RESUMEN
Land-based mitigation measures are needed to achieve climate targets. One option is the mitigation of currently high greenhouse gas (GHG) emissions of nutrient-rich drained peatland forest soils. Continuous cover forestry (CCF) has been proposed as a measure to manage this GHG emission source; however, its emission reduction potential and impact on timber production at regional and national scales have not been quantified. To quantify the potential emission reduction, we simulated four management scenarios for Finnish forests: (i) The replacement of clear-cutting by selection harvesting on nutrient-rich drained peatlands (CCF) and (ii) the current forest management regime (BAU), and both at two harvest levels, namely (i) the mean annual harvesting (2016-2018) and (ii) the maximum sustainable yield. The simulations were conducted at the stand scale with a forest simulator (MELA) coupled with a hydrological model (SpaFHy), soil C model (Yasso07) and empirical GHG exchange models. Simulations showed that the management scenario that avoided clear-cutting on nutrient-rich drained peatlands (i.e. CCF) produced approximately 1 Tg CO2 eq. higher carbon sinks annually compared with BAU at equal harvest level for Finland. This emission reduction can be attributed to the maintenance of a higher biomass sink and to the mitigation of soil emissions from nutrient-rich drained peatland sites.
RESUMEN
Open quantum systems that interact with structured reservoirs exhibit non-Markovian dynamics. We present a quantum jump method for treating the dynamics of such systems. This approach is a generalization of the standard Monte Carlo wave function (MCWF) method for Markovian dynamics. The MCWF method identifies decay rates with jump probabilities and fails for non-Markovian systems where the time-dependent rates become temporarily negative. Our non-Markovian quantum jump approach circumvents this problem and provides an efficient unraveling of the ensemble dynamics.