchmark consists of three problems characterized by increasing complexity. The problems were solved using two different reactive transport codes, namely HP1 and MIN3P-THCm. The first supporting problem introduces a batch-type simulation to assess kinetic networks of SOM degradation. In the second supporting problem, transient water flow, solute transport, gas generation, and diffusive gas transport are considered. The principal problem combines the kinetic networks of SOM degradation with reactive transport under variably saturated flow conditions, including CO2 transport from soils to the atmosphere. Simulation results for the benchmark problems demonstrate an overall excellent agreement between the two codes, building confidence in the ability of RTMs to simulate complex C-cycling in dynamic environments.