Comment on essd-2024-84

作者：Qing Ying, Benjamin Poulter, Jennifer D. Watts, Kyle A. Arndt, Anna‐Maria Virkkala, Lori Bruhwiler, Youmi Oh, Brendan M. Rogers, Susan M. Natali, Hilary Sullivan, Luke D. Schiferl, Clayton D. Elder, Olli Peltola, Annett Bartsch, Amanda Armstrong, Ankur R. Desai, Euskirchen, Eugénie, Göckede, Mathias, Bernhard Lehner, Mats B. Nilsson, Matthias Peichl, Oliver Sonnentag, Eeva‐Stiina Tuittila, Torsten Sachs, Aram Kalhori, Masahito Ueyama, Zhen Zhang · 年份：2024 · DOI：10.5194/essd-2024-84-rc2 · 研究领域：Radiomics and Machine Learning in Medical Imaging

Abstract. Wetlands are the largest natural source of methane (CH 4 ) emissions globally. Northern wetlands (>45° N), accounting for 42 % of global wetland area, are increasingly vulnerable to carbon loss, especially as CH 4 emissions may accelerate under intensified high-latitude warming. However, the magnitude and spatial patterns of high-latitude CH 4 emissions remain relatively uncertain. Here we present estimates of daily CH 4 fluxes obtained using a new machine learning-based wetland CH 4 upscaling framework (WetCH 4 ) that applies the most complete database of eddy covariance (EC) observations available to date, and satellite remote sensing informed observations of environmental conditions at 10-km resolution. The most important predictor variables included near-surface soil temperatures (top 40 cm), vegetation reflectance, and soil moisture. Our results, modeled from 138 site-years across 26 sites, had relatively strong predictive skill with a mean R 2 of 0.46 and 0.62 and a mean absolute error (MAE) of 23 nmol m -2 s -1 and 21 nmol m -2 s -1 for daily and monthly fluxes, respectively. Based on the model results, we estimated an annual average of 20.8 ±2.1 Tg CH 4 yr -1 for the northern wetland region (2016–2022) and total budgets ranged from 13.7–44.1 Tg CH 4 yr -1 , depending on wetland map extents. Although 86 % of the estimated CH 4 budget occurred during the May–October period, a considerable amount (1.4 ±0.2 Tg...