Soil carbon stock estimates in a nationwide inventory: evaluating performance of the ROMUL and Yasso07 models

Abstract. We test whether litter quality, litter quantity and weather data are enough to estimate soil carbon stocks by models. We also test whether inclusion of soil water holding capacity improves soil carbon stock model estimates. Litter input was estimated from stem volume maps provided by the National Forest Inventory, while understorey vegetation was estimated using new biomass models. The litter production rates of trees were based on previous research, while for understorey biomass those were estimated from measured data. We applied Yasso07 and ROMUL models across Finland and ran those models into steady state; thereafter, measured soil carbon stocks were compared with model estimates. We found that the role of understorey litter input is underestimated when the Yasso07 model is parameterised, especially in northern Finland. We also found that the inclusion of soil water holding capacity in the ROMUL model improved predictions, especially in southern Finland. Our results imply that the ecosystem modelling community and greenhouse gas inventories should improve understorey litter estimation in the northern latitudes. Our simulations and measurements show that models using only litter quality, litter quantity and weather data underestimate soil carbon stock in southern Finland and this underestimation is due to omission of the impact of droughts to the decomposition of organic layers.