Organic carbon concentrations and stocks in Romanian mineral forest soils

Estimating soils organic carbon stock and its change in time isan actual concern for scientists and climate change policy makers. Thepresent article firstly focus on determination of C stocks in Romania on forest soil types, as well as development of the spatial distribution mapping using a Geographic Information System (GIS) and also the secondly on the quantification of uncertainty associated with currently available data on C concentration on forest soils geometrical layers. Determination of C stock was done based on forest management plans database created over 2000-2006. Unlike original database, the data for this study was harmonized on following depths: 0-10 cm, 10-20 cm, 20-40 cm, and > 40 cm. Then, the obtained values were grouped by soil types, resulting average values for the main forest soils from Romania. A soil area weighted average value of 137 t/ha is calculated for Romania, in the range of estimationsfor other European geographic and climatic areas. The soils that have the largest amount of organic carbon are andosols, vertisols, entic and haplic podzols, whereas the ones that have the smallest values of organic carbon are solonetz and solonchaks. Although current assessment relies on very large number of samples from the forest management planning database, the variability of C concentration remains very large, ~40-50% for coefficient the variation and ~100% of the average, when defining the range of 95% of entire soil population, rather showing the variability than uncertainty of the average estimated. Best fit for C concentration on geometric layersin any forest soil is asymmetric, associated with log-normal distributions

Silvicultural Interventions Drive the Changes in Soil Organic Carbon in Romanian Forests According to Two Model Simulations

We investigated the effects of forest management on the carbon (C) dynamics in Romanian forest soils, using two model simulations: CBM-CFS3 and Yasso15. Default parametrization of the models and harmonized litterfall simulated by CBM provided satisfactory results when compared to observed data from National Forest Inventory (NFI). We explored a stratification approach to investigate the improvement of soil C prediction. For stratification on forest types only, the NRMSE (i.e., normalized RMSE of simulated vs. NFI) was approximately 26%, for both models; the NRMSE values reduced to 13% when stratification was done based on climate only. Assuming the continuation of the current forest management practices for a period of 50 years, both models simulated a very small C sink during simulation period (0.05 MgC ha(-1) yr(-1)). Yet, a change towards extensive forest management practices would yield a constant, minor accumulation of soil C, while more intensive practices would yield a constant, minor loss of soil C. For the maximum wood supply scenario (entire volume increment is removed by silvicultural interventions during the simulated period) Yasso15 resulted in larger emissions (-0.3 MgC ha(-1) yr(-1)) than CBM (-0.1 MgC ha(-1) yr(-1)). Under 'no interventions' scenario, both models simulated a stable accumulation of C which was, nevertheless, larger in Yasso15 (0.35 MgC ha(-1) yr(-1)) compared to CBM-CSF (0.18 MgC ha(-1) yr(-1)). The simulation of C stock change showed a strong "start-up" effect during the first decade of the simulation, for both models, explained by the difference in litterfall applied to each scenario compared to the spinoff scenario. Stratification at regional scale based on climate and forest types, represented a reasonable spatial stratification, that improved the prediction of soil C stock and stock change.Peer reviewe

Two large-scale forest scenario modelling approaches for reporting CO2 removal: a comparison for the Romanian forests

Background: Forest carbon models are recognized as suitable tools for the reporting and verification of forest carbon stock and stock change, as well as for evaluating the forest management options to enhance the carbon sink pro‑ vided by sustainable forestry. However, given their increased complexity and data availability, different models may simulate diferent estimates. Here, we compare carbon estimates for Romanian forests as simulated by two models (CBM and EFISCEN) that are often used for evaluating the mitigation options given the forest-management choices. Results: The models, calibrated and parameterized with identical or harmonized data, derived from two successive national forest inventories, produced similar estimates of carbon accumulation in tree biomass. According to CBM simulations of carbon stocks in Romanian forests, by 2060, the merchantable standing stock volume will reach an average of 377 m3 ha−1 , while the carbon stock in tree biomass will reach 76.5 tC ha−1 . The EFISCEN simulations produced estimates that are about 5% and 10%, respectively, lower. In addition, 10% stronger biomass sink was simulated by CBM, whereby the difference reduced over time, amounting to only 3% toward 2060. Conclusions: This model comparison provided valuable insights on both the conceptual and modelling algorithms, as well as how the quality of the input data may affect calibration and projections of the stock and stock change in the living biomass pool. In our judgement, both models performed well, providing internally consistent results. Therefore, we underline the importance of the input data quality and the need for further data sampling and model improvements, while the preference for one model or the other should be based on the availability and suitability of the required data, on preferred output variables and ease of use

Silvicultural Interventions Drive the Changes in Soil Organic Carbon in Romanian Forests According to Two Model Simulations

We investigated the effects of forest management on the carbon (C) dynamics in Romanian forest soils, using two model simulations: CBM-CFS3 and Yasso15. Default parametrization of the models and harmonized litterfall simulated by CBM provided satisfactory results when compared to observed data from National Forest Inventory (NFI). We explored a stratification approach to investigate the improvement of soil C prediction. For stratification on forest types only, the NRMSE (i.e., normalized RMSE of simulated vs. NFI) was approximately 26%, for both models; the NRMSE values reduced to 13% when stratification was done based on climate only. Assuming the continuation of the current forest management practices for a period of 50 years, both models simulated a very small C sink during simulation period (0.05 MgC ha−1 yr−1). Yet, a change towards extensive forest management practices would yield a constant, minor accumulation of soil C, while more intensive practices would yield a constant, minor loss of soil C. For the maximum wood supply scenario (entire volume increment is removed by silvicultural interventions during the simulated period) Yasso15 resulted in larger emissions (−0.3 MgC ha−1 yr−1) than CBM (−0.1 MgC ha−1 yr−1). Under ‘no interventions’ scenario, both models simulated a stable accumulation of C which was, nevertheless, larger in Yasso15 (0.35 MgC ha−1 yr−1) compared to CBM-CSF (0.18 MgC ha−1 yr−1). The simulation of C stock change showed a strong “start-up” effect during the first decade of the simulation, for both models, explained by the difference in litterfall applied to each scenario compared to the spinoff scenario. Stratification at regional scale based on climate and forest types, represented a reasonable spatial stratification, that improved the prediction of soil C stock and stock change