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    天然次生林碳储量生长模型与固碳能力驱动力研究

    Growth model of carbon storage and driving force of carbon sequestration capacity of natural secondary forests

    • 摘要:
        目的  针对采伐干扰后天然恢复的次生林,建立其碳储量生长模型及对应的碳汇模型,分析不同因子对固碳能力的驱动作用,为固碳能力量化评价提供科学依据。
        方法  基于吉林省第9次森林资源连续清查固定样地数据筛选出的111个采伐后形成的天然次生林样地数据,采用Richards理论生长方程,以样地平均木的碳储量为因变量,以样地平均年龄为自变量,通过对年龄分组和迭代算法建立碳储量分级生长模型,通过对碳储量分级生长模型中的年龄求导得到碳汇分级生长模型。采用决定系数(R2)和均方根误差(RMSE)评价模型拟合效果。以地理因子、地形因子、气候因子、土壤因子和林分因子为自变量,基于一般线性模型,引入定性和定量因子交互作用,分析固碳能力的驱动力。
        结果  (1)天然次生林碳储量分级生长模型的R2为0.965 6,RMSE为2.61 kg,具有很好的拟合优度。(2)各个分级碳汇量最大的年龄分别为8、10、13、17和29年,以1 000株/hm2的密度计算,5年时间的阈值为1.84 t/hm2,到30年时增加到10.78 t/hm2,各级间的阈值随年龄的增加而增加。(3)一阶定性和一、二阶定量交互的模型对平均木碳储量的解释最高,R2为0.919 2,与不含交互项的主效应模型相比,R2提高了0.088 5。(4)对于平均木碳储量,地理因子(经度和纬度)、地形因子(海拔、地貌、坡向、坡位和坡度)、枯枝落叶层厚度、气候因子(年均气温、极端最低温度、湿度指数、无霜期天数)、土壤因子(土壤厚度)和林分因子(年龄和优势树种)等因子有显著性影响;对于不同立地分级20年时碳密度,地理因子(纬度)、地形因子(地貌、坡度和坡向)、气候因子(极端最高温度、最热月平均温度和湿度指数)、土壤因子(土壤类型和砾石含量)和林分因子(优势树种)等因子有显著性影响,而海拔和腐殖质厚度等变量存在于交互项中,其主效应并不显著。
        结论  天然次生林不同等级的碳密度在不同的时间段,等级间的阈值不同,随着年龄的增加,各级间的阈值不断增加,但不同等级间碳储量的相对差距随着年龄的增加而减小。引入交互作用可以提高模型对森林固碳的解释程度。纬度等地理因子,坡度、坡向等地形因子,湿度指数等气候因子和优势树种等林分因子是影响天然次生林固碳能力因素的关键因子。

       

      Abstract:
        Objective  This paper aims to establish a growth model of carbon storage of the natural secondary forests after logging disturbance and the corresponding carbon sink model, so as to analyze the driving effect of different factors on carbon sequestration capacity, which can provide references for quantitative evaluation of carbon sequestration capacity.
        Method  Totally 111 sample plot data for the natural secondary forests were selected based on the data of the 9th National Forest Inventory in Jilin Province of northeastern China. Based on the Richards theoretical growth equation, taking the average tree carbon storage of the sample plot as the dependent variable and the average age of the sample plot as the independent variable, a carbon storage classified growth model was established by grouping the age and iterative algorithm, and the carbon sink classified growth model was obtained by deriving the age in the carbon storage classified growth model. The coefficient of determination (R2) and the root mean square error (RMSE) were used to evaluate the effect of model fitting. Based on a linear model, taking geographical factors, topographic factors, climate factors, soil factors and forest stand factors as independent variables, the interactions of qualitative and quantitative factors were introduced to analyze the driving force of carbon sequestration capacity.
        Result  (1) The R2 of the natural secondary forest carbon storage classified growth model was 0.965 6, and the RMSE was 2.612 7 kg, this model had a good goodness of fit. (2) The ages of the largest carbon sinks in each level were 8, 10, 13, 17 and 29 years. Calculated at a density of 1 000 plants per hectare, the threshold between levels in 5 years was 1.84 t/ha, and by 30 years increased to 10.78 t/ha, the threshold between each level increased with age. (3) The first-order qualitative, first-order, and second-order quantitative interaction model had the highest explanation for the average tree carbon storage, and R2 was 0.919 2. Compared with the main effect model without interaction terms, R2 was increased by 0.088 5. (4) For the average tree carbon storage, geographic factors (longitude and latitude), topographic factors (altitude, landform, slope aspect, slope position and slope degree), litter layer thickness, climate factors (annual average temperature, extreme minimum temperature, humidity index, frost-free days), soil factor (soil thickness) and stand factors (age and dominant tree species) had significant effects. For 20 years of carbon density by different classifications, geographic factor (latitude), topographic factors (landform, slope degree and slope aspect), climate factors (extreme maximum temperature, hottest monthly average temperature and humidity index), soil factors (soil type and gravel content) and stand factor (dominant tree species) had significant effects, while factors such as altitude and humus thickness existed in the interaction terms, and their main effects were not significant.
        Conclusion  The carbon density of different level thresholds of the natural secondary forests is different in varied time periods. With the increase of age, the thresholds between levels continue to increase, but the relative magnitudes in carbon storage between different levels are decreasing. The introduction of interaction could help improve the interpretation degree of forest carbon sequestration. Geographical factors such as latitude, topographical factors such as slope degree and slope aspect, climate factors such as humidity index, and stand factors such as dominant tree species are the key factors affecting the carbon sequestration capacity of the natural secondary forests.

       

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