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预测美国本土森林中倒下的木质材料碳储量。

Predicting downed woody material carbon stocks in forests of the conterminous United States.

机构信息

USDA Forest Service, Northern Research Station, 271 Mast Road, Durham, NH 03824, USA.

USDA Forest Service, Northern Research Station, 1992 Folwell Avenue, St. Paul, MN 55108, USA.

出版信息

Sci Total Environ. 2022 Jan 10;803:150061. doi: 10.1016/j.scitotenv.2021.150061. Epub 2021 Sep 3.

Abstract

Downed woody material (DWM) is a unique part of the forest carbon cycle serving as a pool between living biomass and subsequent atmospheric emission or transference to other forest pools. Thus, DWM is an individually defined pool in national greenhouse gas inventories. The diversity of DWM carbon drivers (e.g., decay, tree mortality, or wildfire) and associated high spatial variability make this a difficult-to-predict component of forest ecosystems. Using the now fully established nationwide inventory of DWM across the United States (US), we developed models, which substantially improved predictions of stand-level DWM carbon density relative to the current national-reporting model ('previous' model, here). The previous model was developed from published DWM carbon densities prior to the NFI DWM inventory. Those predictions were tested using NFI DWM carbon densities resulting in a poor fit to the data (coefficient of determination, or R = 0.03). We present new random forest (RF) and stochastic gradient boosted (SGB) regression models to prediction DWM carbon density on all NFI plots and spatially on all forest land pixels. We evaluated various biotic and abiotic regression predictors, and the most important were standing dead trees, long-term annual precipitation, and long-term maximum summer temperature. A RF model scored best for expanding predictions to NFI plots (R = 0.31), while an SGB model was identified for DWM carbon predictions based on purely spatial data (i.e., NFI-plot-independent, with R = 0.23). The new RF model predicts conterminous US DWM carbon stocks to be 15% lower than the previous model and 2% higher than NFI data expanded according to inventory design-based inference. The new NFI data-driven models not only improve the predictions of DWM carbon density on all plots, they also provide flexibility in extending these predictions beyond the NFI to make spatially explicit and spatially continuous estimates of DWM carbon on all forest land in the US.

摘要

倒伏木材料(DWM)是森林碳循环的独特组成部分,是活生物质与随后的大气排放或转移到其他森林碳库之间的一个碳库。因此,DWM 是国家温室气体清单中单独定义的一个碳库。DWM 碳驱动因素(如腐烂、树木死亡或野火)的多样性以及相关的高空间变异性,使得这成为森林生态系统中难以预测的组成部分。利用美国全国范围内现有的 DWM 清单,我们开发了模型,这些模型大大提高了林分水平 DWM 碳密度的预测精度,相对于当前的国家报告模型(“先前”模型,以下简称“先前”模型)。先前的模型是根据 NFI DWM 清单之前发表的 DWM 碳密度开发的。这些预测结果使用 NFI DWM 碳密度进行了测试,结果与数据拟合较差(决定系数,或 R=0.03)。我们提出了新的随机森林(RF)和随机梯度提升(SGB)回归模型,以预测所有 NFI 样地和所有森林土地像素的 DWM 碳密度。我们评估了各种生物和非生物回归预测因子,最重要的是立枯木、长期年降水量和长期最高夏季温度。RF 模型在扩展到 NFI 样地的预测方面得分最高(R=0.31),而 SGB 模型则基于纯空间数据(即与 NFI 样地无关,R=0.23)确定了 DWM 碳预测模型。新的 RF 模型预测美国大陆 DWM 碳储量比先前模型低 15%,比根据清单设计推断扩展的 NFI 数据高 2%。新的 NFI 数据驱动模型不仅提高了所有样地 DWM 碳密度的预测精度,而且还提供了在 NFI 之外扩展这些预测的灵活性,以便对美国所有森林土地的 DWM 碳进行空间明确和空间连续的估计。

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