Digital mapping of peat thickness and carbon stock of global peatlands

IF 5.4 1区 农林科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Marliana Tri Widyastuti , Budiman Minasny , José Padarian , Federico Maggi , Matt Aitkenhead , Amélie Beucher , John Connolly , Dian Fiantis , Darren Kidd , Yuxin Ma , Fraser Macfarlane , Ciaran Robb , Rudiyanto , Budi I. Setiawan , Muh Taufik
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Abstract

Peatlands, occupying merely 5% of the Earth’s land surface, are an important carbon sink, storing up to double the carbon of the world’s forests. The quantification of global peatlands carbon stock and their spatial distribution, however, poses a significant challenge due to their heterogeneous nature and the complex hydroecological processes that govern their formation. Using the Global Peatland Map (GPM 2.0), this study employed a digital soil mapping approach to predict peat thickness, and multilayer bulk density (BD) and carbon content (CC) globally. We applied the Quantile Random Forest (QRF) algorithm, informed by land surface data (soil, climate, organisms, and topography), to develop regional models for peat thickness and global models for BD and CC. Peat thickness models, based on approximately 27,000 data points, demonstrated good predictive performance, with the highest accuracy observed in African peatlands (validation R2 = 0.61). In contrast, BD (∼19,000 points) and CC (∼9,000 points) models showed more variable performance across different soil layers (average R2 = 0.45 and R2 = 0.22, respectively). Feature importance analysis indicated that elevation and climate were key predictors, particularly in Latin America and South–Southeast Asia. Applying the models to 1 km resolution covariates across the world, our predicted peat thickness map aligned well with existing high-resolution regional maps. By incorporating error propagation rules, we estimated the global peatlands carbon stock to be 942 ± 312 Pg C over an area of 6.75 million km2. Our results, including detailed maps, are available to facilitate further global peatland analyses and modelling endeavours.
全球泥炭地泥炭厚度和碳储量的数字制图
泥炭地仅占地球陆地面积的5%,是一个重要的碳汇,储存的碳是世界森林的两倍。然而,由于泥炭地碳储量的异质性和控制其形成的复杂水文生态过程,对全球泥炭地碳储量及其空间分布的量化提出了重大挑战。利用全球泥炭地地图(GPM 2.0),采用数字土壤制图方法预测全球泥炭厚度、多层容重(BD)和碳含量(CC)。我们应用分位数随机森林(QRF)算法,根据陆地表面数据(土壤、气候、生物和地形)建立了泥炭厚度的区域模型和BD和CC的全球模型。基于大约27,000个数据点的泥炭厚度模型显示出良好的预测性能,在非洲泥炭地观察到最高的精度(验证R2 = 0.61)。相比之下,BD(~ 19,000点)和CC(~ 9,000点)模型在不同土层上表现出更多的变化(平均R2 = 0.45和R2 = 0.22)。特征重要性分析表明,海拔和气候是主要的预测因子,特别是在拉丁美洲和东南亚南部。将模型应用于全球1公里分辨率的协变量,我们预测的泥炭厚度图与现有的高分辨率区域图很好地吻合。通过结合误差传播规则,我们估计全球泥炭地碳储量为942±312 Pg C,面积为675万平方公里。我们的结果,包括详细的地图,可用于促进进一步的全球泥炭地分析和建模工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Catena
Catena 环境科学-地球科学综合
CiteScore
10.50
自引率
9.70%
发文量
816
审稿时长
54 days
期刊介绍: Catena publishes papers describing original field and laboratory investigations and reviews on geoecology and landscape evolution with emphasis on interdisciplinary aspects of soil science, hydrology and geomorphology. It aims to disseminate new knowledge and foster better understanding of the physical environment, of evolutionary sequences that have resulted in past and current landscapes, and of the natural processes that are likely to determine the fate of our terrestrial environment. Papers within any one of the above topics are welcome provided they are of sufficiently wide interest and relevance.
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